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Thematische Bibliographien / Cyber Algorithm / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Cyber Algorithm“

Autor: Grafiati

Veröffentlicht am 30. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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1

Manas Kumar Yogi and Dwarampudi Aiswarya. "Designing Greedy Algorithm for Cyber Threat Detection." Journal of ISMAC 5, no. 3 (2023): 200–218. http://dx.doi.org/10.36548/jismac.2023.3.002.

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As the digital landscape continues to expand, the complexity and frequency of cyber threats targeting critical information systems also increases. Effective cyber threat detection has become a paramount concern for safeguarding sensitive data and ensuring the uninterrupted operation of various infrastructures. This research introduces a novel approach to cyber threat detection through the design of a greedy algorithm tailored for identifying specific types of threats. The algorithm focuses on a simplified aspect of threat detection, aiming to highlight the potential of greedy algorithms in con

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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

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Rahman, S. R. "A Fussy Based Neural Genetic Algorithm for Securing Data in Cyber Security." International Journal on Future Revolution in Computer Science & Communication Engineering 8, no. 2 (2022): 79–85. http://dx.doi.org/10.17762/ijfrcsce.v8i2.2081.

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Businesses are using cyber security technologies more and more to upgrade their operations. These businesses are prone to hazards and cyber security breaches because to the very specialized characteristics of such settings, including their sensitive exchange of cyber security data and the weak design of connected devices. Our main goal is to develop a cyber security system that can take into account all potential forms of assaults while staying within the allocated budget. To achieve this, a financial strategy based on portfolio management is utilized by enabling the selection of a portfolio o

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Zaki Abdullah, Mahmood, Ali Kalid Jassim, Fadia Noori Hummadi, and Mohammed Majid M. Al Khalidy. "NEW STRATEGIES FOR IMPROVING NETWORK SECURITY AGAINST CYBER ATTACK BASED ON INTELLIGENT ALGORITHMS." Journal of Engineering and Sustainable Development 28, no. 3 (2024): 342–54. http://dx.doi.org/10.31272/jeasd.28.3.4.

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Gradually, since the number of linked computer systems that use networks linked to the Internet is raised the information that is delivered through those systems becomes more vulnerable to cyber threats. This article presents proposed algorithms based on Machine Learning (ML) that ensure early detection of cyber threats that cause network breaking through the use of the Correlation Ranking Filter feature selection method. These proposed algorithms were applied to the Multi-Step Cyber-Attack Dataset (MSCAD) which consists of 66 features. The proposed strategy will apply machine learning algorit

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KACHYNSKYI, A., D. VARYCHEVA, and S. SVYRYDENKO. "Quantitative assessments of cyber security system priorities." INFORMATION AND LAW, no. 2(17) (June 29, 2016): 114–26. http://dx.doi.org/10.37750/2616-6798.2016.2(17).272912.

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The algorithm of a choice of rational cyber security structure with elements of potential cyber threats in a high probability of their realization is proposed. The algorithm is based on the analysis of hierarchical structures. The article discusses the basic cyber threats and evaluation of local and global priorities of cyber security.

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Son, Vu Ngoc. "Optimizing Network Anomaly Detection Based on Network Traffic." International Journal of Emerging Technology and Advanced Engineering 11, no. 11 (2021): 53–60. http://dx.doi.org/10.46338/ijetae1121_07.

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Cyber-attack is a very hot topic today. Nowadays, systems must always be connected to the internet, and network infrastructure keeps growing in both scale and complexity. Therefore, the problem of detecting and warning cyber-attacks is now very urgent. To improve the effectiveness of detecting cyber-attacks, many methods and techniques were applied. In this paper, we propose to apply two methods of optimizing cyber-attack detection based on the IDS 2018 dataset using Principal Component Analysis (PCA) and machine learning algorithms. In the experimental section, we compare and evaluate the eff

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Amin, Reham, Ghada El-Taweel, Ahmed Fouad Ali, and Mohamed Tahoun. "A Hybrid Extreme Gradient Boosting and Long Short-Term Memory Algorithm for Cyber Threats Detection." MENDEL 29, no. 2 (2023): 307–22. http://dx.doi.org/10.13164/mendel.2023.2.307.

