496 research outputs found

    Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

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    Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

    Performance Analysis Of Data-Driven Algorithms In Detecting Intrusions On Smart Grid

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    The traditional power grid is no longer a practical solution for power delivery due to several shortcomings, including chronic blackouts, energy storage issues, high cost of assets, and high carbon emissions. Therefore, there is a serious need for better, cheaper, and cleaner power grid technology that addresses the limitations of traditional power grids. A smart grid is a holistic solution to these issues that consists of a variety of operations and energy measures. This technology can deliver energy to end-users through a two-way flow of communication. It is expected to generate reliable, efficient, and clean power by integrating multiple technologies. It promises reliability, improved functionality, and economical means of power transmission and distribution. This technology also decreases greenhouse emissions by transferring clean, affordable, and efficient energy to users. Smart grid provides several benefits, such as increasing grid resilience, self-healing, and improving system performance. Despite these benefits, this network has been the target of a number of cyber-attacks that violate the availability, integrity, confidentiality, and accountability of the network. For instance, in 2021, a cyber-attack targeted a U.S. power system that shut down the power grid, leaving approximately 100,000 people without power. Another threat on U.S. Smart Grids happened in March 2018 which targeted multiple nuclear power plants and water equipment. These instances represent the obvious reasons why a high level of security approaches is needed in Smart Grids to detect and mitigate sophisticated cyber-attacks. For this purpose, the US National Electric Sector Cybersecurity Organization and the Department of Energy have joined their efforts with other federal agencies, including the Cybersecurity for Energy Delivery Systems and the Federal Energy Regulatory Commission, to investigate the security risks of smart grid networks. Their investigation shows that smart grid requires reliable solutions to defend and prevent cyber-attacks and vulnerability issues. This investigation also shows that with the emerging technologies, including 5G and 6G, smart grid may become more vulnerable to multistage cyber-attacks. A number of studies have been done to identify, detect, and investigate the vulnerabilities of smart grid networks. However, the existing techniques have fundamental limitations, such as low detection rates, high rates of false positives, high rates of misdetection, data poisoning, data quality and processing, lack of scalability, and issues regarding handling huge volumes of data. Therefore, these techniques cannot ensure safe, efficient, and dependable communication for smart grid networks. Therefore, the goal of this dissertation is to investigate the efficiency of machine learning in detecting cyber-attacks on smart grids. The proposed methods are based on supervised, unsupervised machine and deep learning, reinforcement learning, and online learning models. These models have to be trained, tested, and validated, using a reliable dataset. In this dissertation, CICDDoS 2019 was used to train, test, and validate the efficiency of the proposed models. The results show that, for supervised machine learning models, the ensemble models outperform other traditional models. Among the deep learning models, densely neural network family provides satisfactory results for detecting and classifying intrusions on smart grid. Among unsupervised models, variational auto-encoder, provides the highest performance compared to the other unsupervised models. In reinforcement learning, the proposed Capsule Q-learning provides higher detection and lower misdetection rates, compared to the other model in literature. In online learning, the Online Sequential Euclidean Distance Routing Capsule Network model provides significantly better results in detecting intrusion attacks on smart grid, compared to the other deep online models

