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  • Open Access

    ARTICLE

    Deep Learning Based Distributed Intrusion Detection in Secure Cyber Physical Systems

    P. Ramadevi1,*, K. N. Baluprithviraj2, V. Ayyem Pillai3, Kamalraj Subramaniam4

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 2067-2081, 2022, DOI:10.32604/iasc.2022.026377 - 25 May 2022

    Abstract Cyber Physical Systems (CPSs) are network systems containing cyber (computation, communication) and physical (sensors, actuators) components that interact with each other through feedback loop with the help of human intervention. The dynamic and disseminated characteristics of CPS environment makes it vulnerable to threats that exist in virtualization process. Due to this, several security issues are presented in CPS. In order to address the challenges, there is a need exists to extend the conventional security solutions such as Intrusion Detection Systems (IDS) to handle high speed network data traffic and adaptive network pattern in cloud. Additionally,… More >

  • Open Access

    ARTICLE

    Bio-inspired Hybrid Feature Selection Model for Intrusion Detection

    Adel Hamdan Mohammad1,*, Tariq Alwada’n2, Omar Almomani3, Sami Smadi3, Nidhal ElOmari4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 133-150, 2022, DOI:10.32604/cmc.2022.027475 - 18 May 2022

    Abstract Intrusion detection is a serious and complex problem. Undoubtedly due to a large number of attacks around the world, the concept of intrusion detection has become very important. This research proposes a multilayer bio-inspired feature selection model for intrusion detection using an optimized genetic algorithm. Furthermore, the proposed multilayer model consists of two layers (layers 1 and 2). At layer 1, three algorithms are used for the feature selection. The algorithms used are Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Firefly Optimization Algorithm (FFA). At the end of layer 1, a priority value… More >

  • Open Access

    ARTICLE

    Two-Dimensional Projection-Based Wireless Intrusion Classification Using Lightweight EfficientNet

    Muhamad Erza Aminanto1,2,*, Ibnu Rifqi Purbomukti3, Harry Chandra2, Kwangjo Kim4

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5301-5314, 2022, DOI:10.32604/cmc.2022.026749 - 21 April 2022

    Abstract Internet of Things (IoT) networks leverage wireless communication protocols, which adversaries can exploit. Impersonation attacks, injection attacks, and flooding are several examples of different attacks existing in Wi-Fi networks. Intrusion Detection System (IDS) became one solution to distinguish those attacks from benign traffic. Deep learning techniques have been intensively utilized to classify the attacks. However, the main issue of utilizing deep learning models is projecting the data, notably tabular data, into an image. This study proposes a novel projection from wireless network attacks data into a grid-based image for feeding one of the Convolutional Neural… More >

  • Open Access

    ARTICLE

    Optimal Machine Learning Enabled Intrusion Detection in Cyber-Physical System Environment

    Bassam A. Y. Alqaralleh1,*, Fahad Aldhaban1, Esam A. AlQarallehs2, Ahmad H. Al-Omari3

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4691-4707, 2022, DOI:10.32604/cmc.2022.026556 - 21 April 2022

    Abstract Cyber-attacks on cyber-physical systems (CPSs) resulted to sensing and actuation misbehavior, severe damage to physical object, and safety risk. Machine learning (ML) models have been presented to hinder cyberattacks on the CPS environment; however, the non-existence of labelled data from new attacks makes their detection quite interesting. Intrusion Detection System (IDS) is a commonly utilized to detect and classify the existence of intrusions in the CPS environment, which acts as an important part in secure CPS environment. Latest developments in deep learning (DL) and explainable artificial intelligence (XAI) stimulate new IDSs to manage cyberattacks with… More >

  • Open Access

    ARTICLE

    Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

    Masoud Alajmi1, Mohamed K. Nour2, Siwar Ben Haj Hassine3, Mimouna Abdullah Alkhonaini4, Manar Ahmed Hamza5,*, Ishfaq Yaseen5, Abu Sarwar Zamani5, Mohammed Rizwanullah5

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5499-5513, 2022, DOI:10.32604/cmc.2022.026187 - 21 April 2022

    Abstract Recently, cyber physical system (CPS) has gained significant attention which mainly depends upon an effective collaboration with computation and physical components. The greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems (CPES). At the same time, the rising ubiquity of wireless sensor networks (WSN) in several application areas makes it a vital part of the design of CPES. Since security and energy efficiency are the major challenging issues in CPES, this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms… More >

  • Open Access

    ARTICLE

    Wireless Intrusion Detection Based on Optimized LSTM with Stacked Auto Encoder Network

