Human motion recognition is extremely important for many practical applications in several disciplines, such as surveillance, medicine, sports, gait analysis, and computer graphics. Graph convolutional networks (GCNs) enhance the accuracy and performance of skeleton-based action recognition. However, this approach has difficulties in modeling long-term temporal dependencies. In Addition, the fixed topology of the skeleton graph is not sufficiently robust to extract features for skeleton motions. Although transformers that rely entirely on self-attention have demonstrated great success in modeling global correlations between inputs and outputs, they ignore the local correlations between joints. In this study, we propose a novel segmented spatiotemporal skeleton graph-attention network (S3GAAR) to effectively learn different human actions and concentrate on the most operative part of the human body for each action. The proposed S3GAAR models spatial-temporal features through spatiotemporal attention for each segment to capture short-term temporal dependencies. Owing to several human actions that focus on one or more body parts such as mutual actions, our novel method divides the human skeleton into three segments: superior, inferior, and extremity joints. Our proposed method is designed to extract the features of each segment individually because human actions focus on one or more segments. Moreover, our segmented spatiotemporal graph introduces additional edges between important distant joints in the same segment. The experimental results show that our novel method outperforms state-of-the-art methods up to 1.1% on two large-scale benchmark datasets, NTU-RGB+D 60 and NTU-RGB+D 120.

The smart grid advanced metering infrastructure (AMI) is vulnerable to electricity theft cyber-attacks in which malicious smart meters report low readings to reduce the consumers’ bills. To avoid this problem, several machine-learning-based detectors have been proposed to detect electricity theft. Most of these detectors are global in the sense that they are trained on different consumption levels, including low and high consumptions, to be used for all consumers. In this article, we introduce a novel type of evasion attacks against global detectors as follows. A malicious consumer who has high consumption level can send false readings for a low-consumption profile (that resembles the profiles the detector is trained on) to evade the detector, i.e., steal electricity without being detected. We first conduct experiments to prove that the existing global detectors are vulnerable to this new kind of evasion attacks. To launch this attack, we train a generative adversarial network (GAN) on a real data set to generate fake low-consumption readings that can evade the detector. The given results indicate that the success rate of the attack is between 82% and 97%. To thwart this attack, we divide the consumers into clusters of close electricity consumption levels and train one detector for each cluster. Therefore, if a malicious consumer in any cluster tries to imitate the consumption profiles of consumers in other clusters, he/she will be detected. On the other hand, it is not profitable to imitate the electricity consumption profiles of consumers in his/her cluster to evade detection. To prove the effectiveness of our countermeasure, extensive experiments are conducted and the results indicate that our countermeasure can successfully thwart the attack.

Self-driving cars, believed to be on track to become a seven trillion-dollar industry by 2050, are likely to be a popular mode of transportation in the near future, as the technology promises safety and efficiency. Significant advances have been made to achieve these objectives. However, the idea of a self-driving car completing its goals without human intervention in the presence of uncertainties is still posed with challenges. These challenges mostly relate to decision-making under uncertainties that could arise from transient faults or deliberate attempts to disrupt the system in favor of adversarial motives. Traditional schemes of hardening the system against known compromises will not suffice primarily because of the sophistication adversarial attempts have come to achieve and the rate at which new attacks are being devised. The problem demands a solution that is resilient to attacks and that would allow a self-driving vehicle to complete its designated task in the presence of attacks or faults. Several attempts have been made towards achieving resilience in autonomous vehicles. To the best of our knowledge, this is the first work to give a thorough review on the resilient approaches adopted in the context and presents a taxonomy of such approaches. The paper also includes a brief introduction to the architecture of self-driving vehicles and its vulnerabilities in the presented context. This paper also discusses a novel approach using N-version programming for resilience.

