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

This article aims to discuss the consensus mechanism of software-defined blockchain in the Internet of Things, analyze the characteristics of the traditional consensus mechanism algorithms, on the basis of comparing the advantages of each model, the traditional consensus mechanism algorithm is improved. Later, a supervisable consensus scheme based on improved DPOS-PBFT (Delegated proof of stake-Practical Byzantine Fault Tolerance) was proposed. In the context of the development of the Internet of Things technology, the decentralized distributed computing paradigm is used to improve the blockchain smart contract technology, and the DPOS blockchain consensus mechanism is optimized based on the DPOS protocol of the credit model. In addition, through the dynamic grouping algorithm of credibility, the research ranks the credit level of the consensus nodes of the blockchain network, thus further realizing the supervision of the Internet of Things system. The results of the case analysis show that the success rate of the mechanism algorithm can still be maintained at about 97% after 3000 user requests, the maximum delay remains below 8s after 3000 user requests, the minimum delay is always around 3s, the average delay is 2.38s, the overall performance of the algorithm is superior. It can ensure the final consistency of data transmission of each node in the Internet of Things, the research on the blockchain consensus mechanism in the Internet of Things has practical reference value.

Introduction

With the development of physical network technology, Bitcoin [1] As a new encrypted digital currency, it began to be used in the field of financial security and widely used in the supply chain, as the underlying technology to realize Bitcoin virtualization, Blockchain technology has begun to attract people’s attention. The essence of blockchain is a decentralized distributed database, as an emerging technology, Blockchain provides users with a reliable exchange trading platform with its own advantages such as autonomy, synchronicity, and anonymity, provide data integrity to ensure the authenticity of content and user transparency. In the budding period of the development of blockchain [[2], [3], [4]], as the bottom layer of Bitcoin, the application of blockchain is only limited to the use of mathematical methods to calculate and maintain the recorded data of the distributed ledger that will not be tampered with. With the continuous development of blockchain technology, it began to be gradually applied to stocks and bonds in the financial field. In transactions, people introduce “smart contracts” through programs and algorithms, apply blockchain technology to ensure the integrity and reliability of financial transactions. In the current stage of development, Blockchain technology has entered a period of rapid development, the development of technologies such as big data [5] and artificial intelligence [6] has brought about changes in Internet technology, the IoT solution of the autonomous self-organizing network has gradually penetrated into all aspects of social life. The connection between the Internet of Things and advanced technologies such as big data and artificial intelligence is constantly deepening.

Conclusion

With the development of the Internet of Things, the emergence of many decentralized P2P self-organizing network trading platforms makes blockchain technology a hot topic of research quickly, traditional distributed consensus algorithms have problems such as network delay and transmission errors, in order to solve these problems, this research proposes an improved DPOS-PBFT supervisable consensus mechanism based on the traditional consensus mechanism algorithm, and compare the performance with traditional consensus algorithms such as Raft, POW, POS, DPOS, PBFT, etc., the results of the case analysis show that the success rate of the mechanism algorithm can still be maintained at about 97% after 3000 user requests, the maximum delay remains below 8s after 3000 user requests, the minimum delay is always around 3s, the average delay is 2.38s, the overall performance of the algorithm is superior. However, the research still has some shortcomings. First, this research did not consider the issue of secure multi-party computing, in the distributed network to solve the collaborative computing of users who do not trust each other, this problem should be taken into consideration, therefore, secure multi-party computing can be integrated with the consensus mechanism in the future. Second, the number of specific blockchain nodes in the Ethereum environment varies according to the type of application, in the future, different numbers of attack nodes can be added to the Ethereum network for testing.

Abstract

     Concrete is one of the most widely used materials in various civil engineering applications. Its global production rate is increasing to meet demand. Mechanical properties of concrete are among important parameters in designing and evaluating its performance. Over the past few decades, machine learning has been used to model real-world problems. Machine learning, as a branch of artificial intelligence, is gaining popularity in many scientific fields such as robotics, statistics, bioinformatics, computer science, and construction materials. Machine learning has many advantages over statistical and experimental models, such as optimal accuracy, high-performance speed, responsiveness in complex environments, and economic cost-effectiveness. Recently, more researchers are looking into deep learning, which is a group of machine learning algorithms, as a powerful method in matters of diagnosis and classification. Hence, this paper provides a review of successful ML and DL model applications to predict concrete mechanical properties. Several modeling algorithms were reviewed highlighting their applications, performance, current knowledge gaps, and suggestions for future research. This paper will assist construction material engineers and researchers in selecting suitable and accurate techniques that fit their applications.