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The vast amounts of data, lack of scalability, and low detection rates of traditional intrusion detection technologies make it impossible to keep up with evolving and increasingly sophisticated cyber threats. Therefore, there is an urgent need to detect and stop cyber threats early. Deep Learning has greatly improved intrusion detection due to its ability to self-learn and extract highly accurate features. In this paper, a Hybrid XG Boosted and Long Short-Term Memory algorithm (HXGBLSTM) is proposed. A comparative analysis is conducted between the computational performance of six established e

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Jin, Zengwang, Menglu Ma, Shuting Zhang, Yanyan Hu, Yanning Zhang, and Changyin Sun. "Secure State Estimation of Cyber-Physical System under Cyber Attacks: Q-Learning vs. SARSA." Electronics 11, no. 19 (2022): 3161. http://dx.doi.org/10.3390/electronics11193161.

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This paper proposes a reinforcement learning (RL) algorithm for the security problem of state estimation of cyber-physical system (CPS) under denial-of-service (DoS) attacks. The security of CPS will inevitably decline when faced with malicious cyber attacks. In order to analyze the impact of cyber attacks on CPS performance, a Kalman filter, as an adaptive state estimation technology, is combined with an RL method to evaluate the issue of system security, where estimation performance is adopted as an evaluation criterion. Then, the transition of estimation error covariance under a DoS attack

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Korystin, Оleksandr, Svyrydiuk Nataliia, and Olena Mitina. "Risk Forecasting of Data Confidentiality Breach Using Linear Regression Algorithm." International Journal of Computer Network and Information Security 14, no. 4 (2022): 1–13. http://dx.doi.org/10.5815/ijcnis.2022.04.01.

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The paper focuses on the study of cyber security in Ukraine and creation of a predictive model for reducing the risk of identified cyber threats. Forecasting is performed using a linear regression model, taking into account the optimal dependence of specific threats in the field of cyber security of Ukraine on variables characterizing capabilities / vulnerabilities of cyber security. An unique empirical base was used for the analysis, which was formed on the basis of an expert survey of the cyber security system’s subjects in Ukraine. In order to increase the representativeness of the research

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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

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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 algorit

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Wan Ahmad Ramzi W.Y, Aznizah Ab.Karim, Mohamad Shaufi Kambaruddin, Afzanizam Alias, and Mohd Faizal Yahaya. "Formulating Tracing and Linking Algorithm For Identifying Cyber Terrorism Website." International Journal of Mechanical, Electrical and Civil Engineering 2, no. 2 (2025): 25–34. https://doi.org/10.61132/ijmecie.v2i2.266.

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Nowadays, the number of cyber terrorists that using Internet as a medium keep increasing around the world are really worrying. Even though the cyber terrorist is publishing their post openly and public, there are quite difficult to recognize their main communication media. This happen because the cyber terrorist might hide their agenda through any sympathetic base website. Therefore, the aim of this project is to formulate tracing and linking algorithm using data mining technique in order to identify the relationship between each cyber terrorism components that may result to cyber terrorism we

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Kolosok, Irina, and Liudmila Gurina. "Assessment of Cyber Resilience Indices of Information Collection and Processing Systems in Electric Power Systems Based on Semi-Markov Models." Voprosy kiberbezopasnosti, no. 6(46) (2021): 2–11. http://dx.doi.org/10.21681/2311-3456-2021-6-2-11.

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Purpose of the study: The study aims to design an algorithm for determining the cyber resilience indices of information collection, transmission, and processing systems (SCADA, WAMS) to control electric power systems. This algorithm makes it possible to factor in possible states and measures to restore such systems when cyber resilience is lost. Research methods include the probability theory, methods of power system reliability analysis, and Markov methods. Result of the research: The analysis of the reliability of WAMS, which is necessary for assessing the cyber resilience of the EPS, has be

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Charnanand, Dr Bhumika, and Chetan Rathod. "Various Cyber - Attack on Encryption Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 1929–32. http://dx.doi.org/10.22214/ijraset.2023.55512.

