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Journal articles on the topic 'Cyber Algorithm'

Author: Grafiati

Published: 30 June 2021

Last updated: 5 February 2022

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Yin, Peng-Yeng, Fred Glover, Manuel Laguna, and Jia-Xian Zhu. "A Complementary Cyber Swarm Algorithm." International Journal of Swarm Intelligence Research 2, no. 2 (2011): 22–41. http://dx.doi.org/10.4018/jsir.2011040102.

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A recent study (Yin et al., 2010) showed that combining particle swarm optimization (PSO) with the strategies of scatter search (SS) and path relinking (PR) produces a Cyber Swarm Algorithm that creates a more effective form of PSO than methods that do not incorporate such mechanisms. This paper proposes a Complementary Cyber Swarm Algorithm (C/CyberSA) that performs in the same league as the original Cyber Swarm Algorithm but adopts different sets of ideas from the tabu search (TS) and the SS/PR template. The C/CyberSA exploits the guidance information and restriction information produced in the history of swarm search and the manipulation of adaptive memory. Responsive strategies using long term memory and path relinking implementations are proposed that make use of critical events encountered in the search. Experimental results with a large set of challenging test functions show that the C/CyberSA outperforms two recently proposed swarm-based methods by finding more optimal solutions while simultaneously using a smaller number of function evaluations. The C/CyberSA approach further produces improvements comparable to those obtained by the original CyberSA in relation to the Standard PSO 2007 method (Clerc, 2008).

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Liu, Xin-Rui, Yuan Meng, and Peng Chang. "Node Importance Evaluation of Cyber-Physical System under Cyber-Attacks Spreading." Complexity 2021 (January 16, 2021): 1–15. http://dx.doi.org/10.1155/2021/6641030.

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The study of cyber-attacks, and in particular the spread of attack on the power cyber-physical system, has recently attracted considerable attention. Identifying and evaluating the important nodes under the cyber-attack propagation scenario are of great significance for improving the reliability and survivability of the power system. In this paper, we improve the closeness centrality algorithm and propose a compound centrality algorithm based on adaptive coefficient to evaluate the importance of single-layer network nodes. Moreover, we quantitatively calculated the decouple degree of cascading failures caused by exposed nodes formed by attack propagation. At last, experiments based on the IEEE 57 test system show that the proposed compound centrality algorithm can match the cyber-attack propagation scenario well, and we give the importance values of the nodes in a specific attack scenario.

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Choi, Young Hwan, Ali Sadollah, and Joong Hoon Kim. "Improvement of Cyber-Attack Detection Accuracy from Urban Water Systems Using Extreme Learning Machine." Applied Sciences 10, no. 22 (2020): 8179. http://dx.doi.org/10.3390/app10228179.

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This study proposes a novel detection model for the detection of cyber-attacks using remote sensing data on water distribution systems (i.e., pipe flow sensor, nodal pressure sensor, tank water level sensor, and programmable logic controllers) by machine learning approaches. The most commonly used and well-known machine learning algorithms (i.e., k-nearest neighbor, support vector machine, artificial neural network, and extreme learning machine) were compared to determine the one with the best detection performance. After identifying the best algorithm, several improved versions of the algorithm are compared and analyzed according to their characteristics. Their quantitative performances and abilities to correctly classify the state of the urban water system under cyber-attack were measured using various performance indices. Among the algorithms tested, the extreme learning machine (ELM) was found to exhibit the best performance. Moreover, this study not only has identified excellent algorithm among the compared algorithms but also has considered an improved version of the outstanding algorithm. Furthermore, the comparison was performed using various representative performance indices to quantitatively measure the prediction accuracy and select the most appropriate model. Therefore, this study provides a new perspective on the characteristics of various versions of machine learning algorithms and their application to different problems, and this study may be referenced as a case study for future cyber-attack detection fields.

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Yang, Tingting, Hailong Feng, Jian Zhao, Ruilong Deng, Ying Wang, and Zhou Su. "Genetic optimization–based scheduling in maritime cyber physical systems." International Journal of Distributed Sensor Networks 13, no. 7 (2017): 155014771771716. http://dx.doi.org/10.1177/1550147717717163.

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In this article, we attempt to solve the issue of optimal scheduling for vessels monitoring video data uploading in maritime cyber physical systems, during the period of sailing from the origin port to destination port. We consider the terrestrial infrastructure-based networking, delivering video packets assisted by the shoreside infostations to the authorities on land. Each video packet has respective elements (i.e. release time, deadline, processing time, and weight) to describe, in which deadline is utilized to demonstrate the time domain limitation before that to upload it successfully. In order to cope with the computation complexity of traditional scheduling algorithms in intermittent infostations scenario, time-capacity mapping method is exploited to transfer it to a continue scenario when classic scheduling algorithms could be utilized with lower time complexity. An ingenious mathematic job-machine scheduling formulation is indicated with the goal of minimizing the total penalties of tardiness of uploaded video packets, taking into account the tardiness and the weights of jobs simultaneously. A genetic based algorithm, as well as an improved genetic algorithm–based optimization scheme, is proposed to target this optimization formulation. Specially, the genetic based algorithm as well as the improved genetic based algorithm are described in detail, including a novel chromosome representation, a heuristic initialization procedure, as well as a modified crossover and mutation process. The effectiveness of the proposed schemes is verified by the simulation results.

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N, Valliammal, and Barani Shaju. "Deep learning algorithm based cyber-attack detection in cyber-physical systems-a survey." International Journal of Advanced Technology and Engineering Exploration 5, no. 49 (2018): 489–94. http://dx.doi.org/10.19101/ijatee.2018.547030.

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P, Karunakaran. "Deep Learning Approach to DGA Classification for Effective Cyber Security." December 2020 2, no. 4 (2021): 203–13. http://dx.doi.org/10.36548/jucct.2020.4.003.