    Deep Learning Techniques for Power System Operation: Modeling and Implementation

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    The fast development of the deep learning (DL) techniques in the most recent years has drawn attention from both academia and industry. And there have been increasing applications of the DL techniques in many complex real-world situations, including computer vision, medical diagnosis, and natural language processing. The great power and flexibility of DL can be attributed to its hierarchical learning structure that automatically extract features from mass amounts of data. In addition, DL applies an end-to-end solving mechanism, and directly generates the output from the input, where the traditional machine learning methods usually break down the problem and combine the results. The end-to-end mechanism considerably improve the computational efficiency of the DL.The power system is one of the most complex artificial infrastructures, and many power system control and operation problems share the same features as the above mentioned real-world applications, such as time variability and uncertainty, partial observability, which impedes the performance of the conventional model-based methods. On the other hand, with the wide spread implementation of Advanced Metering Infrastructures (AMI), the SCADA, the Wide Area Monitoring Systems (WAMS), and many other measuring system providing massive data from the field, the data-driven deep learning technique is becoming an intriguing alternative method to enable the future development and success of the smart grid. This dissertation aims to explore the potential of utilizing the deep-learning-based approaches to solve a broad range of power system modeling and operation problems. First, a comprehensive literature review is conducted to summarize the existing applications of deep learning techniques in power system area. Second, the prospective application of deep learning techniques in several scenarios in power systems, including contingency screening, cascading outage search, multi-microgrid energy management, residential HVAC system control, and electricity market bidding are discussed in detail in the following 2-6 chapters. The problem formulation, the specific deep learning approaches in use, and the simulation results are all presented, and also compared with the currently used model-based method as a verification of the advantage of deep learning. Finally, the conclusions are provided in the last chapter, as well as the directions for future researches. It’s hoped that this dissertation can work as a single spark of fire to enlighten more innovative ideas and original studies, widening and deepening the application of deep learning technique in the field of power system, and eventually bring some positive impacts to the real-world bulk grid resilient and economic control and operation

    Critical Infrastructure Automated Immuno-Response System (CIAIRS)

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    Critical Infrastructures play a central role in the world around us and are the backbone of everyday life. Their service provision has become more widespread, to the point where it is now practically ubiquitous in many societies. Critical Infrastructure assets contribute to the economy and society as a whole. Their impact on the security, economy and health sector are extremely vital. Critical Infrastructures now possess levels of automation that require the integration of, often, mutually incompatible technologies. Their increasing complexity has led to the creation of direct and indirect interdependent connections amongst the infrastructure groupings. In addition, the data generated is vast as the intricate level of interdependency between infrastructures has grown. Since Critical Infrastructures are the backbone of everyday life, their protection from cyber-threats is an increasingly pressing issue for governments and private industries. Any failures, caused by cyber-attacks, have the ability to spread through interconnected systems and are a challenge to detect; especially as the Internet is now heavily reliant on Critical Infrastructures. This has led to different security threats facing interconnected security systems. Understanding the complexity of Critical Infrastructure interdependencies, how to take advantage of it in order to minimize the cascading problem, enables the prediction of potential problems before they happen. Therefore, this work firstly discusses the interdependency challenges facing Critical Infrastructures; and how it can be used to create a support network against cyber-attacks. In much, the same way as the human immune system is able to respond to intrusion. Next, the development of a distributed support system is presented. The system employs behaviour analysis techniques to support interconnected infrastructures and distribute security advice throughout a distributed system of systems. The approach put forward is tested through a statistical analysis methodology, in order to investigate the cascading failure effect whilst taking into account the independent variables. Moreover, our proposed system is able to detect cyber-attacks and share the knowledge with interconnected partners to create an immune system network. The development of the ‘Critical Infrastructure Auto-Immune Response System’ (CIAIRS) is presented with a detailed discussion on the main segments that comprise the framework and illustrates the functioning of the system. A semi-structured interview helped to demonstrate our approach by using a realistic simulation to construct data and evaluate the system output

    Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

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    The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

    Self-Healing in Cyber–Physical Systems Using Machine Learning:A Critical Analysis of Theories and Tools