Introduction

     Concrete is the most widely used building material worldwide. With population growth and urbanization, the demand for concrete is expected to reach 18 billion by 2050 [[1], [2], [3]]. In order to improve the design of concrete structures, it is necessary to gain a better understanding of concrete performance, relying on accurate evaluation of its mechanical properties. Among the various properties of concrete, compressive strength has been considered a direct indicator of performance. It is directly related to the safety and performance of the structure throughout its life cycle [1,4,5].

     Concrete is a complex system of combinations of different components (coarse and fine aggregates, water, cement, and additional mixtures) that are randomly distributed throughout the concrete matrix [[6], [7], [8], [9]]. This heterogeneous feature makes it difficult to accurately predict certain mechanical properties, especially compressive strength [[10], [11], [12]]. The most direct way to evaluate the compressive strength of concrete is through physical tests performed on specimens cured to the desired age [13,14]. Such a method for evaluating the compressive strength needs time while being affected by other factors related to specimen fabrications and test operators. Moreover, the test tends to damage the specimens. Empirical regression methods were therefore proposed to predict compressive strength [[15], [16], [17]], but the disadvantage of this method is the non-linear relationship between the concrete mixture and the concrete’s compressive strength. This prevents an accurate regression expression. Numerical simulation is another method that can predict the behavior of concrete. However, a good prediction of concrete behavior is not easy to achieve due to the non-linearity and randomness [[18], [19], [20]].

Conclusions and recommendations

     The present study investigated the performance of different ML/DL models to predict the mechanical properties of concrete. Among the wide range of ML/DL models, support vector machines, decision trees, evolutionary algorithms, and artificial neural networks were examined due to the popularity of these models as well as their frequent use in the field of civil engineering. Therefore, the performance of these models in various studies to evaluate the compressive strength, tensile strength, shear strength, flexural strength, and elastic modulus was evaluated. A comparison of experimental models with ML/DL methods shows that ML/DL models with better updating capability and the ability to analyze large datasets perform better. On the other hand, statistical models are not good for predicting complex structures due to their high cost and time-consuming nature. According to ML/DL methods, selecting the appropriate model for predicting the target strength of concrete should be made by considering different criteria. A review of various studies shows that the relationship between concrete components and mechanical strength is influential in choosing the forecasting model. Therefore, models that can respond in nonlinear space should be used if the relationship is nonlinear. In these cases, SVM and ANN models can be used for their acceptable performance in a non-linear environment with fewer errors. To achieve more accurate results, optimization of these models with metaheuristic algorithms can also be used. But if we need the transparency of the model and the explicit mathematical formula between input and output, we can use decision tree models and evolutionary algorithms. On the other hand, the use of ensemble models to optimize these models, although it results in higher accuracy, increases computation time and model complexity. The combined SVM and ANN models, although they increase the computation time, have accurate results in the face of extensive data. Therefore, based on the study, the best option for predicting the strength of concrete in terms of model accuracy and model implementation is the combined use of SVM and ANN combined models.

Abstract

     Recent advances in wearable sensing and machine learning have created ample opportunities for “in the wild” movement analysis in sports, since the combination of both enables real-time feedback to be provided to athletes and coaches, as well as long-term monitoring of movements. The potential for real-time feedback is useful for performance enhancement or technique analysis, and can be achieved by training efficient models and implementing them on dedicated hardware. Long-term monitoring of movement can be used for injury prevention, among others. Such applications are often enabled by training a machine learned model from large datasets that have been collected using wearable sensors. Therefore, in this perspective paper, we provide an overview of approaches for studies that aim to analyze sports movement “in the wild” using wearable sensors and machine learning. First, we discuss how a measurement protocol can be set up by answering six questions. Then, we discuss the benefits and pitfalls and provide recommendations for effective training of machine learning models from movement data, focusing on data pre-processing, feature calculation, and model selection and tuning. Finally, we highlight two application domains where “in the wild” data recording was combined with machine learning for injury prevention and technique analysis, respectively.

Introduction

     Measuring sports movement during training and competition allows monitoring athletes’ performance and their risk of injury (Camomilla et al., 2018, Cust et al., 2019). Performance monitoring is relevant to assess motor capacity and physical demand, as well as to analyze technique and how technique impacts performance (Camomilla et al., 2018). In the complementary perspective of sport-related injuries, monitoring can be oriented to preventing, assessing, and informing the recovery from injuries (Preatoni et al., 2022).

     The state-of-the-art approach to measure movement is to perform a biomechanical analysis from data recorded in a lab environment using optical motion capture (OMC) systems and force plates. Such an analysis includes calculating spatio-temporal variables, as well as joint angles, joint moments, and ground reaction forces. Musculoskeletal models can provide additional insights into muscle forces and activations, which cannot be measured directly. However, these state-of-the-art measurement techniques can only be used in a laboratory environment and are limited by high costs, a stationary setup, and a short duration.