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In recent years, invaders are increasing rapidly in an internet world. Generally, in order to detect the anonymous attackers algorithm needs more number of features. Many algorithms fail in the efficiency of detection malicious code. Immediately this codes will not infect the system; it will attack server after communicate later. Our research focuses on analyzing the traffic of botnets for the domain name determination to the IP address of the server. This botnet creates the domain name differently. Many domains are generated by attackers and create the huge Domain Name System (DNS) traffic. In this research paper, uses both public and real time environments datasets to detect the text features as well as knowledge based feature extraction. The classifying of Domain Generation Algorithm (DGA) generated malicious domains randomly making the efficiency down in many algorithms which were used preprocessing without proper feature extraction. Effectively, our proposed algorithm is used to detect DGA which generates malicious domains randomly. This effective detection of our proposed algorithm performs with text based label prediction and additional features for extraction to improve the efficiency of the model. Our proposed model achieved 94.9% accuracy for DGA classification with help of additional feature extraction and knowledge based extraction in the deep learning architecture.

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Yin, Peng-Yeng, Po-Yen Chen, Ying-Chieh Wei, and Rong-Fuh Day. "Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization." Applied Sciences 10, no. 24 (2020): 8961. http://dx.doi.org/10.3390/app10248961.

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Recently, two evolutionary algorithms (EAs), the glowworm swarm optimization (GSO) and the firefly algorithm (FA), have been proposed. The two algorithms were inspired by the bioluminescence process that enables the light-mediated swarming behavior for mating or foraging. From our literature survey, we are convinced with much evidence that the EAs can be more effective if appropriate responsive strategies contained in the adaptive memory programming (AMP) domain are considered in the execution. This paper contemplates this line and proposes the Cyber Firefly Algorithm (CFA), which integrates key elements of the GSO and the FA and further proliferates the advantages by featuring the AMP-responsive strategies including multiple guiding solutions, pattern search, multi-start search, swarm rebuilding, and the objective landscape analysis. The robustness of the CFA has been compared against the GSO, FA, and several state-of-the-art metaheuristic methods. The experimental result based on intensive statistical analyses showed that the CFA performs better than the other algorithms for global optimization of benchmark functions.

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Kozik, Rafał, and Michał Choraś. "Pattern Extraction Algorithm for NetFlow-Based Botnet Activities Detection." Security and Communication Networks 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/6047053.

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As computer and network technologies evolve, the complexity of cybersecurity has dramatically increased. Advanced cyber threats have led to current approaches to cyber-attack detection becoming ineffective. Many currently used computer systems and applications have never been deeply tested from a cybersecurity point of view and are an easy target for cyber criminals. The paradigm of security by design is still more of a wish than a reality, especially in the context of constantly evolving systems. On the other hand, protection technologies have also improved. Recently, Big Data technologies have given network administrators a wide spectrum of tools to combat cyber threats. In this paper, we present an innovative system for network traffic analysis and anomalies detection to utilise these tools. The systems architecture is based on a Big Data processing framework, data mining, and innovative machine learning techniques. So far, the proposed system implements pattern extraction strategies that leverage batch processing methods. As a use case we consider the problem of botnet detection by means of data in the form of NetFlows. Results are promising and show that the proposed system can be a useful tool to improve cybersecurity.

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Do, ChoXuan, Nguyen Quang Dam, and Nguyen Tung Lam. "Optimization of network traffic anomaly detection using machine learning." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (2021): 2360. http://dx.doi.org/10.11591/ijece.v11i3.pp2360-2370.

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In this paper, to optimize the process of detecting cyber-attacks, we choose to propose 2 main optimization solutions: Optimizing the detection method and optimizing features. Both of these two optimization solutions are to ensure the aim is to increase accuracy and reduce the time for analysis and detection. Accordingly, for the detection method, we recommend using the Random Forest supervised classification algorithm. The experimental results in section 4.1 have proven that our proposal that use the Random Forest algorithm for abnormal behavior detection is completely correct because the results of this algorithm are much better than some other detection algorithms on all measures. For the feature optimization solution, we propose to use some data dimensional reduction techniques such as information gain, principal component analysis, and correlation coefficient method. The results of the research proposed in our paper have proven that to optimize the cyber-attack detection process, it is not necessary to use advanced algorithms with complex and cumbersome computational requirements, it must depend on the monitoring data for selecting the reasonable feature extraction and optimization algorithm as well as the appropriate attack classification and detection algorithms.

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Ma, Wu Bin, Ming Xing Liu, Su Deng, and Hong Bin Huang. "A Spatial Resource Top-K Query Algorithm in Cyber Physical System." Advanced Materials Research 774-776 (September 2013): 1725–28. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1725.

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Cyber physical system (CPS) mainly means that the conjoining of information and physical resources. The physical elements, which could be treated as spatial resources, need be queried through information field. The key problem is how to construct spatial resource index structure and accordingly retrieving algorithm. The spatial retrieving algorithm is widely used in the Internet and moving object. The traditional spatial resource retrieving algorithms ignore the uncertain factor of the spatial resource, which is the important feature of the military resource network. This paper use an index structure named BIR-tree, based on the belief of resource, we also propose corresponding efficient and accurate top-k retrieving algorithm. On the base of traditional IR-tree, we define the resource and regional belief aiming at special spatial environment. The results of our empirical studies with an implementation of the proposed algorithm show it is capable of excellent performance.

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Javeed, Danish, Tianhan Gao, and Muhammad Taimoor Khan. "SDN-Enabled Hybrid DL-Driven Framework for the Detection of Emerging Cyber Threats in IoT." Electronics 10, no. 8 (2021): 918. http://dx.doi.org/10.3390/electronics10080918.

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The Internet of Things (IoT) has proven to be a billion-dollar industry. Despite offering numerous benefits, the prevalent nature of IoT makes it vulnerable and a possible target for the development of cyber-attacks. The diversity of the IoT, on the one hand, leads to the benefits of the integration of devices into a smart ecosystem, but the heterogeneous nature of the IoT makes it difficult to come up with a single security solution. However, the centralized intelligence and programmability of software-defined networks (SDNs) have made it possible to compose a single and effective security solution to cope with cyber threats and attacks. We present an SDN-enabled architecture leveraging hybrid deep learning detection algorithms for the efficient detection of cyber threats and attacks while considering the resource-constrained IoT devices so that no burden is placed on them. We use a state-of-the-art dataset, CICDDoS 2019, to train our algorithm. The results evaluated by this algorithm achieve high accuracy with a minimal false positive rate (FPR) and testing time. We also perform 10-fold cross-validation, proving our results to be unbiased, and compare our results with current benchmark algorithms.

