Abstract

     One of the main challenges in the design of wireless ad-hoc and sensor networks is to reduce energy consumption and radio interference and collision, which are strictly and strongly correlated to the transmission range of the nodes. In this article, the transmission ranges of sensors in sparse Wireless Sensor Networks (WSN) is theoretically analyzed and is shown that the transmission ranges of sensors can be significantly reduced in this type of WSNs without losing any connection. Next, a method is presented to calculate reduced transmission ranges. In this method, an iterative transmission range adjustment mechanism is used to select an almost transmission range while no information is needed on the location of nodes or the distance between nodes. This method is very efficient and its communication overhead is low. It can be used as a pre-step of any routing algorithm in order to decrease energy consumption and radio interference in targeted scenarios. The simulation results show the efficiency of the proposed method. We got at least 50% improvement in energy consumption in comparison with full power network, in sparse WSNs.

Introduction

     Wireless Sensor Networks (WSNs) have become increasingly popular in recent years, as they offer a powerful and cost-effective solution for monitoring and collecting data from a variety of environments. A Wireless Sensor Network (WSN) consists of a large number of distributed sensor nodes that cooperatively monitor the physical world [1]. Each node in these networks is typically equipped with a wireless communication device, a small microcontroller, a memory unit and a power source. The nodes in a WSN are typically battery-powered and have limited processing power, memory, and communication bandwidth. Therefore, the design of a WSN requires careful consideration of these constraints, such as the choice of communication protocol, data routing algorithms, and energy management strategies [2].

     The complexity of the WSN domain as well as the presence of many sensor nodes unavoidably introduces a large amount of data in these networks that must be processed, transmitted and received. The sheer amount of data generated by WSNs can pose a significant challenge for the network design and operation. The sensors in a WSN can generate data continuously or periodically, depending on the application requirements. This data must be processed, stored, and transmitted to the base station in a timely and efficient manner. So the complexity of the WSN domain and the presence of many sensor nodes can introduce a large amount of data that must be processed, transmitted, and received [3]. Despite their profound advantages, the utilization of WSNs is often battery-powered and strictly limited due to energy constraints. In fact, most of the energy expenditure of a sensor node occurs during wireless communication, and the remaining energy is consumed during sensing and data processing. The radio transceiver of a node consumes a significant amount of energy during data transmission and reception. Therefore, the energy consumption of a node must be carefully managed to ensure the longevity of the network [4]. Transmission power adjustment for a special transmitter-receiver pair depends on several environmental conditions. The transmission power required to reach the receiver is affected by two main factors including distance and wireless connection quality. Distance affects transmission power. As the distance between the transmitter and receiver increases, the signal strength decreases, resulting in a weaker connection.

Conclusions and future works

     We studied the transmission range of sensors in wireless sensor networks in this study, and proposed a simple yet effective distributed transmission power control algorithm for sparse wireless sensor networks. We showed that this algorithm has advantages in many cases. The main idea of algorithm is simple, but performance of algorithm in targeted scenarios is excellent. We analyzed the performance of this method theoretically and show that using this method is worthwhile in some scenarios, especially in sparse wireless sensor networks with high amount of communication traffic. In these scenarios, our method can be used as a pre-step of any routing algorithm and decrease energy consumption and radio interference. As a future work, we intend to design methods to overcome the collisions problem. Designing methods for resisting the algorithm against the sensor failures is another future work. Furthermore we intend to work on using learning methods like Reinforcement learning for power control. Designing a good energy efficient reward mechanism is one of the main challenges of these methods.

