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Zanin, M., Menasalvas, E., González, A. Rodriguez, Smrz, P..  2020.  An Analytics Toolbox for Cyber-Physical Systems Data Analysis: Requirements and Challenges. 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO). :271–276.
The fast improvement in telecommunication technologies that has characterised the last decade is enabling a revolution centred on Cyber-Physical Systems (CPSs). Elements inside cities, from vehicles to cars, can now be connected and share data, describing both our environment and our behaviours. These data can also be used in an active way, by becoming the tenet of innovative services and products, i.e. of Cyber-Physical Products (CPPs). Still, having data is not tantamount to having knowledge, and an important overlooked topic is how should them be analysed. In this contribution we tackle the issue of the development of an analytics toolbox for processing CPS data. Specifically, we review and quantify the main requirements that should be fulfilled, both functional (e.g. flexibility or dependability) and technical (e.g. scalability, response time, etc.). We further propose an initial set of analysis that should in it be included. We finally review some challenges and open issues, including how security and privacy could be tackled by emerging new technologies.
Fernando, Praveen, Wei, Jin.  2020.  Blockchain-Powered Software Defined Network-Enabled Networking Infrastructure for Cloud Management. 2020 IEEE 17th Annual Consumer Communications Networking Conference (CCNC). :1–6.
Cloud architecture has become a valuable solution for different applications, such as big data analytics, due to its high degree of availability, scalability and strategic value. However, there still remain challenges in managing cloud architecture, in areas such as cloud security. In this paper, we exploit software-defined networking (SDN) and blockchain technologies to secure cloud management platforms from a networking perspective. We develop a blockchain-powered SDN-enabled networking infrastructure in which the integration between blockchain-based security and autonomy management layer and multi-controller SDN networking layer is defined to enhance the integrity of the control and management messages. Furthermore, our proposed networking infrastructure also enables the autonomous bandwidth provisioning to enhance the availability of cloud architecture. In the simulation section, we evaluate the performance of our proposed blockchain-powered SDN-enabled networking infrastructure by considering different scenarios.
Niu, L., Ramasubramanian, B., Clark, A., Bushnell, L., Poovendran, R..  2020.  Control Synthesis for Cyber-Physical Systems to Satisfy Metric Interval Temporal Logic Objectives under Timing and Actuator Attacks*. 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS). :162–173.
This paper studies the synthesis of controllers for cyber-physical systems (CPSs) that are required to carry out complex tasks that are time-sensitive, in the presence of an adversary. The task is specified as a formula in metric interval temporal logic (MITL). The adversary is assumed to have the ability to tamper with the control input to the CPS and also manipulate timing information perceived by the CPS. In order to model the interaction between the CPS and the adversary, and also the effect of these two classes of attacks, we define an entity called a durational stochastic game (DSG). DSGs probabilistically capture transitions between states in the environment, and also the time taken for these transitions. With the policy of the defender represented as a finite state controller (FSC), we present a value-iteration based algorithm that computes an FSC that maximizes the probability of satisfying the MITL specification under the two classes of attacks. A numerical case-study on a signalized traffic network is presented to illustrate our results.
Zhang, Xing, Cui, Xiaotong, Cheng, Kefei, Zhang, Liang.  2020.  A Convolutional Encoder Network for Intrusion Detection in Controller Area Networks. 2020 16th International Conference on Computational Intelligence and Security (CIS). :366–369.
Integrated with various electronic control units (ECUs), vehicles are becoming more intelligent with the assistance of essential connections. However, the interaction with the outside world raises great concerns on cyber-attacks. As a main standard for in-vehicle network, Controller Area Network (CAN) does not have any built-in security mechanisms to guarantee a secure communication. This increases risks of denial of service, remote control attacks by an attacker, posing serious threats to underlying vehicles, property and human lives. As a result, it is urgent to develop an effective in-vehicle network intrusion detection system (IDS) for better security. In this paper, we propose a Feature-based Sliding Window (FSW) to extract the feature of CAN Data Field and CAN IDs. Then we construct a convolutional encoder network (CEN) to detect network intrusion of CAN networks. The proposed FSW-CEN method is evaluated on real-world datasets. The experimental results show that compared to traditional data processing methods and convolutional neural networks, our method is able to detect attacks with a higher accuracy in terms of detection accuracy and false negative rate.
