Abstract

The Internet of Things (IoT) leads to the next phase of human interaction with technology. With the help of the IoT, physical objects can be given the ability to generate, receive, and seamlessly trade data with one another. The IoT includes a wide variety of applications, each of which focuses on automating a specific task and works to give inanimate objects the ability to act independently of human intervention. The currently available and upcoming IoT applications hold a great deal of promise for enhancing the level of convenience, productivity, and automation enjoyed by users. High levels of security, privacy, authentication, and the ability to recover from attacks are required for the implementation of such a world in a manner that is constantly expanding. In this light, it is necessary to make the necessary adjustments to the architecture of IoT applications to accomplish end-to-end security in IoT environments. In this article, a comprehensive review of the security-related challenges and potential sources of danger posed by IoT applications is provided. Following a discussion of security concerns, a variety of new and established technologies that are focused on achieving a high degree of trust in the applications of the IoT are covered. Machine learning, fog computing, edge computing, and blockchain are just a few of the technologies that help the IoT provide greater security.

Introduction

The Internet of Things (IoT) is one of the disruptive technologies of the 21st century that has revolutionized every business dimension across the globe. The Internet has become an essential component in daily activities such as e-commerce, e-learning, e-conferences, etc. According to [1], the number of IoT devices deployed may exceed 21.3 billion by 2022. Intelligent interfaces connect IoT devices in order to collaborate, gather, communicate, and store data. Deploying IoT devices enhances business productivity by optimizing operational procedures and maximizing the utilization of available resources. IoT brings innovation in the way things are monitored and managed remotely with the help of real-time data acquired from the sensors. With the unrestrained volumes of data generated by billions of connected devices, storage, management, and security are arduous. Security aspects such as authentication, authorization, privacy, confidentiality, availability, and integrity form the basis for information exchange in a trusted environment. Authentication is the process of identifying the genuineness of an entity. The authorization process allows only authenticated users to access the resources. The protection of privacy prevents hackers from accessing the user’s private information. Data confidentiality is an outcome of authentication and authorization. It prevents sensitive data from being reclaimed and exploited. A device’s availability confirms that IoT devices and services are resilient to attacks and are accessible all the time. Data integrity is the process of providing a tamper-proof platform for information communication, exchange, and storage.

Conclusion and future directions

In this paper, the methods that are currently available in the literature are discussed, and this paper also presents the methods that are currently available. The subsequent step is an in-depth discussion of the numerous security risks and the importance of threat modeling. The subsequent topic is a discussion of the various IoT applications’ necessary security precautions. It reviews the prior research on the numerous security issues and the solutions put forth by various researchers. The holes that were found while conducting this literature review served as inspiration for the current research work.

7.1. Research challenges
The billions of IoT devices deployed across the globe collect personal and sensitive data and exchange it with other networks using intelligent interfaces. Providing device authentication, authorization, data privacy, and security in such an un trusted environment is a challenge. Centralized trusted infrastructure cannot scale to the dynamic and ever intensifying traffic and, thus, leading to bottlenecks in the network. Distributed authentication can scale to the increasing demands of the IoT networks, but requires distributed trusted systems. The majority of the work in the IoT security domain focuses on embracing the security mechanisms of WSNs and conventional Internet-based applications. However, these mechanisms are far from being implemented in real-time scenarios due to the specific challenges of IoT, such as

Abstract

The advent of the Internet of Things (IoT) has accelerated the pace of economic development across all sectors. However, it has also brought significant challenges to traditional human resource management, revealing an increasing number of problems and making it unable to meet the needs of contemporary enterprise management. The IoT has brought numerous conveniences to human society, but it has also led to security issues in communication networks. To ensure the security of these networks, it is necessary to integrate data-driven technologies to address this issue. In response to the current state of human resource management, this paper proposes the application of IoT technology in enterprise human resource management and combines it with radial basis function neural networks to construct a model for predicting enterprise human resource needs. The model was also experimentally analyzed. The results show that under this algorithm, the average prediction accuracy for the number of employees over five years is 90.2 %, and the average prediction accuracy for sales revenue is 93.9 %. These data indicate that the prediction accuracy of the model under this study’s algorithm has significantly improved. This paper also conducted evaluation experiments on a wireless communication network security risk prediction model. The average prediction accuracy of four tests is 91.21 %, indicating that the model has high prediction accuracy. By introducing data-driven technology and IoT applications, this study provides new solutions for human resource management and communication network security, promoting technological innovation in the fields of traditional human resource management and information security management. The research not only improves the accuracy of the prediction models but also provides strong support for decision-making and risk management in related fields, demonstrating the great potential of big data and artificial intelligence technology in the future of enterprise management and security.

Introduction

At this stage, there are many problems in the traditional HRM, such as the complicated work of the HRM department, the weak teamwork of the enterprise, and the imperfect employee incentive mechanism, which restrict the overall development of the enterprise. In the new era, the traditional HRM simply cannot meet the new needs of enterprise management, so it needs to change this status quo in line with the development of the times. The arrival of the IoT era has connected the world as a whole, which not only provides many conveniences for human society, but also promotes the economic development of all walks of life. Therefore, this paper proposed to apply IoT technology to enterprise HRM, so as to promote the overall development of enterprise HRM.

This research leverages IoT technology and radial basis function neural networks to significantly enhance the predictive modeling of enterprise human resource needs, outperforming traditional algorithms in accuracy and efficiency. Our model not only predicts employee numbers with an impressive 90.2 % accuracy (compared to the traditional 83.9 %) but also excels in forecasting sales revenue, achieving a remarkable 93.9 % accuracy against the conventional 85.2 %. These advancements highlight our model’s capability to provide more reliable and actionable insights for human resource planning and sales forecasting. Further extending our research’s applicability, we delved into the realm of wireless communication network security, achieving prediction accuracies ranging from 90.29 % to 92.85 % in various tests. This high level of precision in security risk prediction underscores the potential of IoT and data-driven approaches in addressing complex challenges in both human resources and information security management. The comprehensive examination and validation of our predictive models across different domains underscore their significant contribution to enhancing strategic decision-making and risk management in an increasingly digital and interconnected business environment.