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Han, Mee Lan, Deok Jin Kim, and Huy Kang Kim. "Applying CBR algorithm for cyber infringement profiling system." Journal of the Korea Institute of Information Security and Cryptology 23, no. 6 (2013): 1069–86. http://dx.doi.org/10.13089/jkiisc.2013.23.6.1069.

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Mohammadi, Sara, Hamid Mirvaziri, Mostafa Ghazizadeh-Ahsaee, and Hadis Karimipour. "Cyber intrusion detection by combined feature selection algorithm." Journal of Information Security and Applications 44 (February 2019): 80–88. http://dx.doi.org/10.1016/j.jisa.2018.11.007.

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Khudhur, Dhuha Dheyaa, and Muayad Sadik Croock. "Physical cyber-security algorithm for wireless sensor networks." TELKOMNIKA (Telecommunication Computing Electronics and Control) 19, no. 4 (2021): 1177. http://dx.doi.org/10.12928/telkomnika.v19i4.18464.

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Abdel-Basset, Mohamed, Reda Mohamed, Nazeeruddin Mohammad, Karam Sallam, and Nour Moustafa. "An Adaptive Cuckoo Search-Based Optimization Model for Addressing Cyber-Physical Security Problems." Mathematics 9, no. 10 (2021): 1140. http://dx.doi.org/10.3390/math9101140.

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One of the key challenges in cyber-physical systems (CPS) is the dynamic fitting of data sources under multivariate or mixture distribution models to determine abnormalities. Equations of the models have been statistically characterized as nonlinear and non-Gaussian ones, where data have high variations between normal and suspicious data distributions. To address nonlinear equations of these distributions, a cuckoo search algorithm is employed. In this paper, the cuckoo search algorithm is effectively improved with a novel strategy, known as a convergence speed strategy, to accelerate the convergence speed in the direction of the optimal solution for achieving better outcomes in a small number of iterations when solving systems of nonlinear equations. The proposed algorithm is named an improved cuckoo search algorithm (ICSA), which accelerates the convergence speed by improving the fitness values of function evaluations compared to the existing algorithms. To assess the efficacy of ICSA, 34 common nonlinear equations that fit the nature of cybersecurity models are adopted to show if ICSA can reach better outcomes with high convergence speed or not. ICSA has been compared with several well-known, well-established optimization algorithms, such as the slime mould optimizer, salp swarm, cuckoo search, marine predators, bat, and flower pollination algorithms. Experimental outcomes have revealed that ICSA is superior to the other in terms of the convergence speed and final accuracy, and this makes a promising alternative to the existing algorithm.

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J, Shankar Murthy. "Network Software Vulnerability Identifier using J48 decision tree algorithm." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 1889–92. http://dx.doi.org/10.22214/ijraset.2021.37685.

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Abstract: Software vulnerabilities are the primary causes of different security issues in the modern era. When vulnerability is exploited by malicious assaults, it substantially jeopardizes the system's security and may potentially result in catastrophic losses. As a result, automatic classification methods are useful for successfully managing software vulnerabilities, improving system security performance, and lowering the chance of the system being attacked and destroyed. In the software industry and in the field of cyber security, the ever-increasing number of publicly reported security flaws has become a major source of concern. Because software security flaws play such a significant part in cyber security attacks, relevant security experts are conducting an increasing number of vulnerability classification studies, this project can predict the software vulnerability means the software's in the device are authorized or not and who scan the system multiple times, to identify the vulnerability j48 decision tree algorithm was used. Keywords: Malicious assaults, catastrophic losses, Security flaws, Cyber security, Vulnerability Classifications.

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Li, Qianmu, Shunmei Meng, Xiaonan Sang, et al. "Dynamic Scheduling Algorithm in Cyber Mimic Defense Architecture of Volunteer Computing." ACM Transactions on Internet Technology 21, no. 3 (2021): 1–33. http://dx.doi.org/10.1145/3408291.

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Volunteer computing uses computers volunteered by the general public to do distributed scientific computing. Volunteer computing is being used in high-energy physics, molecular biology, medicine, astrophysics, climate study, and other areas. These projects have attained unprecedented computing power. However, with the development of information technology, the traditional defense system cannot deal with the unknown security problems of volunteer computing . At the same time, Cyber Mimic Defense (CMD) can defend the unknown attack behavior through its three characteristics: dynamic, heterogeneous, and redundant. As an important part of the CMD, the dynamic scheduling algorithm realizes the dynamic change of the service centralized executor, which can enusre the security and reliability of CMD of volunteer computing . Aiming at the problems of passive scheduling and large scheduling granularity existing in the existing scheduling algorithms, this article first proposes a scheduling algorithm based on time threshold and task threshold and realizes the dynamic randomness of mimic defense from two different dimensions; finally, combining time threshold and random threshold, a dynamic scheduling algorithm based on multi-level queue is proposed. The experiment shows that the dynamic scheduling algorithm based on multi-level queue can take both security and reliability into account, has better dynamic heterogeneous redundancy characteristics, and can effectively prevent the transformation rule of heterogeneous executors from being mastered by attackers.

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Oladimeji, Olasehinde Olayemi, Alese Boniface Kayode, Adetunmbi Adebayo Olusola, and Aladesote Olomi Isaiah. "Evaluation of Selected Stacked Ensemble Models for the Optimal Multi-class Cyber-Attacks Detection." International Journal on Cyber Situational Awareness 5, no. 1 (2021): 26–48. http://dx.doi.org/10.22619/ijcsa.2020.100132.