Venkataramanan, Venkatesh, Hahn, Adam, Srivastava, Anurag.  2020.  CP-SAM: Cyber-Physical Security Assessment Metric for Monitoring Microgrid Resiliency. IEEE Transactions on Smart Grid. 11:1055–1065.
Trustworthy and secure operation of the cyber-power system calls for resilience against malicious and accidental failures. The objective of a resilient system is to withstand and recover operation of the system to supply critical loads despite multiple contingencies in the system. To take timely actions, we need to continuously measure the cyberphysical security of the system. We propose a cyber-physical security assessment metric (CP-SAM) based on quantitative factors affecting resiliency and utilizing concepts from graph theoretic analysis, probabilistic model of availability, attack graph metrics, and vulnerabilities across different layers of the microgrid system. These factors are integrated into a single metric using a multi-criteria decision making (MCDM) technique, Choquet Integral to compute CP-SAM. The developed metric will be valuable for i) monitoring the microgrid resiliency considering a holistic cyber-physical model; and ii) enable better decision-making to select best possible mitigation strategies towards resilient microgrid system. Developed CP-SAM can be extended for active distribution system and has been validated in a real-world power-grid test-bed to monitor the microgrid resiliency.
Conference Name: IEEE Transactions on Smart Grid
Jin, Z., Yu, P., Guo, S. Y., Feng, L., Zhou, F., Tao, M., Li, W., Qiu, X., Shi, L..  2020.  Cyber-Physical Risk Driven Routing Planning with Deep Reinforcement-Learning in Smart Grid Communication Networks. 2020 International Wireless Communications and Mobile Computing (IWCMC). :1278—1283.
In modern grid systems which is a typical cyber-physical System (CPS), information space and physical space are closely related. Once the communication link is interrupted, it will make a great damage to the power system. If the service path is too concentrated, the risk will be greatly increased. In order to solve this problem, this paper constructs a route planning algorithm that combines node load pressure, link load balance and service delay risk. At present, the existing intelligent algorithms are easy to fall into the local optimal value, so we chooses the deep reinforcement learning algorithm (DRL). Firstly, we build a risk assessment model. The node risk assessment index is established by using the node load pressure, and then the link risk assessment index is established by using the average service communication delay and link balance degree. The route planning problem is then solved by a route planning algorithm based on DRL. Finally, experiments are carried out in a simulation scenario of a power grid system. The results show that our method can find a lower risk path than the original Dijkstra algorithm and the Constraint-Dijkstra algorithm.
Houzé, É, Diaconescu, A., Dessalles, J.-L., Mengay, D., Schumann, M..  2020.  A Decentralized Approach to Explanatory Artificial Intelligence for Autonomic Systems. 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C). :115–120.
While Explanatory AI (XAI) is attracting increasing interest from academic research, most AI-based solutions still rely on black box methods. This is unsuitable for certain domains, such as smart homes, where transparency is key to gaining user trust and solution adoption. Moreover, smart homes are challenging environments for XAI, as they are decentralized systems that undergo runtime changes. We aim to develop an XAI solution for addressing problems that an autonomic management system either could not resolve or resolved in a surprising manner. This implies situations where the current state of affairs is not what the user expected, hence requiring an explanation. The objective is to solve the apparent conflict between expectation and observation through understandable logical steps, thus generating an argumentative dialogue. While focusing on the smart home domain, our approach is intended to be generic and transferable to other cyber-physical systems offering similar challenges. This position paper focuses on proposing a decentralized algorithm, called D-CAN, and its corresponding generic decentralized architecture. This approach is particularly suited for SISSY systems, as it enables XAI functions to be extended and updated when devices join and leave the managed system dynamically. We illustrate our proposal via several representative case studies from the smart home domain.
Javorník, M., Komárková, J., Sadlek, L., Husak, M..  2020.  Decision Support for Mission-Centric Network Security Management. NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium. :1–6.
In this paper, we propose a decision support process that is designed to help network and security operators in understanding the complexity of a current security situation and decision making concerning ongoing cyber-attacks and threats. The process focuses on enterprise missions and uses a graph-based mission decomposition model that captures the missions, underlying hosts and services in the network, and functional and security requirements between them. Knowing the vulnerabilities and attacker's position in the network, the process employs logical attack graphs and Bayesian network to infer the probability of the disruption of the confidentiality, integrity, and availability of the missions. Based on the probabilities of disruptions, the process suggests the most resilient mission configuration that would withstand the current security situation.