Conclusions

In the era of the IoT, there are many problems in the traditional enterprise HRM, which is difficult to meet the new needs of enterprise strategic development, and needs to follow the pace of the times to make changes. The IoT not only brings a lot of convenience to human beings, but also brings some hidden dangers to the security of communication networks. It also needs to combine data driven technology to improve the security of communication networks. In order to promote the development of enterprise HRM, this paper proposed to apply the IoT to enterprise HRM, and combined radial basis function neural network to build a prediction model of enterprise human resource demand. Finally, the model was tested. Under this algorithm, the prediction accuracy of the enterprise human resource demand prediction model for sales revenue and the number of employees was very high, and the prediction accuracy had been significantly improved. This paper also evaluated the wireless communication network security risk prediction model, and the experimental results showed that the prediction accuracy of this model was high.

Abstract

The sixth generation (6G) of cellular transmission is extremely adaptable. 6G will equip everyone with pervasive wireless connectivity and meet the needs of a fully connected world. It is anticipated that revolutionary ideas will increase in support for a fast-expanding variety of smart devices and applications. The sophisticated characteristics of 5G portable cellular network infrastructures produce novel threats and requirements. Comparing 6G cellular network technologies to 5G cellular networks, this paper discusses various security and privacy issues in 6G cellular networks based numerous security services. Proposes AA approach to evaluate the effectiveness of the methodology. Propose a new 6G wireless security architecture based on the analysis of secret key authentication and flexible position based identification, which serves as the foundation for an examination of identity management and flexible authentication. Demonstrates the benefits of the proposed architecture, analyzes BER against SINR and Measure Throughput Against various SINR values. Finally, Limitations and future recommendation for 6G cellular network security is presented.

Introduction

Over the past few years, wireless networks technologies have made significant technological advancement. The revolutionary changes brought about by the most recent 5G innovations have benefited a wide range of stakeholders, including commercial solution suppliers, university research organizations, standards bodies, and mobile-users [1,2]. In contrast, networks comprise a large number of Base Stations (BS) to enable high data transmission rates and adequate network capacity due to the low penetrability of high-frequency signals used in 5G [3,4]. The foundation of mobile network service continuity is multi-hop network technology, which allows users to seamlessly switch between source to destination that are outfitted with various access methods. The complexity of device changeover in 5G networks grows as a result of the diversity of service networks, increasing transmission times and affecting user experiences. As opposed to 5G networks, high-density access networks have a high transmission rate among tiny nodes. Existing wireless networks have been struggling to handle an increase in traffic due to the constant growth of data and devices in cellular networks [5,6]. In response, the 6G of cellular communication technologies has arisen to satisfy these high expectations. With many different communication networks, including Long Term Evolution, the Internet of Things, Mobile multi-hop Networks, wireless personal area networks and several versions of Wi-Fi, 6G is incredibly adaptable [7,8].

The 6G communication network is a network with several additional capabilities in addition to being an upgrade of the existing 5G network [9,10]. Research and development for 6G aims to provide a variety of advanced qualities, including greater bandwidth than existing 5G networks, higher mobile internet connection facilitating communication between devices among users as well as extensive technological communications [11,12]. Planned 6G networks also seeks to reduce energy consumption, latency and enhance security, for better wireless network deployment [13,14]. Additionally, a rise in connection density will lead to demands for higher energy efficiency, which 5G is not intended to provide. As a result, the research community has focused on solving the aforementioned key problems [15,16]. Accordingly, we assert that current work in the fields of terahertz band communications, intelligent surfaces and environments, and network automation. The general architecture of 6G wireless systems is shown in Fig. 1.

Conclusion

Cellular networks have been successful because of their emphasis on secure transmission. When everyone’s access to the internet is ensured in 6G, the networks will grow to enormous sizes. We have presented a thorough analysis of current advancements in 6G wireless security in this paper. We proposed a 6G wireless security architecture based on existing research. From the suggested security framework, an analysis of position based identification and adaptable authentication based on different security architectures has been provided. Demonstrated the benefits of the proposed architecture, Analyzed the performance of BER at the User Interface is 94 % improved than the CoMP, GKM and OSPR. Furthermore, Enhanced Throughput 96 % achieved for AA Approach due to integration of data plane and control plane in the proposed architecture. The efficacy of this class of solution is verified by using Riverbed Modeler 17.5 simulation tool.

Abstract

Distributed denial-of-service (DDoS) attacks pose a significant threat to computer networks and systems by disrupting services through the saturation of targeted systems with traffic from multiple sources. Real-time detection of these attacks has become a critical cybersecurity task. However, current DDoS attack detection methods suffer from high false positive rates and limited ability to capture the complex patterns of attack traffic. This research proposes an enhanced approach for detecting DDoS attacks using a hybrid feature selection technique in combination with an ensemble-based classifiers. The ensemble-based approach aggregates many decision trees to increase classification accuracy and reduce overfitting and model robustness. The feature selection technique uses correlation analysis, mutual information, and principal component analysis to identify the most useful characteristics for attack detection. The ensemble-based Random Forest classifier from the various ensemble-based approaches with the specified relevant features produces the best detection rates. Many datasets related to identifying DDoS attacks are used to evaluate the proposed model, and experimental findings demonstrate that it surpasses existing techniques in terms of accuracy, recall, precision, f1-score, and false positive rate, with other evaluation metrics. The proposed approach achieves almost 100 % accuracy, 100 % true positive rate, and 0 % error rate making it a promising solution for DDoS attack detection.