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The significant rise in the frequency and sophistication of cyber-attacks and their diversity necessitated various researchers to develop strong and effective approaches to address recurring cyber threat challenges. This study evaluated the performance of three selected meta-learning models for optimal multi-class detection of cyber-attacks using the University of New South Wales 2015 Network benchmark (UNSW-NB15) Intrusion Dataset. The results of this study show and confirm the ability of the three base models; Naive Bayes, C4.5 Decision Tree, and K-Nearest Neighbor for solving multi-class problems. It further affirms the knack of the duo of feature selection techniques and stacked ensemble learning to optimize ML models' performances. The stacking of the predictions of the information gain base models with Model Decision Tree meta-algorithm recorded the most improved and optimal cyber-attacks detection accuracy and Mattew's correlation Coefficient than the stacking with the Multiple Model Trees (MMT) and Multi Response Linear regression (MLR) Meta algorithms.

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Abdou Hussien, Abou_el_ela. "Cyber Security Crimes, Ethics and a Suggested Algorithm to Overcome Cyber-Physical Systems Problems (CybSec1)." Journal of Information Security 12, no. 01 (2021): 56–78. http://dx.doi.org/10.4236/jis.2021.121003.

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Kuo, R. J., S. H. Lin, and Zhen-Yao Chen. "Integration of Particle Swarm Optimization and Immune Genetic Algorithm-Based Dynamic Clustering for Customer Clustering." International Journal on Artificial Intelligence Tools 24, no. 05 (2015): 1550019. http://dx.doi.org/10.1142/s0218213015500190.

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This study intends to present a dynamic clustering (DC) approach based on particle swarm optimization (PSO) and immune genetic (IG) (DCPIG) algorithm, which is able to cluster the data into adequate clusters through data characteristics with pre-specified numbers of clusters. The proposed DCPIG algorithm is compared with three DC algorithms in the literature using Iris, Wine, Glass and Vowel benchmark data sets. The experiment results show that the DCPIG algorithm can achieve higher stability and accuracy than the other algorithms. In addition, the DCPIG algorithm is also applied to a real-world problem considering the customer clustering for a cyber flower shop. Lastly, we recommend different products and services to customers based on the clustering results.

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S, Smys, Haoxiang Wang, and Abul Basar. "5G Network Simulation in Smart Cities using Neural Network Algorithm." March 2021 3, no. 1 (2021): 43–52. http://dx.doi.org/10.36548/jaicn.2021.1.004.

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The speed of internet has increased dramatically with the introduction of 4G and 5G promises an even greater transmission rate with coverage outdoors and indoors in smart cities. This indicates that the introduction of 5G might result in replacing the Wi-Fi that is being currently used for applications such as geo-location using continuous radio coverage there by initiating the involvement of IoT in all devices that are used. The introduction of Wi-Fi 6 is already underway for applications that work with IoT, smart city applications will still require 5G to provide internet services using Big Data to reduce the requirement of mobile networks and additional private network infrastructure. However, as the network access begins to expand, it also introduces the risk of cyber security with the enhanced connectivity in the networking. Additional digital targets will be given to the cyber attackers and independent services will also be sharing access channel infrastructure between mobile and wireless network. In order to address these issues, we have introduced a random neural network blockchain technology that can be used to strengthen cybersecurity in many applications. Here the identity of the user is maintained as a secret while the information is codified using neural weights. However, when a cyber security breach occurs, the attacker will be easily tracked by mining the confidential identity. Thus a reliable and decentralized means of authentication method is proposed in this work. The results thus obtained are validated and shows that the introduction of the random neural network using blockchain improves connectivity, decentralized user access and cyber security resilience.

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Meira, Jorge. "Comparative Results with Unsupervised Techniques in Cyber Attack Novelty Detection." Proceedings 2, no. 18 (2018): 1191. http://dx.doi.org/10.3390/proceedings2181191.

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Intrusion detection is a major necessity in current times. Computer systems are constantly being victims of malicious attacks. These attacks keep on exploring new technics that are undetected by current Intrusion Detection Systems (IDS), because most IDS focus on detecting signatures of previously known attacks. This work explores some unsupervised learning algorithms that have the potential of identifying previously unknown attacks, by performing outlier detection. The algorithms explored are one class based: the Autoencoder Neural Network, K-Means, Nearest Neighbor and Isolation Forest. There algorithms were used to analyze two publicly available datasets, the NSL-KDD and ISCX, and compare the results obtained from each algorithm to perceive their performance in novelty detection.

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Balan, Shilpa, Sanchita Gawand, and Priyanka Purushu. "Application of Machine Learning Classification Algorithm to Cybersecurity Awareness." Information Technology and Management Science 21 (December 14, 2018): 45–48. http://dx.doi.org/10.7250/itms-2018-0006.

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Cybersecurity plays a vital role in protecting the privacy and data of people. In the recent times, there have been several issues relating to cyber fraud, data breach and cyber theft. Many people in the United States have been a victim of identity theft. Thus, understanding of cybersecurity plays an important role in protecting their information and devices. As the adoption of smart devices and social networking are increasing, cybersecurity awareness needs to be spread. The research aims at building a classification machine learning algorithm to determine the awareness of cybersecurity by the common masses in the United States. We were able to attain a good F-measure score when evaluating the performance of the classification model built for this study.

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Tsiami, Lydia, and Christos Makropoulos. "Cyber—Physical Attack Detection in Water Distribution Systems with Temporal Graph Convolutional Neural Networks." Water 13, no. 9 (2021): 1247. http://dx.doi.org/10.3390/w13091247.

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Prompt detection of cyber–physical attacks (CPAs) on a water distribution system (WDS) is critical to avoid irreversible damage to the network infrastructure and disruption of water services. However, the complex interdependencies of the water network’s components make CPA detection challenging. To better capture the spatiotemporal dimensions of these interdependencies, we represented the WDS as a mathematical graph and approached the problem by utilizing graph neural networks. We presented an online, one-stage, prediction-based algorithm that implements the temporal graph convolutional network and makes use of the Mahalanobis distance. The algorithm exhibited strong detection performance and was capable of localizing the targeted network components for several benchmark attacks. We suggested that an important property of the proposed algorithm was its explainability, which allowed the extraction of useful information about how the model works and as such it is a step towards the creation of trustworthy AI algorithms for water applications. Additional insights into metrics commonly used to rank algorithm performance were also presented and discussed.