Sekar, K., Devi, K. Suganya, Srinivasan, P., SenthilKumar, V. M..  2020.  Deep Wavelet Architecture for Compressive sensing Recovery. 2020 Seventh International Conference on Information Technology Trends (ITT). :185–189.
The deep learning-based compressive Sensing (CS) has shown substantial improved performance and in run-time reduction with signal sampling and reconstruction. In most cases, moreover, these techniques suffer from disrupting artefacts or high-frequency contents at low sampling ratios. Similarly, this occurs in the multi-resolution sampling method, which further collects more components with lower frequencies. A promising innovation combining CS with convolutionary neural network has eliminated the sparsity constraint yet recovery persists slow. We propose a Deep wavelet based compressive sensing with multi-resolution framework provides better improvement in reconstruction as well as run time. The proposed model demonstrates outstanding quality on test functions over previous approaches.
Moormann, L., Mortel-Fronczak, J. M. van de, Fokkink, W. J., Rooda, J. E..  2020.  Exploiting Symmetry in Dependency Graphs for Model Reduction in Supervisor Synthesis. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). :659–666.
Supervisor synthesis enables the design of supervisory controllers for large cyber-physical systems, with high guarantees for functionality and safety. The complexity of the synthesis problem, however, increases exponentially with the number of system components in the cyber-physical system and the number of models of this system, often resulting in lengthy or even unsolvable synthesis procedures. In this paper, a new method is proposed for reducing the model of the system before synthesis to decrease the required computational time and effort. The method consists of three steps for model reduction, that are mainly based on symmetry in dependency graphs of the system. Dependency graphs visualize the components in the system and the relations between these components. The proposed method is applied in a case study on the design of a supervisory controller for a road tunnel. In this case study, the model reduction steps are described, and results are shown on the effectiveness of model reduction in terms of model size and synthesis time.
Cheng, Xiuzhen, Chellappan, Sriram, Cheng, Wei, Sahin, Gokhan.  2020.  Guest Editorial Introduction to the Special Section on Network Science for High-Confidence Cyber-Physical Systems. IEEE Transactions on Network Science and Engineering. 7:764–765.
The papers in this special section focus on network science for high confidence cyber-physical systems (CPS) Here CPS refers to the engineered systems that can seamlessly integrate the physical world with the cyber world via advanced computation and communication capabilities. To enable high-confidence CPS for achieving better benefits as well as supporting emerging applications, network science-based theories and methodologies are needed to cope with the ever-growing complexity of smart CPS, to predict the system behaviors, and to model the deep inter-dependencies among CPS and the natural world. The major objective of this special section is to exploit various network science techniques such as modeling, analysis, mining, visualization, and optimization to advance the science of supporting high-confidence CPS for greater assurances of security, safety, scalability, efficiency, and reliability. These papers bring a timely and important research topic. The challenges and opportunities of applying network science approaches to high-confidence CPS are profound and far-reaching.
Conference Name: IEEE Transactions on Network Science and Engineering
Sunny, Jerin, Sankaran, Sriram, Saraswat, Vishal.  2020.  A Hybrid Approach for Fast Anomaly Detection in Controller Area Networks. 2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). :1–6.
Recent advancements in the field of in-vehicle network and wireless communication, has been steadily progressing. Also, the advent of technologies such as Vehicular Adhoc Networks (VANET) and Intelligent Transportation System (ITS), has transformed modern automobiles into a sophisticated cyber-physical system rather than just a isolated mechanical device. Modern automobiles rely on many electronic control units communicating over the Controller Area Network (CAN) bus. Although protecting the car's external interfaces is an vital part of preventing attacks, detecting malicious activity on the CAN bus is an effective second line of defense against attacks. This paper proposes a hybrid anomaly detection system for CAN bus based on patterns of recurring messages and time interval of messages. The proposed method does not require modifications in CAN bus. The proposed system is evaluated on real CAN bus traffic with simulated attack scenarios. Results obtained show that our proposed system achieved a good detection rate with fast response times.
Khoury, J., Nassar, M..  2020.  A Hybrid Game Theory and Reinforcement Learning Approach for Cyber-Physical Systems Security. NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium. :1—9.
