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Search Results (19)
  • Open Access

    REVIEW

    A Review of Generative Adversarial Networks for Intrusion Detection Systems: Advances, Challenges, and Future Directions

    Monirah Al-Ajlan*, Mourad Ykhlef

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2053-2076, 2024, DOI:10.32604/cmc.2024.055891 - 18 November 2024

    Abstract The ever-growing network traffic threat landscape necessitates adopting accurate and robust intrusion detection systems (IDSs). IDSs have become a research hotspot and have seen remarkable performance improvements. Generative adversarial networks (GANs) have also garnered increasing research interest recently due to their remarkable ability to generate data. This paper investigates the application of (GANs) in (IDS) and explores their current use within this research field. We delve into the adoption of GANs within signature-based, anomaly-based, and hybrid IDSs, focusing on their objectives, methodologies, and advantages. Overall, GANs have been widely employed, mainly focused on solving the More >

  • Open Access

    ARTICLE

    Multi-Binary Classifiers Using Optimal Feature Selection for Memory-Saving Intrusion Detection Systems

    Ye-Seul Kil1,#, Yu-Ran Jeon1,#, Sun-Jin Lee1, Il-Gu Lee1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1473-1493, 2024, DOI:10.32604/cmes.2024.052637 - 27 September 2024

    Abstract With the rise of remote work and the digital industry, advanced cyberattacks have become more diverse and complex in terms of attack types and characteristics, rendering them difficult to detect with conventional intrusion detection methods. Signature-based intrusion detection methods can be used to detect attacks; however, they cannot detect new malware. Endpoint detection and response (EDR) tools are attracting attention as a means of detecting attacks on endpoints in real-time to overcome the limitations of signature-based intrusion detection techniques. However, EDR tools are restricted by the continuous generation of unnecessary logs, resulting in poor detection… More >

  • Open Access

    ARTICLE

    A New Solution to Intrusion Detection Systems Based on Improved Federated-Learning Chain

    Chunhui Li1,*, Hua Jiang2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4491-4512, 2024, DOI:10.32604/cmc.2024.048431 - 20 June 2024

    Abstract In the context of enterprise systems, intrusion detection (ID) emerges as a critical element driving the digital transformation of enterprises. With systems spanning various sectors of enterprises geographically dispersed, the necessity for seamless information exchange has surged significantly. The existing cross-domain solutions are challenged by such issues as insufficient security, high communication overhead, and a lack of effective update mechanisms, rendering them less feasible for prolonged application on resource-limited devices. This study proposes a new cross-domain collaboration scheme based on federated chains to streamline the server-side workload. Within this framework, individual nodes solely engage in… More >

  • Open Access

    ARTICLE

    A Comprehensive Analysis of Datasets for Automotive Intrusion Detection Systems

    Seyoung Lee1, Wonsuk Choi1, Insup Kim2, Ganggyu Lee2, Dong Hoon Lee1,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3413-3442, 2023, DOI:10.32604/cmc.2023.039583 - 08 October 2023

    Abstract Recently, automotive intrusion detection systems (IDSs) have emerged as promising defense approaches to counter attacks on in-vehicle networks (IVNs). However, the effectiveness of IDSs relies heavily on the quality of the datasets used for training and evaluation. Despite the availability of several datasets for automotive IDSs, there has been a lack of comprehensive analysis focusing on assessing these datasets. This paper aims to address the need for dataset assessment in the context of automotive IDSs. It proposes qualitative and quantitative metrics that are independent of specific automotive IDSs, to evaluate the quality of datasets. These… More >

  • Open Access

    ARTICLE

    Multi-Attack Intrusion Detection System for Software-Defined Internet of Things Network

    Tarcízio Ferrão1,*, Franklin Manene2, Adeyemi Abel Ajibesin3

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4985-5007, 2023, DOI:10.32604/cmc.2023.038276 - 29 April 2023

    Abstract Currently, the Internet of Things (IoT) is revolutionizing communication technology by facilitating the sharing of information between different physical devices connected to a network. To improve control, customization, flexibility, and reduce network maintenance costs, a new Software-Defined Network (SDN) technology must be used in this infrastructure. Despite the various advantages of combining SDN and IoT, this environment is more vulnerable to various attacks due to the centralization of control. Most methods to ensure IoT security are designed to detect Distributed Denial-of-Service (DDoS) attacks, but they often lack mechanisms to mitigate their severity. This paper proposes… More >