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Abstract: With the rapid advancement of technology across various fields, the significance of security has grown exponentially. As information is transmitted over networks or stored in devices, the question of data security has become a critical aspect of technology. To address this concern, data encryption is employed, wherein the data is encoded in a secure manner. However, the pressing question remains: can data truly be safeguarded against all cyber-attacks? In order to encrypt data, different algorithms are utilized, each with its own unique characteristics and level of security. The purp

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Premsai, Ranga. "IMPLEMENTING IDENTITY AND ACCESS MANAGEMENT FOR IMPROVING CYBER SECURITY." International Scientific Journal of Engineering and Management 03, no. 12 (2024): 1–7. https://doi.org/10.55041/isjem01283.

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Smart Banking Cyber-Physical Systems (SBCPS) face significant risks from various forms of financial fraud, including suspicious transactions, forgery, and identity theft. Traditional fraud detection methods often struggle to provide timely and reliable solutions. To address this, this study proposes an advanced fraud detection system based on a blockchain-enabled smart contract framework, integrating dual cryptographic algorithms: the Secure Asymmetric Hash Encryption Algorithm (SAHEA) and the Triple Integrity Twofish Algorithm (TITFA). The SAHEA ensures secure transaction data encryption usin

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Desamsetti, Harshith. "Utilizing Deep Learning to Identify Potentially Dangerous Routing Attacks in the IoT." ABC Journal of Advanced Research 11, no. 2 (2022): 103–14. http://dx.doi.org/10.18034/abcjar.v11i2.699.

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Due to the rapid increase of cyber-security difficulties brought about by sophisticated assaults such as data injection attacks, replay attacks, etc., the design of cyber-attack detection and control systems has emerged as an essential subfield within cyber-physical systems (CPSs) during the past few years. The outcome of these attacks could be a system failure, malfunctioning, or other undesirable effects. Consequently, it may be necessary to implement the cyber defense system in preparation for impending CPSs to have an improved security system. The various cyber-attack detection schemes bas

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Sharman, Ken, Javier Coronel, and Sergio Sáez. "Design Space Exploration using Evolutionary Optimisation in Cyber-Physical Systems: A Smart Port Case Study." ACM SIGAda Ada Letters 44, no. 2 (2025): 76–80. https://doi.org/10.1145/3742939.3742952.

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This work discusses using evolutionary optimisation algorithms at the design stage of cyber-physical systems. We present a parametrized model for a complex, heterogeneous, distributed cyber-physical system. This model is then used to calculate certain performance metrics of the system which will assist the design engineer. The optimisation of the system structure and its parameters is then performed by an evolutionary algorithm.

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Note, Johan, and Maaruf Ali. "Comparative Analysis of Intrusion Detection System Using Machine Learning and Deep Learning Algorithms." Annals of Emerging Technologies in Computing 6, no. 3 (2022): 19–36. http://dx.doi.org/10.33166/aetic.2022.03.003.

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Attacks against computer networks, “cyber-attacks”, are now common place affecting almost every Internet connected device on a daily basis. Organisations are now using machine learning and deep learning to thwart these types of attacks for their effectiveness without the need for human intervention. Machine learning offers the biggest advantage in their ability to detect, curtail, prevent, recover and even deal with untrained types of attacks without being explicitly programmed. This research will show the many different types of algorithms that are employed to fight against the different type

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Prasetyo, Stefanus Eko, Haeruddin Haeruddin, and Arron . "Phishing Website Detection Using the Decision Tree Algorithm Method." Jurnal E-Komtek (Elektro-Komputer-Teknik) 8, no. 2 (2024): 454–65. https://doi.org/10.37339/e-komtek.v8i2.2167.

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Along with the increasing number of internet users and the rapid development of technology, cyber security threats are becoming more complex, including phishing threats that often cause major losses such as loss of individual or corporate privacy. This study aims to identify phishing websites effectively through the application of machine learning algorithms. The dataset used in this study comes from the UCI learning repository developed by the University of Huddersfield. The research methodology includes the stages of problem identification, Cart algorithm collection, validation, and model ev

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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

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Jena, Tamanna, Achyut Shankar, and Adyesha Singhdeo. "Harnessing Machine Learning for Effective Cyber security Classifiers." Asian Journal of Research in Computer Science 16, no. 4 (2023): 453–64. http://dx.doi.org/10.9734/ajrcos/2023/v16i4405.