Conclusion

     In this perspective paper, we have discussed different opportunities and challenges regarding analysis of “in the wild” movement data using machine learning. We have highlighted the importance of careful data recordings. This is important when recording movement in general, to collect the correct data at the correct point in time, but becomes even more important when machine learning is used for processing, to ensure that the trained model works for the data it should be analyzing, and that the correct analysis is performed. We have discussed several considerations regarding both data recording in general and specific to training of machine learning models, to ensure that in future researchers can properly address these in experiments. Furthermore, we highlighted how “in the wild” movement data can be used in two application domains, specifically monitoring injury risk and technique analysis.

     By combining machine learning with “in the wild” recordings, the main advantages are the potential for real-time feedback, while analyses can also be performed on large datasets. The real-time potential can be used to enhance the performance of athletes during training and competition. Its advantages for large datasets can be exploited when performing long-term monitoring to forecast injuries, especially by developing personalized models, while statistical features could be found that provide more insight than traditional parameters as well. The combination of real-time feedback with analysis on large datasets allows for computationally efficient calculation of many variables, e.g. joint angles of a kinematic model, which can then be used to create avatars of athlete’s movements to provide actionable insights. In conclusion, this perspective paper provides researchers with guidance and directions for future development of machine learning models for movement analysis “in the wild”.

Abstract

Mobile cloud computing enables compute-limited mobile devices to perform real-time intensive computations such as speech recognition or object detection by leveraging powerful cloud servers. An important problem in large-scale mobile cloud computing is computational offloading, where each mobile device decides when and how much computation should be uploaded to cloud servers by considering the local processing delay and the cost of using cloud servers. In this paper, we develop a distributed threshold-based offloading algorithm where it uploads an incoming computing task to cloud servers if the number of tasks queued at the device reaches the threshold and processes it locally otherwise. The threshold is updated iteratively based on the computational load and the cost of using cloud servers. We formulate the problem as a symmetric game, and characterize the sufficient and necessary conditions for the existence and uniqueness of the Nash Equilibrium (NE) assuming exponential service times. Then, we show the convergence of our proposed distributed algorithm to the NE when the NE exists. Further, we characterize the performance gap between cost under our proposed distributed algorithm and the minimum cost in terms of Price of Anarchy (PoA) when the cost of using cloud servers is high. Finally, we perform extensive simulations to validate our theoretical findings, demonstrate the efficiency of our proposed distributed algorithm under various scenarios such as hyperexponential service times, imperfect server utilization estimation, and asynchronous threshold updates, and reveal the superior performance of threshold-based policies over their probabilistic counterpart.

Abstract

The Internet of Things (IoT) provides to the client an effective technique for communicating with the Web world through ubiquitary object enabled networks. The rapid progress in IoT connected devices creates a huge amount of data in a second from personal and industrial devices. This information should be utilized to help business and functional objectives. Thus, there is an urgent requirement for adopting cloud computing, big data, and artificial intelligence techniques to enable storage, analytics, and decision making. In this article, we focused our consideration to integrate Cloud Computing, Big data and Artificial Intelligence technique with the Internet of Things devices. Cloud computing, Big Data, Artificial Intelligence, and IoT are different techniques that are already part of our life. Their adoption and uses are expected to make them more comprehensive and make them essential components of the future Internet. The Internet of Things (IoT) is a system of interconnected gadgets, digital or mechanical machines that are given exceptional identifiers and the capacity to move information over an organization without expecting human-to-human or human-to-pc collaboration.

Introduction

With the wide-spread discovery of techniques in the current digital era, increasing physical entities are interconnected to the Internet of Things (IoT) devices. In recent years IoT technologies are applied with different techniques such as Artificial Intelligence, Big Data, and Cloud Computing. Artificial Intelligence (AI) is a technique which has the ability to compute a huge amount of task that is usually done by a human. Artificial Intelligence uses different learning techniques to facilitate automatic rules and regulations for decision making. Artificial intelligence is divided into two different modules such as learning module and predicate module [1]. The learning module is used for effective data collection, training, and data modeling. And the predicate module is used to take action on the current situation. The flow and storage of exponentially increasing data are easily managed by Artificial Intelligence (AI). The integration of cloud computing and IoT are also two different technologies that assume a vital part in our daily life. Cloud computing and the Internet of Things are merged together is expected to break both current and future internet which we called as new paradigm CloudIoT [2]. In the era of the internet which plays a fundamental role in cloud computing, it seems to be represented as a medium or the platform through which many different cloud computing services are accessible or delivered its services. If you are thinking that the internet as a virtual “space” for connecting users from over the globe, it is like a cloud, sharing information by using the internet. Cloud computing is the trending technology in the daily life of everyone which provides on-demand web services such as networking devices, data storage, servers, and applications. It provides higher flexibility and cost efficiency while users try to use cloud computing resources and applications.