Contact tracing is a very effective way to control the COVID-19-like pandemics. It aims to identify individuals who closely contacted an infected person during the incubation period of the virus and notify them to quarantine. However, the existing systems suffer from privacy, security, and efficiency issues. To address these limitations, in this article, we propose an efficient and privacy-preserving Blockchain-based infection control system. Instead of depending on a single authority to run the system, a group of health authorities, that form a consortium Blockchain, run our system. Using Blockchain technology not only secures our system against single point of failure and denial of service attacks, but also brings transparency because all transactions can be validated by different parties. Although contact tracing is important, it is not enough to effectively control an infection. Thus, unlike most of the existing systems that focus only on contact tracing, our system consists of three integrated subsystems, including contact tracing, public places access control, and safe-places recommendation. The access control subsystem prevents infected people from visiting public places to prevent spreading the virus, and the recommendation subsystem categorizes zones based on the infection level so that people can avoid visiting contaminated zones. Our analysis demonstrates that our system is secure and preserves the privacy of the users against identification, social graph disclosure, and tracking attacks, while thwarting false reporting (or panic) attacks. Moreover, our extensive performance evaluations demonstrate the scalability of our system (which is desirable in pandemics) due to its low communication, computation, and storage overheads.

In the smart grid, malicious customers may compromise their smart meters (SMs) to report false readings to achieve financial gains illegally. This causes hefty financial losses to the utility and may degrade the grid performance because the reported readings are used for energy management. This article is the first work that investigates this problem in the net-metering system, in which one SM is used to report the difference between the power consumed and the power generated. First, we prepare a benign data set for the net-metering system by processing a real power consumption and generation data set. Then, we propose a new set of attacks tailored for the net-metering system to create a malicious data set. After that, we analyzed the data and found time correlations between the net meter readings and correlations between the readings and relevant data obtained from trustworthy sources, such as solar irradiance and temperature. Based on the data analysis, we propose a general multidata-source deep hybrid learning-based detector to identify the false-reading attacks. Our detector is trained on net meter readings of all customers besides data from trustworthy sources to enhance the detector performance by learning the correlations between them. The rationale here is that although an attacker can report false readings, he cannot manipulate the solar irradiance and temperature values because they are beyond his control. Extensive experiments have been conducted, and the results indicate that our detector can identify the false-reading attacks with a high detection rate of 98.59% and a low false alarm of 2.92%.

The periodic transmission of the customers’ power consumption readings in the advanced metering infrastructure (AMI) is essential for energy management and billing. To collect the readings efficiently, the change and transmit approach is adopted in AMI (CAT AMI) so that the readings are reported only when there is enough change in the consumption. However, CAT AMI suffers from malicious customers who launch electricity-theft cyberattacks by manipulating their readings to illegally reduce their bills. These attacks can cause hefty financial losses and degrade the grid performance because the readings are used for grid management. In this article, the electricity-theft problem in CAT AMI networks is investigated. We first process a real power consumption readings data set to create a benign data set and propose a new set of cyberattacks to create malicious samples. We then develop a deep-learning-based electricity-theft detection solution to identify malicious customers for the CAT AMI network. The proposed detector uses both the customers’ transmission pattern and CAT readings to learn the correlation between them in order to enhance the detector’s ability in identifying electricity thefts. We conduct extensive experiments to evaluate the performance of our electricity-theft detector, and the results indicate that our detector can accurately detect malicious customers and achieve higher detection rate and lower false alarm than the detectors that are trained only on the CAT readings.

Cyber–physical systems (CPSs) have developed rapidly in recent years, contributing to an efficient integration between the cyber and physical worlds in intelligent and connected city environments. However, efficient mobility in a CPS is not well solved. Here, we present a prototype for a privacy-aware secure human-centric mobility-aware (SHM) model proposed and tested to analyze physical and human domains in IoT-based wireless sensor networks (WSNs). The proposed SHM model involves five modules: 1) sensor advertisements; 2) mobile sensor recruitment; 3) load balancing; 4) transmission guarantee; and 5) privacy with data-sharing phases. The proposed model is also validated using an accurate testing method that involves software and hardware tools and mathematical modeling to confirm secure communication. The model provides a tradeoff between energy efficiency and Quality-of-Service (QoS) requirements and compares the performance with other known models/protocols. Our testing process continued for four days, demonstrating that the SHM model provides compelling features of a secure CPS based on actual testing results. In practice, our model can be used in hospitals, as evident from validation in a real-life environment following the protocols.