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

The Internet of Things (IoT) connects devices, enabling real-time data acquisition, automation, and collaboration. Wireless sensor networks are one of the important components of the Internet of Things, consisting of many wireless sensor nodes distributed in space. These nodes can perceive environmental information and transmit it to other nodes through wireless communication. In wireless sensor network routing optimization methods, improved ant colony algorithm can be used to find the optimal routing scheme. Ant colony algorithm simulates the behavior of ants in the process of searching for food, and optimizes factors such as transfer probability and pheromone concentration to enable ants to find the shortest path. In wireless sensor networks, node positions can be used as reference nodes and anchor nodes, combined with the objective function of wireless sensor network routing optimization, and improved ant colony algorithm can be used to solve the optimal path, thus obtaining the optimal wireless sensor network routing optimization scheme. Through experimental results, it can be found that the proposed method performs well in terms of energy consumption, transmission delay, number of dead nodes, and network throughput. These optimization results have positive implications for the sustainable development and practical application of the Internet of Things, which can improve the development of the digital economy and enhance the construction of smart cities.

Introduction

At present, wireless sensor networks have been widely applied in various fields such as environmental monitoring, agriculture, and the Internet of Things. In these applications [1,2], wireless communication between nodes in wireless sensor networks plays a crucial role. The Internet of Things has characteristics such as connectivity, intelligence, and real-time. It connects devices through wireless communication technology and endows them with perception, computing, and decision-making capabilities. This enables the Internet of Things to obtain and transmit data in real-time, automate collaboration and data sharing, improve production and operational efficiency, and reduce costs. At the same time, the Internet of Things utilizes big data analysis and predictive maintenance to provide insight into market trends and user needs, helping enterprises make more accurate decisions. In addition, the application of the Internet of Things in fields such as smart homes and health monitoring is improving people’s quality of life and promoting the process of sustainable development. Therefore, the Internet of Things has significant advantages in improving efficiency, reducing costs, enhancing insight, improving quality of life, and promoting sustainable development. The routing optimization of wireless sensor networks has become an important and challenging issue due to the limited energy of sensor nodes, wide and random distribution of nodes, and other characteristics. The goal of wireless sensor network routing optimization is to improve network performance and energy utilization efficiency by selecting appropriate paths, minimizing energy consumption, maintaining low latency, and maximizing network lifespan [3]. It involves techniques and mechanisms such as path selection and transmission scheduling to ensure efficient and accurate data transmission between nodes. The research on routing optimization in wireless sensor networks is of great significance for improving network performance, extending network lifespan, providing reliable data transmission, and supporting real-time applications. By adopting effective routing optimization algorithms and mechanisms, more reliable, intelligent, and efficient wireless sensor network systems can be achieved. Wireless sensor network routing optimization plays an important role in the Internet of Things, as it can improve network performance and energy utilization efficiency, extend network lifespan, provide reliable data transmission and support real-time applications for various application fields. Therefore, routing optimization in wireless sensor networks is of great significance in promoting the development and application of animal networking [4,5].

Conclusion

Wireless sensor network routing optimization is one of the key issues in the Internet of Things, which is of great significance for improving network performance, extending network lifespan, providing reliable data transmission, and supporting real-time applications. The current routing optimization methods have problems such as high energy consumption, high transmission delay, high number of dead nodes, and low network throughput. To address these issues, this article proposes a wireless sensor network routing optimization method based on improved ant colony algorithm in the Internet of Things. Through experimental testing, it has been proven that the proposed method has absolute advantages over the current method in terms of energy consumption, transmission delay, number of dead nodes, and network throughput. This research achievement can not only improve the energy efficiency of wireless sensor networks, but also improve the quality of data transmission and provide better support for real-time applications. At the same time, this method can also optimize network capacity and meet the network performance requirements of different application scenarios. These contributions provide important support for the reliability, intelligence, and efficiency of wireless sensor networks, and promote the further development of wireless sensor network technology in various application fields. However, there are still some challenges in the research of routing optimization in wireless sensor networks. For example, when optimizing routing in a dynamic environment, it is necessary to consider issues such as changes in network topology and the balance between energy consumption and data transmission quality. These challenges require further in-depth research to propose more comprehensive and adaptable routing optimization methods for wireless sensor networks, and to promote the development and application of animal networking technology.