Introduction

A distributed denial of service (DDoS) attack uses a large number of compromised devices, sometimes those that are part of a botnet, to overload a targeted system or network with traffic and render it inaccessible to authorized users [1,2]. The goal of a DDoS attack is to disrupt the normal functioning of the target system or network, denying access to its intended users [3,4]. In this DDoS attack, the attacking devices may be compromised computers, routers, or IoT devices that have been infected with malware or taken over by an attacker. These devices are then directed to send a large volume of traffic to the target system or network, making it unable to respond to legitimate requests. DDoS assaults can originate from any location in the world, and since they are widespread, it may be difficult to effectively prevent or stop them. They are frequently employed by hackers or other criminals to demand money or to obstruct the work of a company, government, or organization. DDoS attacks can cause significant harm, including financial losses, reputational harm, and even legal consequences [5,6].

DDoS attacks raise significant ethical and legal concerns due to their potential to harm sensitive data and jeopardize user information. These attacks are on the rise in terms of both frequency and sophistication, which makes their identification and mitigation increasingly challenging [7]. Attackers employ a variety of techniques and technologies, and the impact of DDoS attacks extends beyond the targeted organization. For instance, an attack on a critical infrastructure provider can have a far-reaching impact, affecting other organizations, governments, and individuals. Consequently, addressing the DDoS attack problem is not only essential for individual enterprises but also for the broader community and society at large. Thus, there is a pressing need to develop effective methods and tools to detect and minimize DDoS attacks [8,9]. As DDoS attacks continue to grow in complexity, they pose challenges for mitigation. Countermeasures are difficult to implement because these attacks can target multiple network levels and originate from diverse sources [10]. Moreover, distinguishing genuine traffic from attack traffic remains a challenge. To effectively reduce the impact of DDoS attacks, innovative and collaborative approaches are required to address these evolving challenges [11,12].

Conclusion and future direction

The advanced approach presented for DDoS attack detection, employing a hybrid feature selection method and an ensemble-based Random Forest machine learning classifier, has showcased exceptional performance compared to existing techniques. The fusion of various feature selection methods and ensemble-based classifiers has yielded remarkable results, with near-perfect accuracy and outstanding performance across a range of evaluation metrics, making it a highly promising solution for real-world DDoS attack detection. The model’s consistent excellence in handling diverse DDoS datasets, including Botnet DDoS, APA-DDoS, DDoS-SDN, and others, underscores its versatility and effectiveness. As DDoS attacks continue to evolve in complexity and frequency, the demand for innovative and efficient approaches to identify and mitigate these threats has become imperative. The proposed model represents a significant leap in this domain and holds the potential to deliver substantial benefits to cybersecurity practitioners and organizations. Its applicability in real-time scenarios and its capability to effectively mitigate DDoS attack impacts ensure the uninterrupted availability and functionality of vital systems and services.

The methodologies and principles outlined in this research can be extended to identify and mitigate other network threats beyond DDoS attacks. Investigating its applicability to various cybersecurity challenges is a promising direction. Developing a comprehensive framework that not only detects attacks but also initiates adaptive responses and countermeasures in real-time can enhance network security. This proactive approach will be pivotal in addressing evolving cyber threats.

Abstract

The popularity of cloud computing, with its incredible scalability and accessibility, has already welcomed a new era of innovation. Consumers who subscribe to a cloud-based service and use the associated pay-as-you-go features have unlimited access to the applications mentioned above and technologies. In addition to lowering prices, this notion also increased the reliability and accessibility of the offerings. One of the most crucial aspects of cloud technology is the on-demand viewing of personal services, which is also one of its most significant advantages. Apps that are cloud-based are available on demand from anywhere in the world at a reduced cost. Although it causes its users pain with safety concerns, cloud computing can thrive because of its fantastic instantaneous services. There are various violations, but they all accomplish something similar, taking the systems offline. Distributed denial of service attacks are among the most harmful forms of online assault. For fast and accurate DDoS (Distributed Denial of Service, distributed denial of service) attack detection. This research introduced the DDOS attack and a method to defend against it, making the system more resistant to such attacks. In this scenario, numerous hosts are used to carrying out a distributed denial of service assault against cloud-based web pages, sending possibly millions or even trillions of packets. It uses an OS like ParrotSec to pave the way for the attack and make it possible. In the last phase, the most effective algorithms, such as Naive Bayes and Random Forest, are used for detection and mitigation. Another major topic was studying the many cyber attacks that can be launched against cloud computing.

Introduction

DDos attack is a distributed type of attack mode in which an attacker controls a large number of attack machines and sends out DoS attack instructions to the machine. In the latest Internet security report, DDoS attacks remain one of the major cybersecurity threats. The inexpensive pricing and “pay-as-you-go” focused accessibility to computational features and amenities on demand make cloud-based services a formidable competitor to the conventional IT solutions available in prior eras. The use of cloud computing is gaining popularity rapidly. Whether entirely or largely governments and companies have moved their IT infrastructures onto the cloud. Cloud-based Infrastructure offers various advantages compared to traditional, on-site conventional infrastructures. The removal of expenses associated with operation and impairment, as well as the accessibility of materials on request, are only a few of the advantages. However, there are many concerns that cloud consumers have, and the research addresses these issues. The majority of these inquiries centre on safeguarding operational concepts and information. Many security-related attacks can be prevented in conventional IT systems that do not use cloud computing. Focused cloud-based crimes are already using their innovations. Many security vulnerabilities in cloud computing are unique compared to their predecessors in non-cloud computing environments because data and business logic are stored on an external cloud server that lacks accessible oversight. The denial-of-service (DoS) assault is one technique that has been in the spotlight recently. Denial-of-service incidents are directed at the server rather than the people it supports. DoS attackers attempt to flood live servers by masquerading genuine users to overload the service’s capacity to handle incoming inquiries [1]. Cloud computing is an Internet-based service that enables users to access configurable computing resource sharing pools (including server, storage, application software, services, networks, etc.) to achieve online access to computing resources on demand. As a mixture of emerging technologies and business models, cloud computing has developed rapidly in recent years due to its advantages of super-large scale, virtualization, high reliability, good scalability and on-demand services. To overcome this issue, multiple inquiries are sent to the server simultaneously. The term “distributed denial of service,” or DDoS, refers to a variation on the classic “denial of service” that uses numerous computers to attack and impair one service at a time simultaneously.