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Machine learning has emerged as a transformative force, innovating diverse industries through its capacity to infuse meaningful insights from large datasets. It plays a pivotal role in powering data analysis, discover pattern matching, identifying hidden or evolving risks in securing systems. The ability of categorizing and behavior analysis is central to its efficacy in cybersecurity. This paper highlights the importance of machine learning in landscape of cyber threats. In this paper, we have identified few machine learning algorithms to categorize huge dataset. The complexities of identifyi

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Rouhani, Seyed Hossein, Hamed Mojallali, and Alfred Baghramian. "Load frequency control in the presence of simultaneous cyber-attack and participation of demand response program." Transactions of the Institute of Measurement and Control 44, no. 10 (2022): 1993–2011. http://dx.doi.org/10.1177/01423312211068645.

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Simultaneous investigation of demand response programs and false data injection cyber-attack are critical issues for the smart power system frequency regulation. To this purpose, in this paper, the output of the studied system is simultaneously divided into two subsystems: one part including false data injection cyder-attack and another part without cyder-attack. Then, false data injection cyber-attack and load disturbance are estimated by a non-linear sliding mode observer, simultaneously and separately. After that, demand response is incorporated in the uncertain power system to compensate t

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Zaki, Ahmed Mohamed, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Marwa M. Eid, and El-Sayed M. El El-Kenawy. "Metaheuristic Optimization for Enhancing Cyber Security Index Prediction: A DTO+FGW Approach with MLP Integration." International Journal of Advances in Applied Computational Intelligence 4, no. 2 (2023): 15–25. http://dx.doi.org/10.54216/ijaaci.040202.

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In the realm of cybersecurity, the evaluation and enhancement of cyber resilience are paramount to safeguarding nations and organizations against evolving digital threats. This paper introduces a novel approach that integrates the Dipper Throated Algorithm (DTO) and the Grey Wolf Optimizer (GWO) to fortify the analysis of Cyber Security Indexes. These indexes encompass vital metrics, including the Cybersecurity Exposure Index (CEI), Global Cyber Security Index (GCI), National Cyber Security Index (NCSI), and Digital Development Level (DDL). Leveraging the adaptive nature of DTO and the collabo

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Achar, Sandesh, Nuruzzaman Faruqui, Md Whaiduzzaman, Albara Awajan, and Moutaz Alazab. "Cyber-Physical System Security Based on Human Activity Recognition through IoT Cloud Computing." Electronics 12, no. 8 (2023): 1892. http://dx.doi.org/10.3390/electronics12081892.

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Cyber-physical security is vital for protecting key computing infrastructure against cyber attacks. Individuals, corporations, and society can all suffer considerable digital asset losses due to cyber attacks, including data loss, theft, financial loss, reputation harm, company interruption, infrastructure damage, ransomware attacks, and espionage. A cyber-physical attack harms both digital and physical assets. Cyber-physical system security is more challenging than software-level cyber security because it requires physical inspection and monitoring. This paper proposes an innovative and effec

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Jagdish F. Pimple, Et al. "Elevating Security Measures in Cyber-Physical Systems: Deep Neural Network-Based Anomaly Detection with Ethereum Blockchain for Enhanced Data Integrity." Journal of Electrical Systems 19, no. 2 (2024): 105–15. http://dx.doi.org/10.52783/jes.696.

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The rapid development of physical device-based data collection in emerging technology needs smart, secure, and intelligent transmission. Cyber physical systems compete with the requirement of intelligent transmission of data. In cyber physical systems, security is a very challenging task due to the heterogeneous connections of devices in real time. This paper proposes a novel methodology for cyber-attack finding in cyber physical systems. The proposed system employed a DNN-deep neural network for the categorization of normal and attack data. The employed deep neural network design for 4 hidden

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Gupta, Amit. "Security Algorithm Design and Development for Cyber-Physical Systems against Cyber-Attacks." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 9, no. 3 (2018): 1170–77. http://dx.doi.org/10.17762/turcomat.v9i3.13908.