Abstract

The fast transition to the electrification of the energy system, combined with an exponential growth of the market share of electric vehicles, is leading to a tight interrelation between electric energy production and transportation, two prominent sectors in fossil fuels consumption and greenhouse gas emissions. Accelerating this process, the management of electric fluxes, aiming at optimizing production and demand coupling, plays a crucial role in reaching the net-zero emission target. The proposed software platform is designed to optimally manage the energy fluxes for a solar powered parking lot, serving a fleet of electric vehicles; the real-time knowledge of energy production and demand, in conjunction with forecasted power generation, allows the maximization of renewable energy self-consumption, thus reducing the exchange with the external grid. The software platform can work either in design mode, allowing the dimensioning of the various parking lot components, or in real-time mode managing instantaneously the energy balance. As a case study, it is tested on the 2019 parking lot mobility data of a research center, assuming a complete transformation of the then existing fleet of employees’ cars to electric vehicles. A comparison of the resulting energy flows with those projected by an established commercial tool is performed, as well as a preliminary economic evaluation. Both consistency of the simulation results and favorable economics validate the presented smart charging algorithm and Internet of Things platform for the real-time energy management of a solar parking lot.

Introduction

The steep growth of anthropogenic greenhouse gas (GHG) emissions related to the use of fossil resources requires fast and challenging solutions; transportation and electricity production sectors are responsible for around two thirds of global CO2 emissions. According to the numbers published by the U.S. Energy Information Administration (EIA) in its monthly energy review of August 2022 [1], the total consumption of primary energy in the U.S.A. in the year 2021 was 28.5 Million terawatt-hours (TWh) or 2.4546 Billion tonnes of oil equivalent (toe), 7.9 Million TWh (0.6782 Billion toe) of which are used by the transportation sector and 10.8 Million TWh (0.9267 Billion toe) by the electric power sector. 94% of the total energy used for transportation came from petroleum and natural gas and 6% from biofuels and electricity [2]. In 2021, according to EIA estimates [3], cars, light trucks, and motorcycles accounted for the largest shares of total U.S. transportation sector energy consumption, with light-duty vehicles (cars, small trucks, vans, sport utility vehicles, and motorcycles) accounting for 54.2%, commercial and freight trucks 4.5%, jets, planes, and other aircraft 8.7%, boat, ships, and other watercraft 4.6%, trains and buses 2.6%, the military sector 2.0%, pipelines 2.8%, lubricants 0.5%. Globally, carbon dioxide emissions associated with transportation accounted in 2021 for 7620 Mt; 600 Mt less than pre pandemic level by 2019 [4].

Since the onset of the industrial revolution, ever increasing amounts of GHG have been released into the atmosphere. While the energy mix and corresponding supply chains shifted from coal to oil and more recently towards natural gas, the prosperity of humankind still relies on fossil fuels and energy conversion systems based on fuels combustion. Even today, transportation and electricity production are heavily reliant on CO2 emitting technologies. But there is a silver lining, as technological improvements coupled with a strong cost reduction enabled the fast growth of the total installed energy capacity of renewable energy sources for electricity production (RES-E). Together with powerful battery energy storage systems (BESSs), they will impose drastic changes in these two historically conservative sectors. Today, the fast growth of electric vehicles (EVs) related technologies, as well as their forecasted market penetration, reaching the record of 6.6 million vehicles sold in 2021 [5], generates new scenarios impacting the electric energy production and transportation market. Based on existing climate-focused policy pledges and announcements, the International Energy Agency predicts for 2030 EVs to represent more than 30% of vehicles sold globally across all modes (excluding two- and three-wheelers) and the global electricity demand for EVs to reach 1100 TWh; then about 4% of the total final electricity demand [5].

Conclusions

This paper presented the SPEM IoT platform, capable of simulating, designing and real time managing the smart charging of EV fleets in a RES-E driven parking lot.

A deterministic rule-based algorithm was introduced for the fine-grained management of the energy fluxes between EVs and the different electric components of a solar parking lot. Since the algorithm does not utilize any assumption on the future mobility and energy demand it can handle unforeseen changes avoiding critical situations.

As it was shown, intra-day deferral serves to shift EV charging demand towards the time of maximum production, guided by MPC, constantly analyzing the momentary situation of 1) intermittent solar production, 2) stored energy and energy storage availability, 3) charging station occupancy and 4) solar production forecast.