Conclusion

The key goals of this study are to learn how to recognize and prevent attacks involving distributed denial-of-service. The first and most crucial step is determining which ports can be exploited. Nevertheless, this approach is not risk-free because susceptible ports are more likely to be exploited. Given ParrotSec’s track record for stability and performance, we decided it would be the ideal choice for our company’s computer system. Since a DDoS attack involves sending one million separate packets toward the target, starting with an on-the-internet website would be best. The targeted website was taken offline after it became clear that an assault had happened. Machine learning is constructive in this detecting process as well. Using this data, the most popular and accessible tool, “weka,” is being trained. Employing pre-processing techniques and the “discretize” filter to achieve the desired effect. Therefore, the following phase is not only quite intriguing but also rather useful for both forecasting and detecting. We employed both methods and compared the findings on the same platform, and we found that the naive Bayes method provides the most trustworthy conclusions. PCA selected 21 features from the possible 42 features, while LVQ selected only 20 features. The results suggest that LVQ based feature selection in the DT model may be more accurate than other methods in identifying attacks. As mentioned earlier, the model also outperformed the previous models in terms of accuracy, recall, specificity, and f-score. It was shown that the naive Bayes model had significantly better predictive power than the random forest model. There is a chance that a false positive rate warning will be triggered for packet transmissions within a network. Moreover, when compared to the random forest, naive Bayes produces considerably more accurate forecasts. It was demonstrated that the Naive Bayes algorithm outperformed the random forest technique to identify the false and actual rate of transmissions. The result detection is not carried out in real time. Although attacks can be detected, real-time alarm cannot be realized in the environment of high cluster security, so the feasibility of real-time monitoring under Hadoop platform should be studied continuously.

Abstract

Deep learning techniques have shown significant impact in enhancing security across various domains by leveraging artificial neural networks models. When applied to cloud computing security, deep learning offers cost-effective solutions by automating threat detection, reducing manual monitoring, and improving overall security effectiveness. Deep learning models using neural networks play crucial role in tasks like intrusion detection, malware detection, anomaly detection, and log analysis. Integration of deep learning into cloud security requires careful evaluation of existing systems, defining objectives, dataset selection and preparation, model tuning, and eventual modifications for compatibility. Furthermore, implementing deep learning techniques in cloud security entails considering factors such as computational resources, data collection and preparation costs, model development, integration efforts, and ongoing monitoring and maintenance. This paper proposes a feed-forward propagation Artificial Neural Network (ANN) model in cloud security and investigates the key steps for integrating such models into cloud security strategies. Considering that the effectiveness of the ANN model depends on factors such as training data quality, network architecture, and weight adjustment algorithms, the study utilizes a dataset from Kaggle.com for validation and demonstrates steps involved in training and evaluation of the ANN model.

Introduction

Deep learning (DL) techniques have emerged to become effective tools for enhancing security in various domains. These techniques leverage the capabilities of artificial neural networks to learn and identify patterns from vast amounts of data, enabling more robust and efficient security solutions [1][2]. When applied to cloud computing security, deep learning techniques offer several benefits having direct implications in the cost-effectiveness of the products. A great benefit of implementation of DL in security of cloud is seen in automated threat detection. DL algorithms can analyze large volumes of data, such as network traffic logs, system logs, and user behavior, to detect anomalies and potential security threats automatically [2], [3]. This together with other utilizations as a result of DL, reduces the need for manual monitoring and analysis, what leads to faster identification of security incidents, timely response, mitigation, and reduced costs in time and resources. Considering this, through automation of various security tasks, DL minimizes the risk of human error, which can lead to security breaches and associated costs.

Automated systems powered by deep learning can perform tasks consistently and accurately, enhancing overall security effectiveness [4], [5]. DL models learn patterns and relationships in data, allowing for more accurate threat detection and classification. Through detecting patterns and anomalies which could go unnoticed by traditional rulebased systems, DL implementation makes it possible for organizations to optimize resource allocation and minimize unnecessary costs.

Conclusion

The integration of deep learning techniques into cloud computing security offers numerous benefits. By analyzing large volumes of data, deep learning algorithms can detect security threats in real-time and minimize various risks. However, the implementation of deep learning in cloud security requires careful evaluation of costs and resources. Organizations struggle when having to adjust the solution to the computational resources, data acquisition and preparation costs, personnel expertise, and ongoing monitoring and maintenance.

The integration of deep learning techniques in cloud security involves few steps for a successful outcome. These include assessing the strengths and weaknesses of the existing system, identifying specific tasks that deep learning can address, selecting suitable models and algorithms, training the models with labeled data, and ensuring compatibility and integration with existing components. Continuous monitoring and feedback are also crucial stages for maintaining the efficiency and effectiveness of the integrated system.