Due to the emergence of heterogeneous Internet of Medical Things (IoMT) (e.g., wearable health devices, smartwatch monitoring, and automated insulin delivery systems), large volumes of patient data are dispatched to central cloud servers for disease analysis and diagnosis. Although this direct mode brings a lot of convenience for both patients and medical professionals (MPs), the open communication channel between them also incurs several security and privacy issues, such as man-in-the-middle attacks, eavesdropping attacks, and tracking attacks. Based on the unsolved challenges in wireless medical sensor networks (WMSNs), several researchers have proposed various authentication and key agreement (AKA) protocols for this type of healthcare system recently. However, most of these protocols do not perceive physical-layer security and over-centralized server problem in WMSN. In this article, to address these two open problems, we propose a lightweight and reliable authentication protocol for WMSN, which is composed of cutting-edge blockchain technology and physically unclonable functions (PUFs). In addition, a fuzzy extractor scheme is introduced to deal with biometric information. Subsequently, two security evaluation methods are used to prove the high reliability of our proposed scheme. Finally, performance evaluation experiments illustrate that the proposed mutual authentication protocol requires the least computation and communication cost among the compared schemes.

In advanced metering infrastructure, smart meters (SMs) send fine-grained power consumption readings periodically to the utility for load monitoring and energy management. Change and transmit (CAT) is an efficient approach to collect these readings, where the readings are not transmitted when there is no enough change in consumption. However, this approach causes a privacy problem, that is, by analyzing the transmission pattern of an SM, sensitive information on the house dwellers can be inferred. For instance, since the transmission pattern is distinguishable when dwellers are on travel, attackers may analyze the pattern to launch a presence-privacy attack (PPA) to infer whether the dwellers are absent from home. In this article, we propose a scheme, called “STDL,” for efficient collection of power consumption readings in advanced metering infrastructure (AMI) networks while preserving the consumers’ privacy by sending spoofing transmissions using a deep-learning approach. We first use a clustering technique and real power consumption readings to create a data set for transmission patterns using the CAT approach. Then, we train a deep-learning-based attacker model, and our evaluations indicate that the attacker’s success rate is about 91%. Finally, we train a deep-learning-based defense model to send spoofing transmissions efficiently to thwart the PPA. Extensive evaluations are conducted, and the results indicate that our scheme can reduce the attacker’s success rate to 3.15%, while still achieving high efficiency in terms of the number of readings that should be transmitted. Our measurements indicate that the proposed scheme can increase efficiency by about 41% compared to continuously transmitting readings.

In advanced metering infrastructure (AMI), smart meters (SMs) are installed at the consumer side to send fine-grained power consumption readings periodically to the system operator (SO) for load monitoring, energy management, and billing. However, fraudulent consumers launch electricity theft cyber attacks by reporting false readings to reduce their bills illegally. These attacks do not only cause financial losses but may also degrade the grid performance because the readings are used for grid management. To identify these attackers, the existing schemes employ machine-learning models using the consumers' fine-grained readings, which violates the consumers' privacy by revealing their lifestyle. In this article, we propose an efficient scheme that enables the SO to detect electricity theft, compute bills, and monitor load while preserving the consumers' privacy. The idea is that SMs encrypt their readings using functional encryption (FE), and the SO uses the ciphertexts to: 1) compute the bills following the dynamic pricing approach; 2) monitor the grid load; and 3) evaluate a machine-learning model to detect fraudulent consumers, without being able to learn the individual readings to preserve consumers' privacy. We adapted an FE scheme so that the encrypted readings are aggregated for billing and load monitoring and only the aggregated value is revealed to the SO. Also, we exploited the inner-product operations on encrypted readings to evaluate a machine-learning model to detect fraudulent consumers. The real data set is used to evaluate our scheme, and our evaluations indicate that our scheme is secure and can detect fraudulent consumers accurately with low communication and computation overhead.

Medical IoT devices are rapidly becoming part of management ecosystems for pandemics such as COVID-19. Existing research shows that deep learning (DL) algorithms have been successfully used by researchers to identify COVID-19 phenomena from raw data obtained from medical IoT devices. Some examples of IoT technology are radiological media, such as CT scanning and X-ray images, body temperature measurement using thermal cameras, safe social distancing identification using live face detection, and face mask detection from camera images. However, researchers have identified several security vulnerabilities in DL algorithms to adversarial perturbations. In this article, we have tested a number of COVID-19 diagnostic methods that rely on DL algorithms with relevant adversarial examples (AEs). Our test results show that DL models that do not consider defensive models against adversarial perturbations remain vulnerable to adversarial attacks. Finally, we present in detail the AE generation process, implementation of the attack model, and the perturbations of the existing DL-based COVID-19 diagnostic applications. We hope that this work will raise awareness of adversarial attacks and encourages others to safeguard DL models from attacks on healthcare systems.