Cyber-Physical Systems (CPS) are monitored and controlled by Supervisory Control and Data Acquisition (SCADA) systems that use advanced computing, sensors, control systems, and communication networks. At first, CPS and SCADA systems were protected and secured by isolation. However, with recent industrial technology advances, the increased connectivity of CPSs and SCADA systems to enterprise networks has uncovered them to new cybersecurity threats and made them a primary target for cyber-attacks with the potential of causing catastrophic economic, social, and environmental damage. Recent research focuses on new methodologies for risk modeling and assessment using game theory and reinforcement learning. This paperwork proposes to frame CPS security on two different levels, strategic and battlefield, by meeting ideas from game theory and Multi-Agent Reinforcement Learning (MARL). The strategic level is modeled as imperfect information, extensive form game. Here, the human administrator and the malware author decide on the strategies of defense and attack, respectively. At the battlefield level, strategies are implemented by machine learning agents that derive optimal policies for run-time decisions. The outcomes of these policies manifest as the utility at a higher level, where we aim to reach a Nash Equilibrium (NE) in favor of the defender. We simulate the scenario of a virus spreading in the context of a CPS network. We present experiments using the MiniCPS simulator and the OpenAI Gym toolkit and discuss the results.
Kalkan, Soner Can, Sahingoz, Ozgur Koray.  2020.  In-Vehicle Intrusion Detection System on Controller Area Network with Machine Learning Models. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–6.
Parallel with the developing world, transportation technologies have started to expand and change significantly year by year. This change brings with it some inevitable problems. Increasing human population and growing transportation-needs result many accidents in urban and rural areas, and this recursively results extra traffic problems and fuel consumption. It is obvious that the issues brought by this spiral loop needed to be solved with the use of some new technological achievements. In this context, self-driving cars or automated vehicles concepts are seen as a good solution. However, this also brings some additional problems with it. Currently many cars are provided with some digital security systems, which are examined in two phases, internal and external. These systems are constructed in the car by using some type of embedded system (such as the Controller Area Network (CAN)) which are needed to be protected form outsider cyberattacks. These attack can be detected by several ways such as rule based system, anomaly based systems, list based systems, etc. The current literature showed that researchers focused on the use of some artificial intelligence techniques for the detection of this type of attack. In this study, an intrusion detection system based on machine learning is proposed for the CAN security, which is the in-vehicle communication structure. As a result of the study, it has been observed that the decision tree-based ensemble learning models results the best performance in the tested models. Additionally, all models have a very good accuracy levels.
K, S., Devi, K. Suganya, Srinivasan, P., Dheepa, T., Arpita, B., singh, L. Dolendro.  2020.  Joint Correlated Compressive Sensing based on Predictive Data Recovery in WSNs. 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). :1–5.
Data sampling is critical process for energy constrained Wireless Sensor Networks. In this article, we proposed a Predictive Data Recovery Compressive Sensing (PDR-CS) procedure for data sampling. PDR-CS samples data measurements from the monitoring field on the basis of spatial and temporal correlation and sparse measurements recovered at the Sink. Our proposed algorithm, PDR-CS extends the iterative re-weighted -ℓ1(IRW - ℓ1) minimization and regularization on the top of Spatio-temporal compressibility for enhancing accuracy of signal recovery and reducing the energy consumption. The simulation study shows that from the less number of samples are enough to recover the signal. And also compared with the other compressive sensing procedures, PDR-CS works with less time.
Lenard, Teri, Bolboacă, Roland, Genge, Bela.  2020.  LOKI: A Lightweight Cryptographic Key Distribution Protocol for Controller Area Networks. 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP). :513–519.
The recent advancement in the automotive sector has led to a technological explosion. As a result, the modern car provides a wide range of features supported by state of the art hardware and software. Unfortunately, while this is the case of most major components, in the same vehicle we find dozens of sensors and sub-systems built over legacy hardware and software with limited computational capabilities. This paper presents LOKI, a lightweight cryptographic key distribution scheme applicable in the case of the classical invehicle communication systems. The LOKI protocol stands out compared to already proposed protocols in the literature due to its ability to use only a single broadcast message to initiate the generation of a new cryptographic key across a group of nodes. It's lightweight key derivation algorithm takes advantage of a reverse hash chain traversal algorithm to generate fresh session keys. Experimental results consisting of a laboratory-scale system based on Vector Informatik's CANoe simulation environment demonstrate the effectiveness of the developed methodology and its seamless impact manifested on the network.
Mahamat, A. D., Ali, A., Tanguier, J. L., Donnot, A., Benelmir, R..  2020.  Mechanical and thermophysical characterization of local clay-based building materials. 2020 5th International Conference on Renewable Energies for Developing Countries (REDEC). :1–6.