Abstract

Despite providing unparalleled connectivity and convenience, the exponential growth of the Internet of Things (IoT) ecosystem has triggered significant cybersecurity concerns. These concerns stem from various factors, including the heterogeneity of IoT devices, widespread deployment, and inherent computational limitations. Integrating emerging technologies to address these concerns becomes imperative as the dynamic IoT landscape evolves. Machine Learning (ML), a rapidly advancing technology, has shown considerable promise in addressing IoT security issues. It has significantly influenced and advanced research in cyber threat detection. This survey provides a comprehensive overview of current trends, methodologies, and challenges in applying machine learning for cyber threat detection in IoT environments. Specifically, we further perform a comparative analysis of state-of-the-art ML-based Intrusion Detection Systems (IDSs) in the landscape of IoT security. In addition, we shed light on the pressing unresolved issues and challenges within this dynamic field. We provide a future vision with Generative AI and large language models to enhance IoT security. The discussions present an in-depth understanding of different cyber threat detection methods, enhancing the knowledge base of researchers and practitioners alike. This paper is a valuable resource for those keen to delve into the evolving world of cyber threat detection leveraging ML and IoT security.

Introduction

Brendan O’Brien astutely observed, ”If you think the Internet has changed your life, think again. The Internet of Things is about to change it all over again!” [1]. This is indeed the case, as the Internet of Things (IoT) has heralded unprecedented connectivity. The advancements in sensor technology, wireless communication, and data analytics have spurred an exponential increase in connected devices. This influx of connectivity, brought about by integrating IoT into various industries, cities, and households, promotes unmatched efficiency and convenience. As the backbone of IoT, sensors and actuators acquire and convert data from the physical world into digital signals. These compact devices amass a diverse range of data, thereby enabling real-time monitoring and control of numerous systems and processes.

However, the rapid proliferation and extensive integration of IoT devices into everyday life have ushered in various security challenges. These issues must be robustly addressed to ensure the safety and reliability of this expanding ecosystem. The sheer volume and variety of IoT devices and their often inconsistent security features and protocols engender a fragmented environment teeming with potential attack vectors. IoT devices frequently prioritize low cost and user simplicity over security, making them susceptible to breaches and exploitation. As a result, these devices are at risk of various cyber threats, including data breaches, Distributed Denial-of-Service (DDoS) attacks, and malware infections. Any security breach in these devices could significantly compromise privacy and crucial infrastructure systems, given the sensitive nature of the data they handle. Moreover, IoT devices, potentially serving as entry points, might allow attackers to infiltrate broader networks, amplifying the potential impact of security breaches. Another primary concern is the security of communication routes between IoT devices and networks, as many IoT devices utilize wireless communication protocols susceptible to interception or manipulation. These vulnerabilities can be exacerbated by the resource constraints of specific IoT devices, which prevent them from adopting contemporary encryption and authentication techniques. Furthermore, the long lifespan and widespread deployment of IoT devices compound the difficulty of managing security upgrades and patches, as many devices may not receive regular updates or may be difficult to access for maintenance. This could lead to an increased number of outdated or vulnerable devices, further exacerbating security concerns [2].

Conclusion

IoT devices have become fundamental to everyday life, offering increased connectivity and convenience. However, this accelerated development in IoT devices also brought several security challenges that need to be addressed to guarantee the safety and reliability of these interconnected systems. The survey was organized into five sections detailing the role of machine learning in enhancing IoT security. We have provided an overview of the current trends in ML for cyber threat detection in IoT environments. Furthermore, we survey recent cyber detection methods, define them, highlight the approach, and detail the attack surface utilized along with their evaluations. The utilized ML techniques are also discussed, defined, and compared regarding advantages and drawbacks in the relevant use cases. Moreover, open issues were discussed briefly, concluding that further research and development is required as no current solution can address the issues related to IoT cyber threat detection. A holistic strategy combining diverse techniques and strategies is required as a final recommendation. We present this survey as a reference for the current advancements in the field and point out the direction being taken. Our goal is for the survey to create a more secure and resilient IoT environment by identifying the current issues, understanding their root causes and possible implications, and developing innovative solutions.

Abstract

Employing the Internet of Things (IoT) in healthcare provides many advantages for patient monitoring and analysis of the patient’s health with the help of generated data. The basic role of IoT in healthcare is to ease the patient’s life by giving them a monitor over their medical condition. The use of IoT in medical devices requires a focus on the end-user. Medical devices such as glucose meters are designed to record the data of the patient and their vital signs. The generated information can be used to enhance decision-making for the physician. The collected information about the patient can be at risk due to certain security issues during the transferring of information can compromise the identity and social life of the patient. This review explores the IoT regarding its structural requirement and its role in various fields with special emphasis on healthcare. The security and privacy issues than can hinder the utilization of IoT at its potential and ways to overcome these issues are being addressed.

Role of IoT in medical devices

Medical Devices (MDs) are any instrument, reagent, apparatus, implement, machine or any other in-vitro reagent or calibrator, software which is intended to be used alone or in combination for the diagnosis, mitigation, prevention, treatment or alleviation of diseases in human beings.1 Generally, MDs are classified based on the risk associated with them. As per USFDA MDs are classified into three categories I, II, III where category I has lowest risk and category III has highest risk as shown in Table 12 (see Fig. 4)A report from Fortune Business Insights states that from 2018 to 2025 the Medical Device Industry will experience major investment in Research & Development of new technology from pharmaceutical players, approvals from regulatory authorities will also contribute to increase this number. The medical device global market was USD 42.5 Billion in 2018 and is estimated to reach USD 612.7 Billion by 2025. CAGR is expected at 5.4 % from 2018 to 2025.3

Further Medical Devices have been showing an increasing growth in the previous decade and with promising technology and innovations being made, it will keep growing more. Although there is conventional research and development being done by existing companies. With the entry of giants like Apple and Google, new technological advancements in the form of Artificial Intelligence, Data Analytics and Internet of Things (IoT) are being implemented in this sector.4