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The term "cyber physical system" (CPS) refers to the increasingly common practise of embedding internet connectivity and sensing/transmitting capabilities into everyday things. Think about a smart home app that uses CPS gadgets. Due to its many benefits, such as saving time and money and improving human comfort and energy efficiency, the IoT has been more popular in recent years. The cyber physical system relies heavily on the low-capacity sensor node. To function as clients or hosts on the internet, these diverse components communicate with one another across a wireless network. Due to resour

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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. I

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Rameshwar Saraf, Kundankumar, and P. Malathi. "Security enhancement of cyber-physical system using modified encryption AESGNRSA technique." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 1177. http://dx.doi.org/10.11591/ijeecs.v33.i2.pp1177-1185.

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<span>A cyber-physical system (CPS) is a combination of physical components with computational elements to interact with the physical world. The integration of these two systems has led to an increase in security concerns. Traditional encryption algorithms designed for general-purpose computing environments may not adequately address the distinct challenges of CPS, such as limited processing power, delay, and resource-constrained hardware. Therefore, there is a pressing need to develop an encryption algorithm that is optimized for CPS security without compromising the critical real-time

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Saraf, Kundankumar Rameshwar, and P. Malathi. "Security enhancement of cyber-physical system using modified encryption AESGNRSA technique." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 1177–85. https://doi.org/10.11591/ijeecs.v33.i2.pp1177-1185.

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A cyber-physical system (CPS) is a combination of physical components with computational elements to interact with the physical world. The integration of these two systems has led to an increase in security concerns. Traditional encryption algorithms designed for general-purpose computing environments may not adequately address the distinct challenges of CPS, such as limited processing power, delay, and resource-constrained hardware. Therefore, there is a pressing need to develop an encryption algorithm that is optimized for CPS security without compromising the critical real-time aspects of t

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Desianty, Annisa, and Imelda Imelda. "Systematic Literature Review: Cybersecurity by Utilizing Cryptography Using the Data Encryption Standard (DES) Algorithm." JURNAL TEKNIK INFORMATIKA 17, no. 1 (2024): 30–39. http://dx.doi.org/10.15408/jti.v17i1.37256.

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The world of information technology is currently developing very rapidly. This opens up opportunities in the development of computer applications, but it also creates opportunities for threats to alter and steal data or what is often known as cyber-crime. This action is a violation that can cause direct or indirect losses. Therefore, cyber-security is very important to protect user information from cyber-crime. Based on this description, this research will conduct a Systematic Literature Review (SLR) on cyber-security by utilizing cryptography using the DES algorithm. By using the SLR method,

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Oh, Sang Ho, Jeongyoon Kim, Jae Hoon Nah, and Jongyoul Park. "Employing Deep Reinforcement Learning to Cyber-Attack Simulation for Enhancing Cybersecurity." Electronics 13, no. 3 (2024): 555. http://dx.doi.org/10.3390/electronics13030555.

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In the current landscape where cybersecurity threats are escalating in complexity and frequency, traditional defense mechanisms like rule-based firewalls and signature-based detection are proving inadequate. The dynamism and sophistication of modern cyber-attacks necessitate advanced solutions that can evolve and adapt in real-time. Enter the field of deep reinforcement learning (DRL), a branch of artificial intelligence that has been effectively tackling complex decision-making problems across various domains, including cybersecurity. In this study, we advance the field by implementing a DRL

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Saleh, Hadeel M., Abdulrahman Kareem Oleiwi, and Ahmed Abed Hwaidi Abed. "Detecting attacks in banks by cyber security: an applied study." Babylonian Journal of Machine Learning 2023 (November 14, 2023): 65–72. https://doi.org/10.58496/bjml/2023/011.