The work we present is a comparative study based on an experimental approach to the mechanical and thermal properties of different local clay-based building materials with the incorporation of agricultural waste in Chad. These local building materials have been used since ancient times by the low-income population. They were the subject of a detailed characterization of their mechanical and thermal parameters. The objective is to obtain lightweight materials with good thermomechanical performance and which can contribute to improving thermal comfort, energy-saving, and security in social housing in Chad while reducing the cost of investment. Several clay-based samples with increasing incorporation of 0 to 8% of agricultural waste (cow dung or millet pod) were made. We used appropriate experimental methods for porous materials (the hydraulic press for mechanical tests and the box method for thermal tests). In this article, we have highlighted the values and variations of the mechanical compressive resistances, thermal conductivities, and thermal resistances of test pieces made with these materials. Knowing the mechanical and thermal characteristics, we also carried out a thermomechanical study. The thermal data made it possible to make Dynamic Thermal Simulations (STD) of the buildings thanks to the Pléiades + COMFIE software. The results obtained show that the use of these materials in a building presents good mechanical and thermal performance with low consumption of electrical energy for better thermal comfort of the occupants. Thus agricultural waste can be recovered thanks to its integration into building materials based on clay.
Lenard, Teri, Bolboacă, Roland, Genge, Bela, Haller, Piroska.  2020.  MixCAN: Mixed and Backward-Compatible Data Authentication Scheme for Controller Area Networks. 2020 IFIP Networking Conference (Networking). :395–403.
The massive proliferation of state of the art interfaces into the automotive sector has triggered a revolution in terms of the technological ecosystem that is found in today's modern car. Accordingly, on the one hand, we find dozens of Electronic Control Units (ECUs) running several hundred MB of code, and more and more sophisticated dashboards with integrated wireless communications. On the other hand, in the same vehicle we find the underlying communication infrastructure struggling to keep up with the pace of these radical changes. This paper presents MixCAN (MIXed data authentication for Control Area Networks), an approach for mixing different message signatures (i.e., authentication tags) in order to reduce the overhead of Controller Area Network (CAN) communications. MixCAN leverages the attributes of Bloom Filters in order to ensure that an ECU can sign messages with different CAN identifiers (i.e., mix different message signatures), and that other ECUs can verify the signature for a subset of monitored CAN identifiers. Extensive experimental results based on Vectors Informatik's CANoe/CANalyzer simulation environment and the data set provided by Hacking and Countermeasure Research Lab (HCRL) confirm the validity and applicability of the developed approach. Subsequent experiments including a test bed consisting of Raspberry Pi 3 Model B+ systems equipped with CAN communication modules demonstrate the practical integration of MixCAN in real automotive systems.
Kwasinski, A..  2020.  Modeling of Cyber-Physical Intra-Dependencies in Electric Power Grids and Their Effect on Resilience. 2020 8th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems. :1–6.
This paper studies the modeling of cyber-physical dependencies observed within power grids and the effects of these intra-dependencies, on power grid resilience, which is evaluated quantitatively. A fundamental contribution of this paper is the description of the critically important role played by cyber-physical buffers as key components to limit the negative effect of intra-dependencies on power grids resilience. Although resilience issues in the electric power provision service could be limited thanks to the use of local energy storage devices as the realization of service buffers, minimal to no autonomy in data connectivity buffers make cyber vulnerabilities specially critical in terms of resilience. This paper also explains how these models can be used for improved power grids resilience planning considering internal cyber-physical interactions.
Pelissero, N., Laso, P. M., Puentes, J..  2020.  Naval cyber-physical anomaly propagation analysis based on a quality assessed graph. 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). :1–8.
As any other infrastructure relying on cyber-physical systems (CPS), naval CPS are highly interconnected and collect considerable data streams, on which depend multiple command and navigation decisions. Being a data-driven decision system requiring optimized supervisory control on a permanent basis, it is critical to examine the CPS vulnerability to anomalies and their propagation. This paper presents an approach to detect CPS anomalies and estimate their propagation applying a quality assessed graph, which represents the CPS physical and digital subsystems, combined with system variables dependencies and a set of data and information quality measures vectors. Following the identification of variables dependencies and high-risk nodes in the CPS, data and information quality measures reveal how system variables are modified when an anomaly is detected, also indicating its propagation path. Taking as reference the normal state of a naval propulsion management system, four anomalies in the form of cyber-attacks - port scan, programmable logical controller stop, and man in the middle to change the motor speed and operation of a tank valve - were produced. Three anomalies were properly detected and their propagation path identified. These results suggest the feasibility of anomaly detection and estimation of propagation estimation in CPS, applying data and information quality analysis to a system graph.