    S. Karthic1,*, S. Manoj Kumar2

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 439-453, 2022, DOI:10.32604/iasc.2022.025153 - 15 April 2022

    Abstract In recent years, due to the rapid progress of various technologies, wireless computer networks have developed. However, the activities of the security threats and attackers affect the data communication of these technologies. So, to protect the network against these security threats, an efficient IDS (Intrusion Detection System) is presented in this paper. Namely, optimized long short-term memory (OLSTM) network with a stacked auto-encoder (SAE) network is proposed as an IDS system. Using SAE, significant features are extracted from the databases such as input NSL-KDD database and the UNSW-NB15 database. Then extracted features are given as More >

  • Open Access

    ARTICLE

    Bat-Inspired Optimization for Intrusion Detection Using an Ensemble Forecasting Method

    R. Anand Babu1,*, S. Kannan2

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 307-323, 2022, DOI:10.32604/iasc.2022.024098 - 15 April 2022

    Abstract An Intrusion detection system (IDS) is extensively used to identify cyber-attacks preferably in real-time and to achieve integrity, confidentiality, and availability of sensitive information. In this work, we develop a novel IDS using machine learning techniques to increase the performance of the attack detection process. In order to cope with high dimensional feature-rich traffic in large networks, we introduce a Bat-Inspired Optimization and Correlation-based Feature Selection (BIOCFS) algorithm and an ensemble classification approach. The BIOCFS is introduced to estimate the correlation of the identified features and to choose the ideal subset for training and testing… More >

  • Open Access

    ARTICLE

    Hybrid Deep Learning Enabled Intrusion Detection in Clustered IIoT Environment

    Radwa Marzouk1, Fadwa Alrowais2, Noha Negm3, Mimouna Abdullah Alkhonaini4, Manar Ahmed Hamza5,*, Mohammed Rizwanullah5, Ishfaq Yaseen5, Abdelwahed Motwakel5

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3763-3775, 2022, DOI:10.32604/cmc.2022.027483 - 29 March 2022

    Abstract Industrial Internet of Things (IIoT) is an emerging field which connects digital equipment as well as services to physical systems. Intrusion detection systems (IDS) can be designed to protect the system from intrusions or attacks. In this view, this paper presents a novel hybrid deep learning with metaheuristics enabled intrusion detection (HDL-MEID) technique for clustered IIoT environments. The HDL-MEID model mainly intends to organize the IIoT devices into clusters and enabled secure communication. Primarily, the HDL-MEID technique designs a new chaotic mayfly optimization (CMFO) based clustering approach for the effective choice of the Cluster Heads More >

  • Open Access

    ARTICLE

    Optimized Artificial Neural Network Techniques to Improve Cybersecurity of Higher Education Institution

    Abdullah Saad AL-Malaise AL-Ghamdi1, Mahmoud Ragab2,3,4,*, Maha Farouk S. Sabir1, Ahmed Elhassanein5,6, Ashraf A. Gouda4

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3385-3399, 2022, DOI:10.32604/cmc.2022.026477 - 29 March 2022

    Abstract Education acts as an important part of economic growth and improvement in human welfare. The educational sectors have transformed a lot in recent days, and Information and Communication Technology (ICT) is an effective part of the education field. Almost every action in university and college, right from the process from counselling to admissions and fee deposits has been automated. Attendance records, quiz, evaluation, mark, and grade submissions involved the utilization of the ICT. Therefore, security is essential to accomplish cybersecurity in higher security institutions (HEIs). In this view, this study develops an Automated Outlier Detection… More >

  • Open Access

    ARTICLE

    Enhanced Artificial Intelligence-based Cybersecurity Intrusion Detection for Higher Education Institutions

    Abdullah S. AL-Malaise AL-Ghamdi1, Mahmoud Ragab2,3,4,*, Maha Farouk S. Sabir1

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2895-2907, 2022, DOI:10.32604/cmc.2022.026405 - 29 March 2022

    Abstract As higher education institutions (HEIs) go online, several benefits are attained, and also it is vulnerable to several kinds of attacks. To accomplish security, this paper presents artificial intelligence based cybersecurity intrusion detection models to accomplish security. The incorporation of the strategies into business is a tendency among several distinct industries, comprising education, have recognized as game changer. Consequently, the HEIs are highly related to the requirement and knowledge of the learner, making the education procedure highly effective. Thus, artificial intelligence (AI) and machine learning (ML) models have shown significant interest in HEIs. This study… More >

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