Conclusion

The amalgamation of a variety of technology is required to build an IoT. Due to this complexity, the system becomes more vulnerable to security and privacy threats. The generic engineering framework involved in patient monitoring remotely should meet the security requirements with the help of cryptographic algorithms. These algorithms should be device-specific to nurture the design & environment of patient-specific needs. The security and safety issues of a patient which arise from the application of IoT cannot be solved with the help of a single solution. To practice, the best security controls an informed user is also necessary. The management ability of the network and device automation can be of great help to deliver patient well-being and care. Delivering healthcare to patients efficiently and effectively, independent of time and location requires a new paradigm that is opened by IoT. The existing foundations of telemedicine have now been extended and better assessment of normal public health in a cohort sense has shifted the passive involvement of patient data gathering. Improving the outcomes for the patient and reducing the care cost are being balanced against the privacy and confidentiality of data and threats of cyber security. There is a vast source of data collection and potential to transfer clinical data, but there is a challenge to triage this data for clinical use. The cyber security and safety of patient data need to move faster than regulation and implementation of policy. The way forward for IoT needs superior standards every day to use the connected devices.

Abstract

Cloud computing has revolutionized the way businesses and individuals manage and utilize computing resources, providing significant benefits such as scalability, flexibility, and cost-effectiveness. However, the increasing use of cloud computing to store and transmit sensitive data has raised concerns about data security. Ensuring confidentiality and data integrity while enabling effective data transmission is a significant challenge. To overcome this issue, the proposed approach integrates encryption and compression methods to enhance transmission performance in the cloud and prevent unauthorized access to confidential information. This approach applies multiple layers of robust encryption algorithms, followed by LZMA, which aims to compress data size while ensuring data security. The proposed approach is well-suited for real-time implementation and delivers high encryption quality and compression capabilities. Various performance metrics evaluate its performance, including space-saving percentage, processing time, and the NIST randomness test. It provides a substantial improvement in space-saving percentage from 58.63% to 81.8%, ensuring efficiency. The security analysis confirms that ciphertexts generated by this approach pass all NIST tests, generating a 99% confidence level regarding the randomness of the ciphertext. The proposed hybrid approach offers an effective solution for addressing the challenges of securing sensitive data while taking advantage of the benefits of cloud computing.

introduction

Cloud computing has become an increasingly popular way in the networking and computer science field. It has given small and medium-scale businesses with a cost-saving opportunity to avoid the expense of purchasing hardware resources. It gives us an equal opportunity to shine as it enables users with access to a powerful computing resource without having to invest in expensive hardware or software. Cloud computing operates on the principle of virtualization, wherein a single large machine is utilized by multiple clients with a view that they have their dedicated resources. Due to the significant benefits of managing resources in unlimited storage in the most inexpensive way, business continuity, and scalability, cloud computing has had rapid growth in the IT industry, therefore, this technology has emerged as one of the most powerful innovations that has attracted the interest of technologists globally (Sajay, 2019). Cloud computing services can be classified into three primary categories: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). SaaS provides software packages, system software, and application-related server storage networks to end users such as Gmail. PaaS is used by application developers to create applications that are hosted in the cloud such as Google App Engine. IaaS deals with hardware services, virtual machines, and network architecture infrastructure such as Microsoft Azure (Mahesh et al., 2023). The deployment models include private cloud, public cloud, community cloud and hybrid cloud. Public clouds are available to everyone while private clouds are used by organizations. Community clouds are shared by multiple organizations and hybrid clouds combine both private and public clouds (El-Booz and Attiya, 2017, Thabit et al., 2021).

Conclusion and futurework

This research utilized a multi-layered security approach with compression to secure and compress text data streams while preserving the original data sequence without any changes. This research focused on developing a secure and efficient way of storing and transferring data streams in the cloud computing environment by incorporating existing encryption and data compression techniques, including symmetric key encryption techniques Rc4, AES, and the substitution cipher Vigenère, while AES is a more advanced encryption standard. Combining these techniques provided a more robust and comprehensive solution for data security while maintaining low storage of text data through LZMA compression. The proposed approach consists of seven stages in the encoding phase (sender Side) Initial Encryption, Decomposition, Substitution, Data size treatment, Compression, Encryption2, and Uploading Encrypted-compressed data in the cloud and the inverse on the (receiver side). Experimental results proved that the proposed approach achieved faster processing time compared to performing the encryption and compression techniques as separate steps. Security analysis demonstrated that the proposed approach for generating ciphertexts passed all NIST tests with a high level of confidence (99 %) in terms of ciphertext randomness. Additionally, compression efficiency analysis demonstrates that the proposed approach produced a significantly higher range of space-saving percentages (58.63 % to 81.8 %) compared to standard techniques while also providing adequate security.

Abstract

Software vulnerabilities resulting from coding weaknesses and poor development practices are common. Attackers can exploit these vulnerabilities and impact the security and privacy of end-users. Most end-user software is distributed as program binaries. Therefore, to increase trust in third-party software, researchers have built techniques and tools to detect and resolve different classes of coding weaknesses in binary software. Our work is motivated by the need to survey the state-of-the-art and understand the capabilities and challenges faced by binary-level techniques that were built to detect the most important coding weaknesses in software binaries. Therefore, in this paper, we first show the most critical coding weaknesses for compiled programming languages. We then survey, explore, and compare the static techniques that were developed to detect each such coding weakness in software binaries. Our other goal in this work is to discover and report the state of published open-source implementations of static binary-level security techniques. For the open-source frameworks that work as documented, we independently evaluate their effectiveness in detecting code vulnerabilities on a suite of program binaries. To our knowledge, this is the first work that surveys and independently evaluates the performance of state-of-the-art binary-level techniques to detect weaknesses in binary software.