An extensive case study was conducted, where the design mode of SPEM was applied to the historic situation of an Italian Research Center in 2019, assuming the substitution of all employee’s cars by EVs. As a benchmark for the validation of SPEM, HOMER® Grid, a commercial software for the modeling, design and simulation of electrical infrastructure, was used.

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 platforms constructed on Trusted Computing Technology are commonly referred to as trusted cloud computing. The core component of this technology is known as Trusted Platform Module (TPM). Its specifications are given by the consortium known as Trusted Computing Group (TCG). Though TPM is an immovable module and is fixed inside any computer, yet its credentials can be shared with other devices. This paper presents the accessibility model for Virtual Machine (VM) of a distributed trust protocol which is based on TPM, through which consumer’s credentials are shared to other trusted devices. These credentials essentially describe the integrity of VM.

Introduction

     Cloud computing is a new way of outsourcing IT services as an online service. Although varying to the field of distributed, cluster or grid computing the use of the Internet in cloud computing separates it from the cluster and the grid computation. As per the terminology of cloud computing implies, the word cloud refers to the Internet that rawhides the basic enigmas of cloud computing users and conveys the seamless thinking of accepting Information Communication Technology (ICT) computation facilities from the cloud service provider (CSP). The domain of cloud computing includes three components namely Cloud Infrastructure, Cloud Provider and Cloud Consumer. Cloud computing services are used in other forms of conveyance such as Software as a Service, Platform as a Service and infrastructure as a service. According the definition of SaaS, code and databases furnished to several clients. In PaaS, software development platform is provided. IaaS allows the consumer to produce a complete and fully functional virtual machine with network capability, data storage devices, CPU or GPU processing, run time memory and almost all other resources needed for integration.

     Cloud services can be used likewise we use utilities in our daily life i.e. pay what you use. IaaS model is a fundamental layer for the cloud environment. At this layer computing resources are designed for virtualization purposes, in order to achieve widespread distribution and deployment. Due to high deployment, there is a high risk of security attacks and it is very difficult for a cloud buyer to rely on such a multitenant site. Cloud computing and IaaS security issues such as data security and privacy, trust, multiple tenants, damage, and data leaks, malicious VM etc are shown in [2] and [3]. Caring these issues it is very difficult for the consumer to rely on the infrastructure provided to the consumer. User stores data in bought machines (VM) swung on the provider facet for processing and other purposes. It is important to examine the cloud equipment for privateness and integrity before, after and at some stage in VM implementation. The literature reviewed for this motive conveys the clean impact that there may be a loss of such structures that permit the user to discover a VM controlled with integrity and privateness. In the IaaS framework all environmental safety measures are done via way of means of the provider, so the user ought to depend now no longer best on his or her infrastructure however additionally on his or her personal countermeasures

Conclusion

     Cloud computing is an internet-centric technology for outsourcing IT services such as software, platform, and infrastructure. In this research work accessibility model for the guest VM is proposed. In the proposed model integrity values of guest VM are shared and synchronized with more than one authenticated client devices to access the guest VM from everywhere and anywhere.

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

Electroencephalography (EEG), which tracks the brain waves that contain the brain’s neural activity, plays an essential role in detecting human motion and treating neurological diseases. In the Artificial Intelligence (AI) era, deep learning algorithms are widely used in human action recognition and classification. Various convolutional neural networks that process this signal are also being born. This paper provides a detailed survey of the application of deep learning to EEG signals and outlines the research process when classifying EEG signals. At the same time, this paper reviews the relevant research on the classification of human action EEG signals in recent years. Human motion signals usually use different deep learning algorithms and convolutional neural network architectures in the EEG signal analysis task. This article will discuss the advantages and challenges of each method in other studies. Finally, the paper discusses future directions for deep learning-based EEG signal classification.