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Brentan, Bruno, Pedro Rezende, Daniel Barros, Gustavo Meirelles, Edevar Luvizotto, and Joaquín Izquierdo. "Cyber-Attack Detection in Water Distribution Systems Based on Blind Sources Separation Technique." Water 13, no. 6 (2021): 795. http://dx.doi.org/10.3390/w13060795.

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Service quality and efficiency of urban systems have been dramatically boosted by various high technologies for real-time monitoring and remote control, and have also gained privileged space in water distribution. Monitored hydraulic and quality parameters are crucial data for developing planning, operation and security analyses in water networks, which makes them increasingly reliable. However, devices for monitoring and remote control also increase the possibilities for failure and cyber-attacks in the systems, which can severely impair the system operation and, in extreme cases, collapse the service. This paper proposes an automatic two-step methodology for cyber-attack detection in water distribution systems. The first step is based on signal-processing theory, and applies a fast Independent Component Analysis (fastICA) algorithm to hydraulic time series (e.g., pressure, flow, and tank level), which separates them into independent components. These components are then processed by a statistical control algorithm for automatic detection of abrupt changes, from which attacks may be disclosed. The methodology is applied to the case study provided by the Battle of Attack Detection Algorithms (BATADAL) and the results are compared with seven other approaches, showing excellent results, which makes this methodology a reliable early-warning cyber-attack detection approach.

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Yan, He Hua, Jia Fu Wan, and Hui Suo. "Adaptive Resource Management for Cyber-Physical Systems." Applied Mechanics and Materials 157-158 (February 2012): 747–51. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.747.

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Cyber-physical systems (CPSs) perfectly integrate computing with physical processes, and the emergence of CPSs has attracted significant interest in recent years. In order to fully utilize system resource and improve quality of service (QoS), the innovative resource management method for CPSs is essential. According to a representative case of CPSs (e. g., unmanned vehicle with wireless sensor network navigation), we propose a hierarchical architecture for CPSs, and further establish a system performance optimization model with resource constraints. The particle swarm optimization (PSO) algorithm is applied to solve the considered constraint model. The simulation experiment results verify the efficiency of PSO algorithm, and some instructive proposals for promoting QoS are also outlined.

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Lazaro, Caterina, Erdal Oruklu, and Ali Cinar. "Cyber-Physical Platform Development for Multivariable Artificial Pancreas Systems." International Journal of Handheld Computing Research 6, no. 3 (2015): 1–16. http://dx.doi.org/10.4018/ijhcr.2015070101.

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This paper describes a distributed sensor platform for a new breed of artificial pancreas devices. In recent work, a multi-variable adaptive algorithm has been proposed which incorporates physical activity of the patients for accurate prediction and control of glucose levels. In order to facilitate this algorithm, the authors integrate a smartphone and multiple sensors including activity trackers and a glucose monitor into a distributed system. The proposed sensor platform provides real-time data access for the artificial pancreas control algorithm hosted on a remote device.

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Zhou, Wei, Qian Mu Li, and Hong Zhang. "A Method to Solving a Kind of Nonlinear Cyber-Security Equations." Applied Mechanics and Materials 121-126 (October 2011): 627–31. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.627.

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A method to get numerical solutions to Nonlinear Cyber-Security Equationss with the the N algorithm is proposed . This paper especially focused on its converging rate and application. Also, It did some comparison between with other universal algorithms. Both mathematical method and experimental method are used to do the comparison. This paper gave strict mathematical proof and also used the results we got from our program to draw a solid conclusion.

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Liu, Jia, Mingchu Li, William C. Tang, and Sardar M. N. Islam. "A Cyber Physical System Crowdsourcing Inference Method Based on Tempering: An Advancement in Artificial Intelligence Algorithms." Wireless Communications and Mobile Computing 2021 (February 19, 2021): 1–11. http://dx.doi.org/10.1155/2021/6618980.

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Activity selection is critical for the smart environment and Cyber-Physical Systems (CPSs) that can provide timely and intelligent services, especially as the number of connected devices is increasing at an unprecedented speed. As it is important to collect labels by various agents in the CPSs, crowdsourcing inference algorithms are designed to help acquire accurate labels that involve high-level knowledge. However, there are some limitations in the algorithm in the existing literature such as incurring extra budget for the existing algorithms, inability to scale appropriately, requiring the knowledge of prior distribution, difficulties to implement these algorithms, or generating local optima. In this paper, we provide a crowdsourcing inference method with variational tempering that obtains ground truth as well as considers both the reliability of workers and the difficulty level of the tasks and ensure a local optimum. The numerical experiments of the real-world data indicate that our novel variational tempering inference algorithm performs better than the existing advancing algorithms. Therefore, this paper provides a new efficient algorithm in CPSs and machine learning, and thus, it makes a new contribution to the literature.

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Singh, Khundrakpam Johnson, and Tanmay De. "Efficient Classification of DDoS Attacks Using an Ensemble Feature Selection Algorithm." Journal of Intelligent Systems 29, no. 1 (2017): 71–83. http://dx.doi.org/10.1515/jisys-2017-0472.

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Abstract In the current cyber world, one of the most severe cyber threats are distributed denial of service (DDoS) attacks, which make websites and other online resources unavailable to legitimate clients. It is different from other cyber threats that breach security parameters; however, DDoS is a short-term attack that brings down the server temporarily. Appropriate selection of features plays a crucial role for effective detection of DDoS attacks. Too many irrelevant features not only produce unrelated class categories but also increase computation overhead. In this article, we propose an ensemble feature selection algorithm to determine which attribute in the given training datasets is efficient in categorizing the classes. The result of the ensemble algorithm when compared to a threshold value will enable us to decide the features. The selected features are deployed as training inputs for various classifiers to select a classifier that yields maximum accuracy. We use a multilayer perceptron classifier as the final classifier, as it provides better accuracy when compared to other conventional classification models. The proposed method classifies the new datasets into either attack or normal classes with an efficiency of 98.3% and also reduces the overall computation time. We use the CAIDA 2007 dataset to evaluate the performance of the proposed method using MATLAB and Weka 3.6 simulators.