Conclusion

The Internet of Things is a wide field and contains incredible and different variety of applications. The main aim of IoT is to provide a facility of exchange information and synergic performance between devices and peoples via global machine-to-machine (M2M) networks. Due to M2M network, exchange of information between the devices creates an exponential amount of data. It is impossible to manage or keep secure these generated personal or organizational data in local devices. The IoT techniques need centrally storage space for personal or industrial data. To avoid these problems, in this paper we have introduced the integration of Cloud Computing, Big Data and Artificial Intelligence techniques with the Internet of Things devices. The integration of cloud computing with IoT techniques stores the information centrally generated by the IoT devices. And the collected data which is centrally stored in cloud computing is managed, processed, and analyzed by the Big Data Techniques. To extract the high volume of IoT data in real-time, processing needs machine learning and AI algorithms.

Abstract

     Providing pay-as-you-go storage and computing services have contributed to the widespread adoption of cloud computing. Using virtualization technology, cloud service providers can execute several instances on a single physical server, maximizing resource utilization. A challenging issue in cloud data centers is that available resources are rarely fully utilized. The server utilization rate is poor and often below 30%. An accurate host workload prediction enhances resource allocation resulting in more efficient resource utilization. Recently, numerous methods based on deep learning for predicting cloud computing workload have been developed. An efficient strategy must predict long-term dependencies on nonstationary host workload data and be quick enough to respond to incoming requests. This study employs a Bidirectional Gated-Recurrent Unit (BiGRU), Discrete Wavelet Transformation (DWT), and an attention mechanism to improve the host load prediction accuracy. DWT is used to decompose input data into sub-bands with different frequencies and to extract patterns from nonlinear and nonstationary data in order to improve prediction accuracy. The extracted features are fed into BiGRu to predict future workload. The attention mechanism is used in order to extract the temporal correlation features. This hybrid model was evaluated with cluster data sets from Google and Alibaba. Experimental results reveal that our method improves prediction accuracy by 3% to 56% compared to a variety of state-of-the-art methods.

Introduction

     Cloud computing indicates the on-demand accessibility of computer system resources, particularly data storage and computing resources, enabling the users to manage without direct intervention [1], [2]. Organizations can rent cloud computing services as an alternative to investing in their own computing infrastructure or data centers. [3]. The various pay-as-you-go cloud services not only enable clients to purchase resources on-demand [4] but also enables the provision of an infinite amount of resource capacity (e.g., CPU, memory, network, and disk) at a reasonable price without investing in infrastructure or incurring additional expenditures for maintenance [5], [6], [7]. The average capacity utilization rate for regular deployments is less than 40%, although businesses require a relatively large number of servers and other resources to ensure the quality of service (QoS) during peak periods [8], [9]. Fig. 1 depicts boxplots of CPU consumption over two working days for 50 Google cluster machines. Each record of this data represents the cumulative consumption in 5 min. As seen in Fig. 1, however, the average CPU utilization rarely exceeds 50%, and in most cases, it is less than 30%.

Conclusion

     This article proposes a host workload prediction method in cloud computing by combining the DWT, BiGRU model and attention mechanism. In addition to learning long-term dependencies in BiGRU, DWT can decompose nonlinear and nonstationary data into predictable subbands in order to predict future host workload in cloud computing. The proposed approach was evaluated using two real-time host load trace datasets, the Google Cluster Database and the Alibaba Cluster. According to the experimental results, basic techniques cannot learn nonlinear data, mainly when random fluctuations occur in the data. However, the model presented in the proposed method shows good compatibility and achieves better results than DWT-LSTM, DWT-BPNN, DWT-SVR, LSTM, BPNN, and SVR in both datasets. Since distributed computing is increasingly oriented toward lightweight virtualization technologies such as containers, we will predict container workloads in the Docker and Kubernetes environments in future research. We run various applications in these environments, and a series of simulations will run to generate tasks and measure the container’s CPU load. Then, the workload will be predicted using machine learning models, and resource provisioning will be performed.