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In the present era, digital banks are more defenseless to cyber banks due to their banking responses. With the increasing reliance on the maximum in digital business operations, these targets have become attractive targets for people who want to exploit personal and financial information. This aims to explore how cyber security can be used to reveal passwords in banks. By using several choices such as usage detection (IDS), behavior study, and learning algorithms, this study is current in identifying chances before major damages. By studying the CICIDS 2017 dataset, we highlight this study on

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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 res

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Agrawal, Aniruddha, and Shaik Affijulla. "A concept for discrimination of electrical fault from cyber attack in smart electric grid." Journal of Electrical Engineering 73, no. 4 (2022): 299–304. http://dx.doi.org/10.2478/jee-2022-0039.

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Abstract This letter proposes a concept to discriminate an electrical fault from a cyber attack in the modern power system. A cyber attack factor is introduced which may mislead the bus voltage stability virtually at load buses. The proposed cyber attack models are validated by executing multiple cyber attacks at a time on Western system coordinating council (WSCC) 9 bus test power system by using Siemens PSS/E and MATLAB softwares. Further, the impact of electrical fault and cyber attack on the WSCC 9 bus test power systems voltage stability has been analysed to develop a discrimination algor

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Rai, Khushbu, and Dr Megha Kamble. "Reviewing a New Optimized an ANFIS-Based Framework for Detecting Intrusion Detection System with Machine Learning Algorithms (Deep Learning Algorithm)." International Journal of Engineering and Advanced Technology 12, no. 3 (2023): 35–42. http://dx.doi.org/10.35940/ijeat.b3916.0212323.

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Today's world is becoming more interconnected due to the current global internet, communication, or ways of doing business that have recently shifted to cloud computing platforms in order to increase their speed and productivity. But such can also be affected by cyber attacks on cloud infrastructure services to be executed on various cloud platforms, increasing the number of attacks on such systems to neutralize any harm caused by a cyber attack on such cloud-based infrastructure. Although network administrators utilize intrusion detection systems (IDS) to detect threats and anomalies, they fr

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Khushbu, Rai, and Megha Kamble Dr. "Reviewing a New Optimized an ANFIS-Based Framework for Detecting Intrusion Detection System with Machine Learning Algorithms (Deep Learning Algorithm)." International Journal of Engineering and Advanced Technology (IJEAT) 12, no. 3 (2023): 35–42. https://doi.org/10.35940/ijeat.B3916.0212323.

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<strong>Abstract:</strong> Today's world is becoming more interconnected due to the current global internet, communication, or ways of doing business that have recently shifted to cloud computing platforms in order to increase their speed and productivity. But such can also be affected by cyber attacks on cloud infrastructure services to be executed on various cloud platforms, increasing the number of attacks on such systems to neutralize any harm caused by a cyber attack on such cloud-based infrastructure. Although network administrators utilize intrusion detection systems (IDS) to detect

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Banasode, Ms Swapna S., Nidhi K. V, Sabhareesh Balaji P, Tanvi Kamath, and Tushar S. "Cyber Bullying." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 1057–60. http://dx.doi.org/10.22214/ijraset.2023.56676.

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Abstract: Cyberbullying involves the use of cell phones or social networking platforms like Facebook and Twitter to harass, threaten, or intimidate individuals, adversely impacting their mental health. Our project focuses on the detection of such harmful commentsusing a machine learning algorithm, categorizing them into two severity levels: "low" and "high." Given the ubiquitous nature of social media, especially in the post-pandemic era, it has become an integral part of our lives for communication across all age groups. While it facilitates easy communication, social media also presents chal

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Gurina, Liudmila. "Assessment of Cyber Resilience of Operational Dispatch Control System of EPS." Voprosy kiberbezopasnosti, no. 3(49) (2022): 23–31. http://dx.doi.org/10.21681/2311-3456-2022-3-23-31.

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The research objective is to develop an algorithm for assessing the cyber resilience of the operational dispatch control (ODC) system of electric power system (EPS) during cyberattacks on data collecting, processing, and transmitting systems. The research methods include the probabilistic methods, fuzzy set theory methods, and methods of EPS reliability analysis. Result of the research: the impact of cyberattacks on the functionality of the EPS ODC system is analyzed. The factors ensuring the cyber resilience of the EPS ODC system in the case of materialization of cyber threats are identified.