Balestrieri, E., Vito, L. De, Picariello, F., Rapuano, S., Tudosa, I..  2020.  A Novel CS-based Measurement Method for Impairments Identification in Wireline Channels. 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). :1–6.
The paper proposes a new measurement method for impairments identification in wireline channels (i.e. wire cables) by exploiting a Compressive Sampling (CS)-based technique. The method consists of two-phases: (i) acquisition and reconstruction of the channel impulse response in the nominal working condition and (ii) analysis of the channel state to detect any physical anomaly/discontinuity like deterioration (e.g. aging due to harsh environment) or unauthorized side channel attacks (e.g. taps). The first results demonstrate that the proposed method is capable of estimating the channel impairments with an accuracy that could allow the classification of the main channel impairments. The proposed method could be used to develop low-cost instrumentation for continuous monitoring of the physical layer of data networks and to improve their hardware security.
Ding, K., Meng, Z., Yu, Z., Ju, Z., Zhao, Z., Xu, K..  2020.  Photonic Compressive Sampling of Sparse Broadband RF Signals using a Multimode Fiber. 2020 Asia Communications and Photonics Conference (ACP) and International Conference on Information Photonics and Optical Communications (IPOC). :1–3.
We propose a photonic compressive sampling scheme based on multimode fiber for radio spectrum sensing, which shows high accuracy and stability, and low complexity and cost. Pulse overlapping is utilized for a fast detection. © 2020 The Author(s).
Alabadi, Montdher, Albayrak, Zafer.  2020.  Q-Learning for Securing Cyber-Physical Systems : A survey. 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). :1–13.
A cyber-physical system (CPS) is a term that implements mainly three parts, Physical elements, communication networks, and control systems. Currently, CPS includes the Internet of Things (IoT), Internet of Vehicles (IoV), and many other systems. These systems face many security challenges and different types of attacks, such as Jamming, DDoS.CPS attacks tend to be much smarter and more dynamic; thus, it needs defending strategies that can handle this level of intelligence and dynamicity. Last few years, many researchers use machine learning as a base solution to many CPS security issues. This paper provides a survey of the recent works that utilized the Q-Learning algorithm in terms of security enabling and privacy-preserving. Different adoption of Q-Learning for security and defending strategies are studied. The state-of-the-art of Q-learning and CPS systems are classified and analyzed according to their attacks, domain, supported techniques, and details of the Q-Learning algorithm. Finally, this work highlight The future research trends toward efficient utilization of Q-learning and deep Q-learning on CPS security.
Manchanda, R., Sharma, K..  2020.  A Review of Reconstruction Algorithms in Compressive Sensing. 2020 International Conference on Advances in Computing, Communication Materials (ICACCM). :322–325.
Compressive Sensing (CS) is a promising technology for the acquisition of signals. The number of measurements is reduced by using CS which is needed to obtain the signals in some basis that are compressible or sparse. The compressible or sparse nature of the signals can be obtained by transforming the signals in some domain. Depending on the signals sparsity signals are sampled below the Nyquist sampling criteria by using CS. An optimization problem needs to be solved for the recovery of the original signal. Very few studies have been reported about the reconstruction of the signals. Therefore, in this paper, the reconstruction algorithms are elaborated systematically for sparse signal recovery in CS. The discussion of various reconstruction algorithms in made in this paper will help the readers in order to understand these algorithms efficiently.
Li, Y., Zhou, Y., Hu, K., Sun, N., Ke, K..  2020.  A Security Situation Prediction Method Based on Improved Deep Belief Network. 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT. :594–598.
With the rapid development of smart grids and the continuous deepening of informatization, while realizing remote telemetry and remote control of massive data-based grid operation, electricity information network security problems have become more serious and prominent. A method for electricity information network security situation prediction method based on improved deep belief network is proposed in this paper. Firstly, the affinity propagation clustering algorithm is used to determine the depth of the deep belief network and the number of hidden layer nodes based on sample parameters. Secondly, continuously adjust the scaling factor and crossover probability in the differential evolution algorithm according to the population similarity. Finally, a chaotic search method is used to perform a second search for the best individuals and similarity centers of each generation of the population. Simulation experiments show that the proposed algorithm not only enhances the generalization ability of electricity information network security situation prediction, but also has higher prediction accuracy.