Abstract

     With the rapid development of the Internet of Things (IoT), there are several challenges pertaining to security in IoT applications. Compared with the characteristics of the traditional Internet, the IoT has many problems, such as large assets, complex and diverse structures, and lack of computing resources. Traditional network intrusion detection systems cannot meet the security needs of IoT applications. In view of this situation, this study applies cloud computing and machine learning to the intrusion detection system of IoT to improve detection performance. Usually, traditional intrusion detection algorithms require considerable time for training, and these intrusion detection algorithms are not suitable for cloud computing due to the limited computing power and storage capacity of cloud nodes; therefore, it is necessary to study intrusion detection algorithms with low weights, short training time, and high detection accuracy for deployment and application on cloud nodes. An appropriate classification algorithm is a primary factor for deploying cloud computing intrusion prevention systems and a prerequisite for the system to respond to intrusion and reduce intrusion threats. This paper discusses the problems related to IoT intrusion prevention in cloud computing environments. Based on the analysis of cloud computing security threats, this study extensively explores IoT intrusion detection, cloud node monitoring, and intrusion response in cloud computing environments by using cloud computing, an improved extreme learning machine, and other methods. We use the Multi-Feature Extraction Extreme Learning Machine (MFE-ELM) algorithm for cloud computing, which adds a multi-feature extraction process to cloud servers, and use the deployed MFE-ELM algorithm on cloud nodes to detect and discover network intrusions to cloud nodes. In our simulation experiments, a classical dataset for intrusion detection is selected as a test, and test steps such as data preprocessing, feature engineering, model training, and result analysis are performed. The experimental results show that the proposed algorithm can effectively detect and identify most network data packets with good model performance and achieve efficient intrusion detection for heterogeneous data of the IoT from cloud nodes. Furthermore, it can enable the cloud server to discover nodes with serious security threats in the cloud cluster in real time, so that further security protection measures can be taken to obtain the optimal intrusion response strategy for the cloud cluster.

Introduction

     With the extension and expansion of Internet technology, the Internet of Things (IoT), as an important information technology, promotes the intelligent development of modern society. Intelligent IoT applications are being gradually popularized in daily life and industrial development, such as intelligent logistics, transportation, security, medical treatment, homes, and agriculture. The centralized processing mode of cloud computing is to upload all pending transactions to the cloud for execution, and the cloud uses computing resources, storage capacity, and transmission bandwidth to process all service requests sent by end users. The IoT refers to connecting any object with the network through information sensing equipment and according to the agreed protocol. Objects exchange and communicate information through information media to realize the functions of intelligent identification, positioning, tracking, supervision, and so on. The number of global IoT device connections has increased annually, and according to forecasts, the growth rate will gradually stabilize at 15%. Furthermore, data transmission, processing, and storage will add more load to cloud services. Mobile and sensor devices connected to the edge of the Internet are constantly generating new data with diverse data types [1], which will lead to great challenges in the computing resources and transmission bandwidth of traditional network architecture. At the same time, for IoT applications that require low network latency, such as automatic driving, if the traditional calculation is used as the operation model, low latency cannot be achieved, and thus the vehicle cannot make real-time decisions to avoid risk in the case of emergency braking. The tasks to be processed by the terminal equipment require strong real-time performance, i.e., the tasks must complete data transmission, analysis, and processing with minimum delay, which conflicts with the processing method of the traditional computing model. In summary, in this highly information-based era, the IoT faces multiple severe challenges: a large amount of data redundancy, cloud processing capacity bottlenecks, network bandwidth limits, data security and privacy, increased cloud power load, and increased task processing delays [2]. Intrusion detection technology is an active security protection technology. According to the different methods of data analysis, IoT intrusion detection systems can be divided into anomaly intrusion detection and misuse intrusion detection. Anomaly intrusion detection is an attack or malicious behavior that occurs when the host data in the network or the entire network does not conform to normal data specifications. Misuse intrusion detection refers to modeling certain specific attack models, determining the characteristic behavior of the attack, and matching it with the current behavior. A successful match represents malicious behavior [3]. The above problems are caused by limitations in the development of the IoT data processing mode and the increasing requirements for service quality for processing operations. The cloud collaborative network architecture has brought the terminal service quality to a new development stage and promoted fast-paced technology development. The optimization of task scheduling and application module mapping is a key problem in cloud computing, which plays an important role in the performance and power optimization of network architecture. Therefore, designing an efficient scheme and algorithm for IoT intrusion detection, task scheduling, and application module mapping is the key to improving the overall performance of the IoT architecture, which is of great research significance.

Conclusion and future work

     The IoT is a complex, dynamic environment, which poses new challenges to the deployment and algorithm performance of IoT intrusion detection. Based on the research of cloud-based IoT defense architecture, this study focused on cloud computing intrusion detection algorithms and measurement models. Considering the resource constraints of cloud nodes and the high complexity of the network environment, a measurement model of IoT intrusion detection in a cloud computing environment is proposed, and an MFE-ELM algorithm of a limit learning machine with multi-feature extraction is proposed. Experimental simulations verified the training time and detection accuracy of the proposed algorithm. In the experiment, the MFE-ELM algorithm showed good performance in cloud computing, especially in terms of accuracy and time efficiency. Through several experiments, it was proved that the MFE-ELM is an intrusion detection algorithm suitable for cloud computing environments.