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    The rapid advancement of networking, computing, sensing, and control systems has introduced a wide range of cyber threats, including those from new devices deployed during the development of scenarios. With recent advancements in automobiles, medical devices, smart industrial systems, and other technologies, system failures resulting from external attacks or internal process malfunctions are increasingly common. Restoring the system’s stable state requires autonomous intervention through the self-healing process to maintain service quality. This paper, therefore, aims to analyse state of the art and identify where self-healing using machine learning can be applied to cyber–physical systems to enhance security and prevent failures within the system. The paper describes three key components of self-healing functionality in computer systems: anomaly detection, fault alert, and fault auto-remediation. The significance of these components is that self-healing functionality cannot be practical without considering all three. Understanding the self-healing theories that form the guiding principles for implementing these functionalities with real-life implications is crucial. There are strong indications that self-healing functionality in the cyber–physical system is an emerging area of research that holds great promise for the future of computing technology. It has the potential to provide seamless self-organising and self-restoration functionality to cyber–physical systems, leading to increased security of systems and improved user experience. For instance, a functional self-healing system implemented on a power grid will react autonomously when a threat or fault occurs, without requiring human intervention to restore power to communities and preserve critical services after power outages or defects. This paper presents the existing vulnerabilities, threats, and challenges and critically analyses the current self-healing theories and methods that use machine learning for cyber–physical systems

    CPS Attacks Mitigation Approaches on Power Electronic Systems with Security Challenges for Smart Grid Applications: A Review

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    This paper presents an inclusive review of the cyber-physical (CP) attacks, vulnerabilities, mitigation approaches on the power electronics and the security challenges for the smart grid applications. With the rapid evolution of the physical systems in the power electronics applications for interfacing renewable energy sources that incorporate with cyber frameworks, the cyber threats have a critical impact on the smart grid performance. Due to the existence of electronic devices in the smart grid applications, which are interconnected through communication networks, these networks may be subjected to severe cyber-attacks by hackers. If this occurs, the digital controllers can be physically isolated from the control loop. Therefore, the cyber-physical systems (CPSs) in the power electronic systems employed in the smart grid need special treatment and security. In this paper, an overview of the power electronics systems security on the networked smart grid from the CP perception, as well as then emphases on prominent CP attack patterns with substantial influence on the power electronics components operation along with analogous defense solutions. Furthermore, appraisal of the CPS threats attacks mitigation approaches, and encounters along the smart grid applications are discussed. Finally, the paper concludes with upcoming trends and challenges in CP security in the smart grid applications

    Enhancing Bio-inspired Intrusion Response in Ad-hoc Networks

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    Practical applications of Ad-hoc networks are developing everyday and safeguarding their security is becoming more important. Because of their specific qualities, ad-hoc networks require an anomaly detection system that adapts to its changing behaviour quickly. Bio-inspired algorithms provide dynamic, adaptive, real-time methods of intrusion detection and particularly in initiating a response. A key component of bio-inspired response methods is the use of feedback from the network to better adapt their response to the specific attack and the type of network at hand. However, calculating an appropriate length of time at which to provide feedback is crucial - premature feedback or delayed feedback from the network can have adverse effects on the attack mitigation process. The antigen-degeneracy response selection algorithm (Schaust & Szczerbicka, 2011) is one of the few bio-inspired algorithms for selecting the appropriate response for misbehavior that considers network performance and adapts to the network. The main drawback of this algorithm is that it has no measure of the amount of time to wait before it can take performance measurements (feedback) from the network. In this thesis, we attempt to develop an understanding of the length of time required before feedback is provided in a range of types of ad-hoc network that have been subject of an attack, in order that future development of bio-inspired intrusion detection algorithms can be enhanced.Aiming toward an adaptive timer, we discuss that ad-hoc networks can be divided into Wireless Sensor Network (WSN), Wireless Personal Area Network (WPAN) and Spontaneously Networked Users (SNU). We use ns2 to simulate these three different types of ad-hoc networks, each of which is analysed for changes in its throughput after an attack is responded to, in order to calculate the corresponding feedback time. The feedback time in this case is the time it takes for the network to stabilise. Feedback time is not only essential to bio-inspired intrusion response methods, but can also be used in network applications where a stable network reading is required, e.g. security monitoring and motion tracking.Interestingly, we found that the network feedback time does not vary greatly between the different types of networks, but it was calculated to be less than half of what Schaust and Szczerbicka used in their algorith
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