  • Open Access

    ARTICLE

    Adaptive Cyber Defense Technique Based on Multiagent Reinforcement Learning Strategies

    Adel Alshamrani1,*, Abdullah Alshahrani2

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2757-2771, 2023, DOI:10.32604/iasc.2023.032835 - 15 March 2023

    Abstract The static nature of cyber defense systems gives attackers a sufficient amount of time to explore and further exploit the vulnerabilities of information technology systems. In this paper, we investigate a problem where multiagent systems sensing and acting in an environment contribute to adaptive cyber defense. We present a learning strategy that enables multiple agents to learn optimal policies using multiagent reinforcement learning (MARL). Our proposed approach is inspired by the multiarmed bandits (MAB) learning technique for multiple agents to cooperate in decision making or to work independently. We study a MAB approach in which More >

  • Open Access

    ARTICLE

    Detection of Abnormal Network Traffic Using Bidirectional Long Short-Term Memory

    Nga Nguyen Thi Thanh, Quang H. Nguyen*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 491-504, 2023, DOI:10.32604/csse.2023.032107 - 20 January 2023

    Abstract Nowadays, web systems and servers are constantly at great risk from cyberattacks. This paper proposes a novel approach to detecting abnormal network traffic using a bidirectional long short-term memory (LSTM) network in combination with the ensemble learning technique. First, the binary classification module was used to detect the current abnormal flow. Then, the abnormal flows were fed into the multilayer classification module to identify the specific type of flow. In this research, a deep learning bidirectional LSTM model, in combination with the convolutional neural network and attention technique, was deployed to identify a specific attack. More >

  • Open Access

    ARTICLE

    Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

    Reem Alkanhel1,*, Doaa Sami Khafaga2, El-Sayed M. El-kenawy3, Abdelaziz A. Abdelhamid4,5, Abdelhameed Ibrahim6, Rashid Amin7, Mostafa Abotaleb8, B. M. El-den6

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2695-2709, 2023, DOI:10.32604/cmc.2023.033153 - 31 October 2022

    Abstract The Internet of Things (IoT) is a modern approach that enables connection with a wide variety of devices remotely. Due to the resource constraints and open nature of IoT nodes, the routing protocol for low power and lossy (RPL) networks may be vulnerable to several routing attacks. That’s why a network intrusion detection system (NIDS) is needed to guard against routing assaults on RPL-based IoT networks. The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks. Therefore, we propose in this paper… More >

  • Open Access

    REVIEW

    Machine Learning Techniques for Intrusion Detection Systems in SDN-Recent Advances, Challenges and Future Directions

    Gulshan Kumar1,*, Hamed Alqahtani2

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 89-119, 2023, DOI:10.32604/cmes.2022.020724 - 24 August 2022

    Abstract Software-Defined Networking (SDN) enables flexibility in developing security tools that can effectively and efficiently analyze and detect malicious network traffic for detecting intrusions. Recently Machine Learning (ML) techniques have attracted lots of attention from researchers and industry for developing intrusion detection systems (IDSs) considering logically centralized control and global view of the network provided by SDN. Many IDSs have developed using advances in machine learning and deep learning. This study presents a comprehensive review of recent work of ML-based IDS in context to SDN. It presents a comprehensive study of the existing review papers in More >

  • Open Access

    ARTICLE

    A Novel MegaBAT Optimized Intelligent Intrusion Detection System in Wireless Sensor Networks

    G. Nagalalli*, G. Ravi

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 475-490, 2023, DOI:10.32604/iasc.2023.026571 - 06 June 2022

    Abstract Wireless Sensor Network (WSN), which finds as one of the major components of modern electronic and wireless systems. A WSN consists of numerous sensor nodes for the discovery of sensor networks to leverage features like data sensing, data processing, and communication. In the field of medical health care, these network plays a very vital role in transmitting highly sensitive data from different geographic regions and collecting this information by the respective network. But the fear of different attacks on health care data typically increases day by day. In a very short period, these attacks may… More >

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