Abstract

The research aims to reduce the network resource pressure on cloud centers (CC) and edge nodes, to improve the service quality and to optimize the network performance. In addition, it studies and designs a kind of edge–cloud collaboration framework based on the Internet of Things (IoT). First, raspberry pi (RP) card working machines are utilized as the working nodes, and a kind of edge–cloud collaboration framework is designed for edge computing. The framework consists mainly of three layers, including edge RP (ERP), monitoring & scheduling RP (MSRP), and CC. Among the three layers, collaborative communication can be realized between RPs and between RPs and CCs. Second, a kind of edge–cloud​ matching algorithm is proposed in the time delay constraint scenario. The research results obtained by actual task assignments demonstrate that the task time delay in face recognition on edge–cloud collaboration mode is the least among the three working modes, including edge only, CC only, and edge–CC collaboration modes, reaching only 12 s. Compared with that of CC running alone, the identification results of the framework rates on edge–cloud collaboration and CC modes are both more fluent than those on edge mode only, and real-time object detection can be realized. The total energy consumption of the unloading execution by system users continuously decreases with the increase in the number of users. It is assumed that the number of pieces of equipment in systems is 150, and the energy-saving rate of systems is affected by the frequency of task generation. The frequency of task generation increases with the corresponding reduction in the energy-saving rate of systems. Based on object detection as an example, the system energy consumption is decreased from 18 W to 16 W after the assignment of algorithms. The included framework improves the resource utility rate and reduces system energy consumption. In addition, it provides theoretical and practical references for the implementation of the edge–cloud collaboration framework.

Introduction

In the context of the rapid development of the Internet of Things (IoT), the basic network framework is faced with huge challenges because of the surge in industrial data [1]. The mass production and use of IoT has brought many security issues. Although several organizations have published guidelines for IoT use security, few IoT providers are able to properly follow these guidelines due to lack of accountability [2]. Traditional centralized cloud computing centers cannot deal with massive digital business. In this context, edge computing demonstrates its significant advantages. For example, it can perform the initial analysis of the input data on edge nodes and upload a few data that it cannot process to the cloud center (CC) for processing, which effectively reduces the storage computing and data transmission costs of edge nodes. In addition, the network resource pressures on edge computing nodes and CC are also reduced correspondingly [3]. CC possesses strong storage and computing capacities. The real-time performance of edge servers is significant, and they respond quickly and are flexible. The combination of edge servers with CC to support the 5th generation (5G) basic network can promote the rapid development of domestic manufacturing and accelerate its digital transformation [4], [5].

The edge–cloud collaboration framework is a hot topic of the current research into the IoT. The advantages of the edge–cloud collaboration framework are more obvious, especially when the actual node tasks are considered [6]. After the reinforcement of the processing capacity of edge nodes, they can deal with the uploading of deep learning and other heavy tasks. In addition, actual application scenarios constrain voltage regulation frequency and chip technology to achieve flexible assignment in industry. At the moment, edge nodes unload intensive computing tasks with the constraints of their own resources [7], [8]. Related literature analyses IoT data by the assignment of different user roles and further proposes a kind of IoT data analysis framework structure, which can make the maximum use of cloud resources and then generate corresponding computing models. On the edge sides, the model structure is adopted in the real-time operation on controllers [9], [10], [11].

Conclusion

With the background of IoT, it is difficult to meet the requirements of the high-quality low time delay by delay-sensitive applications because of the long physical distance between CC and sensors. As a result, the service quality can hardly be guaranteed. In this research, cloud computing is combined with edge computing to study and design a kind of edge–cloud collaboration framework. First, RP card machines are utilized as the working nodes in multitask scenarios, and a kind of edge–cloud collaboration framework is designed for edge computing. The framework consists mainly of ERP, MSRP, and CC layers. Among each of the layers, the collaborative communication between RPs and between RP and CC can be realized by wireless networks. Second, a kind of edge–cloud matching algorithm is proposed in time delay constraint scenarios to achieve the deployment of industrial manufacturing lines by initial edge–cloud collaboration frameworks. In addition, the resource utility rate is enhanced, and the system energy consumption is saved. Due to time limits, there are still many disadvantages in the research, and further studies are necessary. During the framework construction in the research, remote command, only remote data streaming transmission, and file transmission are taken into account. In subsequent research, lightweight node tasks will be virtualized combined with KUbemet and lightweight container technologies (such as Docker).