Introduction

Technology and software have become integral to our daily lives. More software is now present in more systems, including many embedded devices, like refrigerators and microwave ovens, to cars and planes. Additionally, new software features continue to be added as the hardware, including the processor, memory, and storage, becomes faster, larger, and/or more capable. Thus, software programs also continue to grow in size and perhaps, complexity.

Given this growth in the amount of software in use, it is no surprise that the number of reported code vulnerabilities has been increasing in number and severity for many years [1]. At the same time, software vulnerabilities have been found to cause many disastrous real-world attacks [2], [3].

Software vulnerabilities are caused by weaknesses or flaws in the program code. These weaknesses may then be exploited to compromise the security or integrity of the system. Code in any language can be insecure when it is not developed with due care. However, some programming languages are designed with features that make them immune or more resistant to certain types of weaknesses. Such safer languages typically provide built-in mechanisms for memory management, input validation, type safety, and other security-related features. Languages like Rust and Go belong to this category of safe programming languages.

Alternatively, unsafe programming languages, like C and C++, are low-level languages with poor built-in memory, type, and thread safety. Code bugs and missing safety oversight for vulnerable code constructs are widespread in programs written using these languages [4]. In spite of these safety concerns and even though memory-safe language alternatives are available, C/C++ remains popular due to the large amount of existing legacy code, and low-level features of these languages that are desired by many performance and memory critical, embedded, and real-time systems. Consequently, C and C++ separately and consistently rank among the top five most popular programming languages according to the TIOBE index 1.

Conclusions

Our goal in this work was to comprehensively review and compare past research in static analysis based approaches to detect the most important CWE categories for program binaries. Another major goal was to evaluate the accuracy of open-source tools built to detect each studied program’s weaknesses. We made many significant, interesting, and novel discoveries and observations in this work. First, we found that we currently lack tools and techniques to accurately detect many important classes of errors in binary software. Second, we found that much research is not available in the open-source domain, and even the tools that exist are often not maintained and lack critical support. Third, many research works only evaluate their techniques on small benchmarks, and their results may not adequately represent performance in real-world applications. Fourth, many CWE detection techniques suffer from a high incidence of false positives and false negatives, underscoring the need for refinement and enhancement of existing techniques and tools. Thus, this work distinguishes itself as the first survey of binary-level CWE detection techniques, and the first independent assessment of binary-level open-source tools for identifying software weaknesses, offering valuable insights and setting the stage for further advancements in this critical field.

Abstract

     Today, industry 4.0 is becoming a major target for cybercriminals due to its hyper-connectivity. Fortunately, there are several advanced means of securing industrial systems such as Intrusion Detection Systems (IDS). However, one of the main limitations of industrial IDS is the high rate of false positives and how to distinguish a real attack from an industrial failure. This paper deals precisely with the two latter points and proposes a way to reduce the rate of false positives and to distinguish attacks from industrial failures. The proposed approach combines two kinds of IDS using Neural Network (NN) through a Decision Making System (DMS). It was tested on a real industrial environment. The performance results are promising with a high percentage of accuracy and a low false positive rate.

Introduction

     Nowadays, Industrial Control Systems (ICS) exist in many different industrial sectors such as meatpacking, chemistry, construction, automotive, electronics industry. But also in vital industrial sectors such as energy, health, military and food sectors. Therefore, the suspension or the stopping of these systems could be costly for industrialists and cause consequent damage. Today, securing such equipment becomes more than necessary. Over the past decade, industry has become the center of attackers’ focus and has been the victim of several attacks starting with Stuxnet, Black Energy, WannaCry. This wave of attacks has been succeeded by several ransomware attacks in 2020 during the coronavirus pandemic, especially with the increase in the number of remote workers and a lack of security in this new working model [1]. Kaspersky’s ICS CERT researchers forecast a list of attacks likely to target industries in 2023 [2]. Among these attacks are phishing pages and emails, Torjans, N-day vulnerabilities, attacks on cloud services, exploiting vulnerabilities in legitimate software, the spread of malware via removable media …

This cyber-criminality phenomenon is favored with the emergence of the industry 4.0. This 4th industrial revolution is characterized by the convergence of the worlds of Information Technology (IT) and Operation Technology (OT), the huge amount of generated data, the use of Cloud as new storage means. All these reasons increase the risk of cyber attacks in industry.

Fortunately, there are several solutions to secure the industry and its equipment. Among these mechanisms, we mention firewalls, anti-virus, auditing processes and IDS. Each of these securing mechanisms has a specific role such as detecting and removing malware, preventing unauthorized access or detecting intrusions by IDS. These latter give visibility to the system’s activities, which allows a timely detection and response to any suspicious events [3], [4].

Conclusions

     BIANO-IDS is a new intrusion detection approach combining two kinds of IDS: anomaly-based IDS and specification-based IDS. The approach has been tested on a real environment and the detection accuracy rate is high in addition to the different performance metrics. However, to further improve performance metrics, we intend to explore other traces such as system logs and apply reduction or selection features methods in the future to improve the computation and training time of neural networks.

Abstract

     Technology is pivotal in the rapid growth of services and intensifying the quality of life. Recent technology, like the Internet of Things (IoT), demonstrates an impressive performance in fast-forward development. Intrusion Detection System (IDS) is used as a lifeline to prevent attacks by classifying the activities as normal and suspicious. In this paper, we propose a two-phase IDS for IoT. In the first phase, we categorize data into four sections according to the data types (i.e., nominal, integer, binary, and float). We then classify them using different versions of the Naive Bayes classifier. After that, we use majority voting to choose the final result of the classification. In the second phase, we pass those data which behave normally or are benign in the first phase and classify them using an unsupervised elliptic envelope. We validated our work using the standard NSL-KDD, UNSW_NB15, and CIC-IDS2017 datasets. We found the proposed method more efficient than existing IDS techniques and achieved reasonable accuracy in the first phase. Furthermore, the benign data is sent to the second phase of the analysis. After the second phase, we achieved a 97% accuracy in the NSL-KDD dataset, 86.9% in the UNSW_NB15 dataset, and 98.59% accuracy in the CIC-IDS2017 dataset.