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Umar, Sani, and Muhamad Felemban. "Rule-Based Detection of False Data Injections Attacks against Optimal Power Flow in Power Systems." Sensors 21, no. 7 (2021): 2478. http://dx.doi.org/10.3390/s21072478.

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Cyber-security of modern power systems has captured a significant interest. The vulnerabilities in the cyber infrastructure of the power systems provide an avenue for adversaries to launch cyber attacks. An example of such cyber attacks is False Data Injection Attacks (FDIA). The main contribution of this paper is to analyze the impact of FDIA on the cost of power generation and the physical component of the power systems. Furthermore, We introduce a new FDIA strategy that intends to maximize the cost of power generation. The viability of the attack is shown using simulations on the standard IEEE bus systems using the MATPOWER MATLAB package. We used the genetic algorithm (GA), simulated annealing (SA) algorithm, tabu search (TS), and particle swarm optimization (PSO) to find the suitable attack targets and execute FDIA in the power systems. The proposed FDIA increases the generation cost by up to 15.6%, 45.1%, 60.12%, and 74.02% on the 6-bus, 9-bus, 30-bus, and 118-bus systems, respectively. Finally, a rule-based FDIA detection and prevention mechanism is proposed to mitigate such attacks on power systems.

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D., Kushnir, and Paramud Y. "THE INTELIGENE ALGORITHM OF CYBER–PHYSICAL SYSTEM TARGETING ON A MOVABLE OBJECT USING THE SMART SENSOR UNIT." Computer systems and network 2, no. 1 (2017): 44–52. http://dx.doi.org/10.23939/csn2020.01.044.

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As a result of the analytical review, it was established that smart sensor units are one of the main components of the cyber–physical system. One of the tasks, which have been entrusted to such units, are targeting and tracking of movable objects. The algorithm of targeting on such objects using observation equipment has been considered. This algorithm is able to continuously monitor observation results, predict the direction with the highest probability of movement and form a set of commands to maximize the approximation of a moving object to the center of an information frame. The algorithm, is based on DDPG reinforcement learning algorithm. The algorithm has been verified on an experimental physical model using a drone. The object recognition module has been developed using YOLOv3 architecture. iOS application has been developed in order to communicate with the drone through WIFI hotspot using UDP commands. Advanced filters have been added to increase the quality of recognition results. The results of experimental research on the mobile platform confirmed the functioning of the targeting algorithm in real–time. Key words: Cyber–physical system, smart sensor unit, reinforcement learning, targeting algorithm, drones.

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Kuc, Mateusz, Wojciech Sułek, and Dariusz Kania. "FPGA-Oriented LDPC Decoder for Cyber-Physical Systems." Mathematics 8, no. 5 (2020): 723. http://dx.doi.org/10.3390/math8050723.

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A potentially useful Cyber-Physical Systems element is a modern forward error correction (FEC) coding system, utilizing a code selected from the broad class of Low-Density Parity-Check (LDPC) codes. In this paper, development of a hardware implementation in an FPGAs of the decoder for Quasi-Cyclic (QC-LDPC) subclass of codes is presented. The decoder can be configured to support the typical decoding algorithms: Min-Sum or Normalized Min-Sum (NMS). A novel method of normalization in the NMS algorithm is proposed, one that utilizes combinational logic instead of arithmetic units. A comparison of decoders with different bit-lengths of data (beliefs that are messages propagated between computing units) is also provided. The presented decoder has been implemented with a distributed control system. Experimental studies were conducted using the Intel Cyclone V FPGA module, which is a part of the developed testing environment for LDPC coding systems.

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Kolosok, Irina, and Liudmila Gurina. "Cyber Security-Oriented Smart Grid State Estimation." E3S Web of Conferences 69 (2018): 02004. http://dx.doi.org/10.1051/e3sconf/20186902004.

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Development of Smart Grid involves the introduction of Wide Area Measurement System (WAMS), which provides the use of information, computing and digital technologies for measuring, transmitting and processing operating parameters when solving control problems. In this regard, the increased vulnerability to cyberattacks of the control system was noted. The control of Smart Grid includes monitoring, forecasting and planning of the system operation based on its Electric Power System state estimation results. Therefore, the goal of the paper is to develop a mathematical instrument to bad data detection under cyberattacks. Particular attention is paid to false data injection attacks which result in distortion of state variables estimates. The result of the research is an algorithm developed for state estimation based on the interior point method and test equation obtained by Crout matrix decomposition. The obtained results showed effectiveness of the algorithm in state estimation.

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Do, Taehoon, Seungwoo Park, Jaehwan Lee, and Sangoh Park. "M-folding method–based elliptic curve cryptosystem for industrial cyber-physical system." International Journal of Distributed Sensor Networks 15, no. 10 (2019): 155014771987904. http://dx.doi.org/10.1177/1550147719879045.

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Recently, cyber-physical system is widely used for smart system control in various fields. Various functions of the cyber-physical system must overcome the limited hardware resources constraint of an embedded system. In addition, the data required from the industrial cyber-physical system are critical; therefore, a highly secure encryption technique is required. However, security and computational throughput are incompatible with each other in the cryptographic technique; therefore, the industrial cyber-physical system needs to adopt a highly efficient and secure encryption technique considering the limited available resources. This study applies the m-folding method to the highly secure elliptic curve algorithm to improve efficiency and proposes the cryptosystem optimized for the resource-constrained industrial cyber-physical system. The proposed m-folding method–based elliptic curve encryption showed 50% faster encryption than the existing methods.

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Alhudhaif, Adi, Maryam Yammahi, Tong Yan, and Simon Berkovich. "A Cyber-Physical Stream Algorithm for Intelligent Software Defined Storage." International Journal of Computer Applications 109, no. 5 (2015): 21–25. http://dx.doi.org/10.5120/19185-0672.

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Dayanika, J., G. Archana, K. Siva Kumar, and N. Pavani. "Early Detection of Cyber Attacks Based on Feature Selection Algorithm." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4648–53. http://dx.doi.org/10.1166/jctn.2020.9293.