Abstract

The IoT market is continuously expanding, and security measures parallel to the growing industrialization has drawn the research community’s attention. While several works have focused on the different technologies used to protect IoT devices, this paper aims to narrow the focus to Blockchain and Artificial Intelligence (AI) solutions for security and privacy issues faced by these devices. These technologies address two major vulnerabilities with edge devices on IoT networks: centralization and weak edge nodes. Blockchain provides a distributed system that can help avoid a single-point failure and can be used to optimize battery life on low-level devices by load balancing. On the other hand, AI’s power to learn and adapt is critical to automate systems and make them intelligent enough to analyze collected data. The combination of these two technologies is capable of mitigating multiple challenges IoT architectures face currently. The paper discusses the latest major architectures that integrate Blockchain and AI to enhance security within the. This paper also presents the comprehensive take on the device security and drawbacks to these mechanisms. This work also addresses the future of security in IoT and suggests different areas that need further work to better comprehend how the combination of these technologies can assist in enhancing security.

INTRODUCTION

The Internet of Things (IoT) market, a network of devices that communicate and exchange data with each other, has expanded over the last decade. IoT devices provide different applications such as health care, smart homes, wearables, and smart cities. The advent of a wide range of devices requires security that matches its capabilities to ensure the safety of the system and network. The number and type of attacks carried out against IoT devices have increased exponentially. As the need for faster and more efficient devices increases, there is a lag in equal breakthroughs in IoT security. According to Business Insider’s 2020 IoT research, IoT devices are predicted to increase to 41 billion by 2027 [1].

In order to meet this challenge, we must focus on the security strategies that match IoT devices’ hardware and computational power to minimize internal or outsider attacks.

CONCLUSION

This paper defined the role that AI/ML and Blockchain have currently in IoT devices, explaining in detail how Blockchain technology works and how AI/ML is used to lessen the computational load on IoT devices. We then talked about Blockchain and AI/ML in IoT security and how having these technologies strengthens privacy, data integrity, and data processing. Four potential solutions were explored and analyzed to understand how they could help deficiencies in the current IoT network setup as well as challenges that were not addressed in the solutions. Finally, future lanes of research were brought up for researchers to study.

This paper presented the role that AI/ML and Blockchain have currently in IoT devices and how these two can work in tandem to lessen the computational load on IoT devices. The paper then discussed the scope of Blockchain and AI/ML in IoT security towards strengthening privacy, data integrity, and data processing. Then it addresses the vulnerabilities and feasibility of AI/ML and Blockchain towards IoT security. With several potential solutions, four major works were studied. The paper then progresses to analyze these works on several parameters, summarizing them based on their: 1) Feasibility, 2) Security Strength, 3) Applicability and 4) Robustness.

Abstract

     The ongoing expansion of the Industrial Internet of Things (IIoT) is enabling the possibility of effective Industry 4.0, where massive sensing devices in heterogeneous environments are connected through dedicated communication protocols. This brings forth new methods and models to fuse the information yielded by the various industrial plant elements and generates emerging security challenges that we have to face, providing ad-hoc functions for scheduling and guaranteeing the network operations. Recently, the large development of Software-Defined Networking (SDN) and Artificial Intelligence (AI) technologies have made feasible the design and control of scalable and secure IIoT networks. This paper studies how AI and SDN technologies combined can be leveraged towards improving the security and functionality of these IIoT networks. After surveying the state-of-the-art research efforts in the subject, the paper introduces a candidate architecture for AI-enabled Software-Defined IIoT Network (AI-SDIN) that divides the traditional industrial networks into three functional layers. And with this aim in mind, key technologies (Blockchain-based Data Sharing, Intelligent Wireless Data Sensing, Edge Intelligence, Time-Sensitive Networks, Integrating SDN&TSN, Distributed AI) and improve applications based on AI-SDIN are also discussed. Further, the paper also highlights new opportunities and potential research challenges in control and automation of IIoT networks.

Introduction

     Currently, we are witnessing an industrial revolution ranging term as Industry 4.0. Although this term may not be fully correct and involves a certain level of hype, the generalized implementation of autonomous decision making systems has become a new industry per se. The industry 4.0 is built by integrating networking capabilities that interconnect Artificial Intelligence (AI) agents in industrial plants, leading to the paradigms of Industrial Internet of Things (IIoT) and smart manufacturing [1,2]. These subjects are attracting continuous attention from both academia and industry, and have been categorized in various prototype models [3,4].