In this paper, we introduce a translation tool that translates any sequential C++ source code into parallel source code written in C++ and OpenACC programming model. The tool generates different types of dependency graphs: class, method, loop, and block-of-statements graphs. The class and method dependency graphs are created for the sequential code, and the loop and block-of-statements dependency graphs are created for each method. From the class dependency graph, the dependency analyser identifies the independent classes, where the objects of the independent classes can run in parallel, and from the method dependency graph, the method analyser detects the methods that can run in parallel. For each block, the analyser detects the statements that run in parallel, where there is no data dependency between statements, and the loop analyser detects the type of parallelism in the loop-block: parallel statements, pipeline, or data partitions.

In this paper, seven greedy heuristics for construction of association rules are compared from the point of view of the length and coverage of constructed rules. The obtained rules are compared also with optimal ones constructed by dynamic programming algorithms. The average relative difference between length of rules constructed by the best heuristic and minimum length of rules is at most 4%. The same situation is with coverage.

Injection attacks remain yet one of the major challenges in non-relational data stores or NoSQL databases. Indeed, such databases are intended to store big data and are classified into four categories; Key-values Stores, Wide Column Stores, Document Stores, and Graph Databases. The different vulnerabilities of these NoSQL databases have attracted many researchers to attempt solving or mitigating this problem. Unfortunately, extensive experiments have revealed that all proposed approaches and techniques are away from the expectations. This is due mainly to their focusing only either on some parts of the problem or on a specific NoSQL engine. In this paper, we propose an open tool architecture which can take into consideration any NoSQL engines belonging to the four data stores categories whatever the programming language used. The proposed tool architecture is able to detect first vulnerable statements in the static mode on the developer side. Second, it detects also automatically injection attacks during run-time on the server side thanks to the added instrumenting statements during the first control (static mode). The easy expansion and adaptation of the proposed tool to any NoSQL engine and/or any kind of attacks and/or programming languages makes it very attractive compared the existing ones. Indeed nowadays, we observe the emergence of new kinds of attacks once a new security approach or framework or technique is proposed.

In the paper, an application of dynamic programming approach for optimization of association rules from the point of view of knowledge representation is considered. The association rule set is optimized in two stages, first for minimum cardinality and then for minimum length of rules. Experimental results present cardinality of the set of association rules constructed for information system and lower bound on minimum possible cardinality of rule set based on the information obtained during algorithm work as well as obtained results for length.

Knowledge representation and extraction are very important tasks in data mining. In this work, we proposed a variety of rule-based greedy algorithms that able to obtain knowledge contained in a given dataset as a series of inhibitory rules containing an expression “attribute ≠ value” on the right-hand side. The main goal of this paper is to determine based on rule characteristics, rule length and coverage, whether the proposed rule heuristics are statistically significantly different or not; if so, we aim to identify the best performing rule heuristics for minimization of rule length and maximization of rule coverage. Friedman test with Nemenyi post-hoc are used to compare the greedy algorithms statistically against each other for length and coverage. The experiments are carried out on real datasets from UCI Machine Learning Repository. For leading heuristics, the constructed rules are compared with optimal ones obtained based on dynamic programming approach. The results seem to be promising for the best heuristics: the average relative difference between length (coverage) of constructed and optimal rules is at most 2.27% (7%, respectively). Furthermore, the quality of classifiers based on sets of inhibitory rules constructed by the considered heuristics are compared against each other, and the results show that the three best heuristics from the point of view classification accuracy coincides with the three well-performed heuristics from the point of view of rule length minimization.

In this work, we consider so-called nonredundant inhibitory rules, containing an expression “attribute:F value” on the right- hand side, for which the number of misclassifications is at most a threshold γ. We study a dynamic programming approach for description of the considered set of rules. This approach allows also the optimization of nonredundant inhibitory rules relative to the length and coverage. The aim of this paper is to investigate an additional possibility of optimization relative to the number of misclassifications. The results of experiments with decision tables from the UCI Machine Learning Repository show this additional optimization achieves a fewer misclassifications. Thus, the proposed optimization procedure is promising.