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The world is turning out to be progressively digitalized raising security concerns and the urgent requirement for strong and propelled security advancements and systems to battle the expanding complex nature of digital assaults. This paper talks about how machine learning is being utilized in digital security in resistance and offense exercises, remembering conversations for digital assaults focused at machine learning models. In this review, we are proposing a scientific categorization of IDS, which considers information protests to be essential measurements to group and condense IDS Literature based on machine learning and based on profound knowledge. The review explains initially the idea and scientific grade of IDSs. Machine learning calculations are presented at that point for the many time used in IDSs, measurements and presented benchmark datasets. Next, we take the proposed ordered framework as a benchmark in conjunction with the agent writing and explain how to understand key IDS issues with machine learning and profound systems. At long last, difficulties and future advancements are talked about by assessing ongoing agent examines. This paper proposes IDS dependent on highlight determination and bunching calculation utilizing channel and wrapper techniques. Channel and wrapper strategies are named include gathering dependent on direct connection coefficient (FGLCC) calculation and cuttlefish calculation (CFA), separately.

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Iwendi, Celestine, Zunera Jalil, Abdul Rehman Javed, et al. "KeySplitWatermark: Zero Watermarking Algorithm for Software Protection Against Cyber-Attacks." IEEE Access 8 (2020): 72650–60. http://dx.doi.org/10.1109/access.2020.2988160.

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Li, Shancang, Shanshan Zhao, Po Yang, Panagiotis Andriotis, Lida Xu, and Qindong Sun. "Distributed Consensus Algorithm for Events Detection in Cyber-Physical Systems." IEEE Internet of Things Journal 6, no. 2 (2019): 2299–308. http://dx.doi.org/10.1109/jiot.2019.2906157.

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Price, Benjamin, Michael Zhivich, Michael Thompson, and Chris Eagle. "House Rules: Designing the Scoring Algorithm for Cyber Grand Challenge." IEEE Security & Privacy 16, no. 2 (2018): 23–31. http://dx.doi.org/10.1109/msp.2018.1870877.

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Hu, Zhaohua, and JiaJing Huang. "Re-detection object tracking algorithm in the cyber physical system." IET Cyber-Physical Systems: Theory & Applications 5, no. 3 (2020): 253–62. http://dx.doi.org/10.1049/iet-cps.2019.0086.

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Park, Sanghyuk, Jai-Hoon Kim, and Geoffrey Fox. "Effective real-time scheduling algorithm for cyber physical systems society." Future Generation Computer Systems 32 (March 2014): 253–59. http://dx.doi.org/10.1016/j.future.2013.10.003.

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Kuo, Shu-Yu, Yao-Hsin Chou, and Chi-Yuan Chen. "Quantum-inspired algorithm for cyber-physical visual surveillance deployment systems." Computer Networks 117 (April 2017): 5–18. http://dx.doi.org/10.1016/j.comnet.2016.11.013.

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Strohschein, Jan, Andreas Fischbach, Andreas Bunte, Heide Faeskorn-Woyke, Natalia Moriz, and Thomas Bartz-Beielstein. "Cognitive capabilities for the CAAI in cyber-physical production systems." International Journal of Advanced Manufacturing Technology 115, no. 11-12 (2021): 3513–32. http://dx.doi.org/10.1007/s00170-021-07248-3.

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AbstractThis paper presents the cognitive module of the Cognitive Architecture for Artificial Intelligence (CAAI) in cyber-physical production systems (CPPS). The goal of this architecture is to reduce the implementation effort of artificial intelligence (AI) algorithms in CPPS. Declarative user goals and the provided algorithm-knowledge base allow the dynamic pipeline orchestration and configuration. A big data platform (BDP) instantiates the pipelines and monitors the CPPS performance for further evaluation through the cognitive module. Thus, the cognitive module is able to select feasible and robust configurations for process pipelines in varying use cases. Furthermore, it automatically adapts the models and algorithms based on model quality and resource consumption. The cognitive module also instantiates additional pipelines to evaluate algorithms from different classes on test functions. CAAI relies on well-defined interfaces to enable the integration of additional modules and reduce implementation effort. Finally, an implementation based on Docker, Kubernetes, and Kafka for the virtualization and orchestration of the individual modules and as messaging technology for module communication is used to evaluate a real-world use case.

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Kushnir, Dmytro, and Yaroslav Paramud. "The Algorithm of Cyber-physical System Targeting on a Movable Object Using the Smart Sensor Unit." Advances in Cyber-Physical Systems 5, no. 1 (2017): 16–22. http://dx.doi.org/10.23939/acps2020.01.016.

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It is known that smart sensor units are one of the main components of the cyber-physical system. One of the tasks, which have been entrusted to such units, are targeting and tracking of movable objects. The algorithm of targeting on such objects using observation equipment has been considered. This algorithm is able to continuously monitor observation results, predict the direction with the highest probability of movement and form a set of commands to maximize the approximation of a moving object to the center of an information frame. The algorithm has been verified on an experimental physical model using a drone. The object recognition module has been developed using YOLOv3 architecture. iOS application has been developed in order to communicate with the drone through WIFI hotspot using UDP commands. Advanced filters have been added to increase the quality of recognition results. The results of experimental research on the mobile platform confirmed the functioning of the targeting algorithm in real-time.

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Tang, Hong Tao. "Strategy for Test Paper Composition Based on Genetic Algorithm." Applied Mechanics and Materials 513-517 (February 2014): 1688–91. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1688.

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Recently, with rapid development of computer/network technology and algorithms for composing test paper, cyber-based online examination system is a practically valuable hot research concern. In the paper, the mathematical model is created for solving problems with the online test paper composition system. Through comparative analysis of merits and shortcomings of various coding schemes, and to overcome the shortcoming that traditional genetic algorithms easily fall into premature convergence, it utilizes the adaptive adjustment method of dynamic parameters and elitist strategy to improve to develop the online test paper forming scheme based on adaptive genetic algorithm. For the selection of each parameter, simulation test is conducted to obtain the solution approximate to the best one.

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Fernando, Chandima, Carrick Detweiler, and Justin Bradley. "Co-Regulated Consensus of Cyber-Physical Resources in Multi-Agent Unmanned Aircraft Systems." Electronics 8, no. 5 (2019): 569. http://dx.doi.org/10.3390/electronics8050569.