Introduction

     In the evolutionary era, the Internet has always been performing a most significant role. Globally, the total estimate of Internet users is projected to increase from 3.9 billion in 2018 to 5.3 billion by 2023, as stated by Cisco Annual Internet Report [1]. Furthermore, the Internet of Things (IoT) is becoming increasingly widespread. IoT integrates many heterogeneous objects (such as in a smart home: intelligent bulbs, refrigerators, fans, air conditioners, automated doors, and TVs.) with various connecting technologies such as Bluetooth Low Energy (BLE), WiFi, and ZigBee. There are also other domains and applications in which the IoT can play an important role and enhance our lives quality. These applications include smart transportation, industrial automation, agriculture, and healthcare [2].

     The IoT model [3] has been emerging towards formulating a cyber–physical environment where everything can be found, operated, investigated, and modernized. Because of being connected, the chances of attacks on the network increase. Many attacks and malicious incidents can affect different layers of the IoT architecture, creating security concerns. Makhdoom et al. [4] discussed the commonly known attacks on different layers, depending on the anatomy of the malware, and IoT-enabled cyber-attacks are also illustrated in a survey [5]. Similarly, Zarpel et al. [6] elaborated on intrusion detection systems in IoT. They have classified IDS based on placement strategies, detection methods, security threats, and validation strategies. Zargar et al. [7] explained in detail about Distributed Denial of Service (DDoS) attacks and also classified the countermeasures.

Conclusion

     This paper presents a machine learning-based two-phase IDS. Firstly, we categorize data into four sections according to the data types (e.g., nominal, integer, binary, and float). Then classify them using different versions of the Naive Bayes classifier. After that, with the help of majority voting, we choose the final result of the classification. In the second phase, we pass those data which behave like normal in the first stage, and these data are classified using an unsupervised elliptic envelope. It draws an imaginary envelope and assigns value 1, which lies inside the Envelope, and −1 outside the Envelope. Our proposed model is also performing very well in the imbalanced distribution of the data by providing the weight initialization to each class. Finally, we got an overall 97% accuracy with a meager false positive rate. The drawback of this model is that it does not work pretty in multiclass classification.

     In future work, we will improve the multiclass classification and feature engineering techniques model, expand this procedure in real-time for network traffic analysis, and evaluate performance. We will further attempt to capture network data by deploying IoT devices in the real world. Moreover, the IDS system will detect malicious incidents in real-time and immediately take appropriate action to prevent damage.

Abstract

The use of computers and the internet has spread rapidly over the course of the past few decades. Every day, more and more people are coming to rely heavily on the internet. When it comes to the field of information security, the subject of security is one that is becoming an increasingly important focus. It is vital to design a powerful intrusion detection system in order to prevent computer hackers and other intruders from effectively getting into computer networks or systems. This can be accomplished by: (IDS). The danger and attack detection capabilities of the computer system are built into the intrusion detection system. Abuse has occurred and can be used to identify invasions when there is a deviation between a preset pattern of intrusion and an observed pattern of intrusion. An intrusion detection system (IDS) is a piece of hardware (or software) that is used to generate reports for a Management Station as well as monitor network and/or system activities for unethical behaviour or policy violations. In the current study, an approach known as machine learning is suggested as a possible paradigm for the development of a network intrusion detection system. The results of the experiment show that the strategy that was suggested improves the capability of intrusion detection.

Introduction

Over the past few years, there has been an increase in the usage of computer systems to make the lives of consumers easier and more convenient. When people try to takeadvantage of the amazing capabilities and processing capacity of computer systems, however, security has been one of the most significant problems in the field of computer science. This is because attackers frequently try to break into systems and act maliciously, such as stealing vital information from a corporation, rendering the systems useless, or even destroying the systems. Internal attacks, such as pharming, distributed denial-of-service (DDoS), eavesdropping, and spear-phishing attempts, are often among the most difficult to identify of all well-known attacks. This is due to the fact that firewalls and intrusion detection systems (also known as IDSs) often guard against attacks from the outside. At this time, the majority of systems authenticate users by analysing a login pattern consisting of the user ID and password. As a result of this, we have proposed in this study a security solution that we have dubbed the Internal Intrusion Detection and Protection System (IIDPS) [1]. This solution recognises hostile or malicious behaviour carried out against a system at the System call level. IIDPS uses data mining and forensic profiling techniques in order to mine system call patterns, also known as SC-patterns, which are the longest system call sequences (SC-sequences) that have repeatedly appeared numerous times in a user’s log file for the user. SC-patterns can be used to identify malicious activity. The user’s computer usage history is used to compile the user’s forensic features, which are then defined as a SCpattern that commonly appears in the user’s own submitted SCsequences but is rarely utilised by other users. This information is gleaned from the user’s computer.

Conclusion

As a consequence of this, within this work, we suggest asecurity system that we refer to as the Internal Intrusion Detection and Protection System (IIDPS). This system is capable of identifying hostile behaviour that is aimed towardsa system at the SC level. The IIDPS minessystem call patterns, also known as SC-patterns, which are defined as the longest system call sequences (System Call-sequences) that have repeatedly appeared numerous times in a user’s log file for the user. These are the operations carried out by the user, such as sending a file, updating a file, or viewing a file, and they are validated by an administrator. The user’s computer usage history is used to compile the user’s forensic features, which are then defined as a SC-pattern that commonly appearsin the user’s own submitted SC-sequences but is rarely utilised by other users. This information is gleaned from the user’s computer.