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Ranjane, Gayatri Dattatraya, Vishwa Hiteshkumar Joshi, and Priyanshi Lokesh Kumar Singhal. "Cyberattack Analysis, Detection and Prevention using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 1224–33. http://dx.doi.org/10.22214/ijraset.2024.60026.

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Abstract: In today's linked world, with the increase of cyberattacks, it is critical to have strong detection and prevention systems. This study presents an advanced approach utilizing both machine learning and deep learning algorithms for cyberattack detection and prevention. The UNSW-NB15 dataset, renowned for its comprehensive representation of diverse cyber threats, serves as the foundation for experimentation and evaluation. Several algorithms such as Random Forest, Naïve Bayes, boosting algorithms, Artificial neural networks, Support vector machine are employed where the comparative anal

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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 k

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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 st

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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 so

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Perumal, Senthilkumar, and Kumaresan Devarajan. "Comparative Performance Analysis of Machine Learning Algorithms: Random Cut Forest, Robust Random Cut Forest, and Amazon Sage Maker Random Cut Forest for Intrusion Detection Systems Using the CIS IDS 2017 Dataset." Turkish Journal of Engineering 9, no. 3 (2025): 535–34. https://doi.org/10.31127/tuje.1614930.

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Dynamic cyber threats are screaming for better anomaly detection techniques in Intrusion Detection Systems. Organizations today are hugely dependent on digital infrastructures for which effective security is priceless. The following research article does a critical and comparative analysis among three popular algorithms, namely Amazon Sage Maker Random Cut Forest, Robust Random Cut Forest, and traditional Random Cut Forest. Using the CIS IDS 2017 dataset with multifaceted network traffic features together with the labeled type of attack, this work rigorously tests the performance in anomaly de

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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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Almajed, Rasha, Amer Ibrahim, Abedallah Zaid Abualkishik, Nahia Mourad, and Faris A Almansour. "Using machine learning algorithm for detection of cyber-attacks in cyber physical systems." Periodicals of Engineering and Natural Sciences (PEN) 10, no. 3 (2022): 261. http://dx.doi.org/10.21533/pen.v10i3.3035.

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Alrowais, Fadwa, Heba G. Mohamed, Fahd N. Al-Wesabi, Mesfer Al Duhayyim, Anwer Mustafa Hilal, and Abdelwahed Motwakel. "Cyber attack detection in healthcare data using cyber-physical system with optimized algorithm." Computers and Electrical Engineering 108 (May 2023): 108636. http://dx.doi.org/10.1016/j.compeleceng.2023.108636.

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Almajed, Rasha, Amer Ibrahim, Abedallah Zaid Abualkishik, Nahia Mourad, and Faris A. Almansour. "Using machine learning algorithm for detection of cyber-attacks in cyber physical systems." Periodicals of Engineering and Natural Sciences (PEN) 10, no. 3 (2022): 261–75. https://doi.org/10.21533/pen.v10.i3.661.

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Network integration is common in cyber-physical systems (CPS) to allow for remote access, surveillance, and analysis. They have been exposed to cyberattacks because of their integration with an insecure network. In the event of a violation in internet security, an attacker was able to interfere with the system's functions, which might result in catastrophic consequences. As a result, detecting breaches into mission-critical CPS is a top priority. Detecting assaults on CPSs, which are increasingly being targeted by cyber criminals and cyber threats, is becoming increasingly difficult. It is pot

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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 conv

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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 ha

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Albakri, Ashwag, Bayan Alabdullah, and Fatimah Alhayan. "Blockchain-Assisted Machine Learning with Hybrid Metaheuristics-Empowered Cyber Attack Detection and Classification Model." Sustainability 15, no. 18 (2023): 13887. http://dx.doi.org/10.3390/su151813887.

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Cyber attack detection is the process of detecting and responding to malicious or unauthorized activities in networks, computer systems, and digital environments. The objective is to identify these attacks early, safeguard sensitive data, and minimize the potential damage. An intrusion detection system (IDS) is a cybersecurity tool mainly designed to monitor system activities or network traffic to detect and respond to malicious or suspicious behaviors that may indicate a cyber attack. IDSs that use machine learning (ML) and deep learning (DL) have played a pivotal role in helping organization

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