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Intelligent utilization of resources and improved mission performance in an autonomous agent require consideration of cyber and physical resources. The allocation of these resources becomes more complex when the system expands from one agent to multiple agents, and the control shifts from centralized to decentralized. Consensus is a distributed algorithm that lets multiple agents agree on a shared value, but typically does not leverage mobility. We propose a coupled consensus control strategy that co-regulates computation, communication frequency, and connectivity of the agents to achieve faster convergence times at lower communication rates and computational costs. In this strategy, agents move towards a common location to increase connectivity. Simultaneously, the communication frequency is increased when the shared state error between an agent and its connected neighbors is high. When the shared state converges (i.e., consensus is reached), the agents withdraw to the initial positions and the communication frequency is decreased. Convergence properties of our algorithm are demonstrated under the proposed co-regulated control algorithm. We evaluated the proposed approach through a new set of cyber-physical, multi-agent metrics and demonstrated our approach in a simulation of unmanned aircraft systems measuring temperatures at multiple sites. The results demonstrate that, compared with fixed-rate and event-triggered consensus algorithms, our co-regulation scheme can achieve improved performance with fewer resources, while maintaining high reactivity to changes in the environment and system.

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Cao, Yuanlong, Ruiwen Ji, Lejun Ji, Mengshuang Bao, Lei Tao, and Wei Yang. "Can Multipath TCP Be Robust to Cyber Attacks? A Measuring Study of MPTCP with Active Queue Management Algorithms." Security and Communication Networks 2021 (May 27, 2021): 1–11. http://dx.doi.org/10.1155/2021/9963829.

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With the development of social networks, more and more mobile social network devices have multiple interfaces. Multipath TCP (MPTCP), as an emerging transmission protocol, can fit multiple link bandwidths to improve data transmission performance and improve user experience quality. At the same time, due to the large-scale deployment and application of emerging technologies such as the Internet of Things and cloud computing, cyber attacks against MPTCP have gradually increased. More and more network security research studies point out that low-rate distributed denial of service (LDDoS) attacks are relatively popular and difficult to detect and are recognized as one of the most severe threats to network services. This article introduces six classic queue management algorithms: DropTail, RED, FRED, REM, BLUE, and FQ. In a multihomed network environment, we perform the performance evaluation of MPTCP under LDDoS attacks in terms of throughput, delay, and packet loss rate when using the six algorithms, respectively, by simulations. The results show that in an MPTCP-enabled multihomed network, different queue management algorithms have different throughput, delay, and packet loss rate performance when subjected to LDDoS attacks. Considering these three performance indicators comprehensively, the FRED algorithm has better performance. By adopting an effective active queue management (AQM) algorithm, the MPTCP transmission system can enhance its robustness capability, thus improving transmission performance. We suggest that when designing and improving the queue management algorithm, the antiattack performance of the algorithm should be considered: (1) it can adjust the traffic speed by optimizing the congestion control mechanism; (2) the fairness of different types of data streams sharing bandwidth is taken into consideration; and (3) it has the ability to adjust the parameters of the queue management algorithm in a timely and accurate manner.

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Bihl, Trevor J., Todd J. Paciencia, Kenneth W. Bauer, and Michael A. Temple. "Cyber-Physical Security with RF Fingerprint Classification through Distance Measure Extensions of Generalized Relevance Learning Vector Quantization." Security and Communication Networks 2020 (February 24, 2020): 1–12. http://dx.doi.org/10.1155/2020/3909763.

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Radio frequency (RF) fingerprinting extracts fingerprint features from RF signals to protect against masquerade attacks by enabling reliable authentication of communication devices at the “serial number” level. Facilitating the reliable authentication of communication devices are machine learning (ML) algorithms which find meaningful statistical differences between measured data. The Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is one ML algorithm which has shown efficacy for RF fingerprinting device discrimination. GRLVQI extends the Learning Vector Quantization (LVQ) family of “winner take all” classifiers that develop prototype vectors (PVs) which represent data. In LVQ algorithms, distances are computed between exemplars and PVs, and PVs are iteratively moved to accurately represent the data. GRLVQI extends LVQ with a sigmoidal cost function, relevance learning, and PV update logic improvements. However, both LVQ and GRLVQI are limited due to a reliance on squared Euclidean distance measures and a seemingly complex algorithm structure if changes are made to the underlying distance measure. Herein, the authors (1) develop GRLVQI-D (distance), an extension of GRLVQI to consider alternative distance measures and (2) present the Cosine GRLVQI classifier using this framework. To evaluate this framework, the authors consider experimentally collected Z-wave RF signals and develop RF fingerprints to identify devices. Z-wave devices are low-cost, low-power communication technologies seen increasingly in critical infrastructure. Both classification and verification, claimed identity, and performance comparisons are made with the new Cosine GRLVQI algorithm. The results show more robust performance when using the Cosine GRLVQI algorithm when compared with four algorithms in the literature. Additionally, the methodology used to create Cosine GRLVQI is generalizable to alternative measures.

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Naeem Yasir, Muntadher, and Muayad Sadik Croock. "Multi-level cyber security system for VANET." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 2 (2020): 940. http://dx.doi.org/10.11591/ijeecs.v19.i2.pp940-948.

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<p>Recently, the cyber security of vehicular ad-hoc network (VANET) including two practicable: car-to-car and car-to-infrastructure has been considered due to importance. It has become possible to keep pace with the development in the world, where the safety of people is a priority in the development of technology in general and in particular in the field of VANET. In this paper, a cyber security system for VANET has been proposed to tackle the DOS attacks. The proposed system includes three security levels. Firstly, the registeration level that ask vehicles to be registered in the system, in which the network exclude the unrigestered ones. Secondly, the authentication level that checks the vehicles before joining the network. This is done by applying a proposed algorithm that considers the hash function and factory number. Thirdly, the proposed system ables to detect the DOS attack by any involved vehicle using the monitoring algorithm that allocate such vehicle to be excluded from the network. The obtained results show the efficient performance of the proposed system in managing the security of the VANET network. The multi-level system increases the security of the network, in which the attacks can be eliminated.</p>

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