Abstract

Undoubtedly, the evolution of Generative AI (GenAI) models has been the highlight of digital transformation in the year 2022. As the different GenAI models like ChatGPT and Google Bard continue to foster their complexity and capability, it’s critical to understand its consequences from a cybersecurity perspective. Several instances recently have demonstrated the use of GenAI tools in both the defensive and offensive side of cybersecurity, and focusing on the social, ethical and privacy implications this technology possesses. This research paper highlights the limitations, challenges, potential risks, and opportunities of GenAI in the domain of cybersecurity and privacy. The work presents the vulnerabilities of ChatGPT, which can be exploited by malicious users to exfiltrate malicious information bypassing the ethical constraints on the model. This paper demonstrates successful example attacks like Jailbreaks, reverse psychology, and prompt injection attacks on the ChatGPT. The paper also investigates how cyber offenders can use the GenAI tools in developing cyber attacks, and explore the scenarios where ChatGPT can be used by adversaries to create social engineering attacks, phishing attacks, automated hacking, attack payload generation, malware creation, and polymorphic malware. This paper then examines defense techniques and uses GenAI tools to improve security measures, including cyber defense automation, reporting, threat intelligence, secure code generation and detection, attack identification, developing ethical guidelines, incidence response plans, and malware detection. We will also discuss the social, legal, and ethical implications of ChatGPT. In conclusion, the paper highlights open challenges and future directions to make this GenAI secure, safe, trustworthy, and ethical as the community understands its cybersecurity impacts.

Introduction

The evolution of Artificial Intelligence (AI) and Machine Learning (ML) has led the digital transformation in the last decade. AI and ML have achieved significant breakthroughs starting from supervised learning and rapidly advancing with the development of unsupervised, semi-supervised, reinforcement, and deep learning. The latest frontier of AI technology has arrived as Generative AI [1]. Generative AI models are developed using deep neural networks to learn the pattern and structure of big training corpus to generate similar new content [2]. Generative AI (GenAI) technology can generate different forms of content like text, images, sound, animation, source code, and other forms of data. The launch of ChatGPT [3] (Generative Pre-trained Transformer), a powerful new generative AI tool by OpenAI in November 2022, has disrupted the entire community of AI/ML technology [4]. ChatGPT has demonstrated the power of generative AI to reach the general public, revolutionizing how people perceive AI/ML. At this time, the tech industry is in a race to develop the most sophisticated Large Language Models (LLMs) that can create a human-like conversation, the result of which is Microsoft’s GPT model [5], Google’s Bard [6], and Meta’s LLaMa [7]. GenAI has become a common tool on the internet within the past year. With ChatGPT reaching 100 million users within two months of release, suggesting that people who have access to the internet have either used GenAI or know someone who has [8]. Figure 1 demonstrates the working of an AI-powered chatbot where a user initiates requests, and after analysis using Natural Language Processing (NLP), is given a real-time response by the chatbot. This response is analyzed again to provide a better user experience in the proceeding conversation.

Conclusion

GenAI driven ChatGPT and other LLM tools have made significant impact on the society. We, as humans, have embraced it openly and are using them in different ingenious ways to craft images, write text or create music. Evidently, it is nearly impossible to find a domain where this technology has not infringed and developed use-cases. Needless to mention, cybersecurity is no different, where GenAI has made significant impacts how cybersecurity posture of an organization will evolve with the power and threat ChatGPT (and other LLM tools) offers. This paper attempts to systematically research and present the challenges, limitations and opportunities GenAI offers in cybersecurity space. Using ChatGPT as our primary tool, we first demonstrate how it can be attacked to bypass its ethical and privacy safeguards using reverse psychology and jailbreak techniques. This paper then reflects different cyber attacks that can be created and unleashed using ChatGPT, demonstrating GenAI use in cyber offense. Thereafter, this article also experiment various cyber defense mechanisims supported by ChatGPT, followed by discussion on social, legal and ethical concerns of GenAI. We also highlight the key distinguishing features of two dominant LLM tools ChatGPT and Googe Bard demonstrating their capabilities in terms of cybersecurity. Finally, the paper illustrates several open challenges and research problems pertinent to cybersecurity and performance of GenAI tools. We envision this work will simulate more research and develop novel ways to unleash the potential of GenAI in cybersecurity.