     Fig. 1 displays a typical architecture for IIoT networks, integrating four functional layers: data sensing, data transfer, data processing, and application layers [5,6]. The data sensing layer utilizes information acquisition and sensing technologies to collect heterogeneous data with different industrial attributes, and uploads them to the data transfer layer through advanced communication technologies, e.g., WiFi, Bluetooth, Internet of Things (IoT), 5G, etc. [7,8]. As the intermediated layer, the data transfer layer (based on the Internet backbone) aims at uploading the industrial data to the data processing layer by deploying Traffic Engineering (TE) or data routing policies, according to the given requirements and constraints (e.g., Quality of Service (QoS), costs, lags, etc.) of the industrial data delivery services [9]. Based on the dedicated data computing schemes (e.g., cloud/edge computing, distributed computing), the data processing layer adopts data fusion algorithms to process the data in real-time, and it provides an open interface for the application layer, such that the diverse categories of industrial applications can be edited and deployed at the application layer according to the current needs [10].

Conclusion

     Although the paradigm of IIoT networks promotes the progress of smart manufacture towards achieving reduced costs and increased efficiency, IIoT networks present the constraints of network heterogeneity and scalability. Current network architectures integrate computing units of low-intelligence, which cannot support the scheduling for diverse and complex industrial flow – especially for case scenarios with delay-sensitivity requirements. In this paper, we argued that these issues can be addressed by utilizing an AI-enabled SDN technique, and we have presented a formal architecture named by AI-SDIN to achieve this vision. Within the presented architecture, the IIoT networks are divided into three functional layers: data layer, network layer, and control layer. In the latter, advanced machine intelligence can be deployed to improve the security and functionality of the network. Then, we have surveyed possible key technologies towards actual implementation – e.g., Blockchain-based data sharing, intelligent wireless data sensing, edge intelligence, TSN&SDN, distributed AI, etc. We have also presented potential enhanced industrial applications based on AI-SDIN ranging from logistics to agriculture. Finally, the paper has indicated challenges and areas where further research is required. Our paper aims at revealing that the AI-SDIN leads the future of smart manufacture to be clear and bright. With AI-SDIN, industrial manufacture will be supermatic with less manufacture time and intelligent logistics, supporting fast production and marketing.

Abstract

     Data storage for attendance at a company or agency is usually stored in a local or cloud database. This type of storage has several issues, such as privacy and data integrity, because several parties will fully regulate privacy and data integrity. Therefore, a data storage system is needed that can provide privacy, security, and data integrity to maintain the authenticity of sensitive data. This study wants to develop to implement blockchain technology to store employee attendance data from the HR department in a company. Employee attendance data is taken from the Angular web application integrated with a permissioned blockchain framework called Hyperledger Composer. This study chose Hyperledger Composer because this type of blockchain has a fast validation time. Hyperledger Composer has a Representational State Transfer Application Programming Interface (REST API) named composer-rest-server, which allows Hyperledger blockchain to interact with other components. The implementation results show that Hyperledger Composer can be functionally used as data storage for the attendance management and Payroll system. In addition, Hyperledger Composer performance is measured by evaluating block transaction times. The evaluation of the Hyperledger Composer is done in three ways: directly within the Hyperledger Composer, using the Angular web application through the REST API, and using JMeter through the REST API. As a result, testing for making transaction blocks varies from 1 – 17ms on an experiment directly in Hyperledger Composer, 5 – 296ms through the REST API using JMeter tools, and 1 – 4270ms through the Angular Web Application. The result shows that the performance of the REST API produced by the composer-rest-server is recorded faster than Ethereum. With these results, composer-rest-server can handle systems that require fast transaction times, such as voting systems, health monitoring, and the Internet of Things (IoT) application, because the average time to conduct transactions is still under one second. Thus, Hyperledger as attendance data storage can provide privacy and data integrity.

Introduction

     Blockchain is a distributed digital ledger technology that Nowadays, the development and use of technology are developing more rapidly in various fields, one of which is information technology. Information technology is a technology used to get, store, organize, process, and manipulate the amount of data to produce relevant and accurate information. The attendance management and payroll system are some of the information systems on financial activities required in a company or agency. The use of this information system can provide an accurate report of attendance records and payroll. The attendance management and payroll system is a system that processes the attendance information to determine the total salary of each employee. Attendance data is sensitive information that must be maintained for authenticity because it can affect the number of salaries to be paid.

Conclusion

     The blockchain attendance management and payroll system design can be well implemented. All functions of the Hyperledger Composer component and the Angular web application are running properly and according to the design. Hyperledger Composer can communicate with the other components through the REST API provided by the composer-rest-server. The REST API provided by composerrest-server performs well through JMeter with an average transaction time of 198ms and the total time required for 100 transactions of 1.98 seconds compared to Ethereum Blockchain. Blockchain technology can support the attendance and payroll system, so this system has advantages in data security and data integrity.