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  • Open Access

    ARTICLE

    An Enhanced Intelligent Intrusion Detection System to Secure E-Commerce Communication Systems

    Adil Hussain1, Kashif Naseer Qureshi2,*, Khalid Javeed3, Musaed Alhussein4

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2513-2528, 2023, DOI:10.32604/csse.2023.040305

    Abstract Information and communication technologies are spreading rapidly due to their fast proliferation in many fields. The number of Internet users has led to a spike in cyber-attack incidents. E-commerce applications, such as online banking, marketing, trading, and other online businesses, play an integral role in our lives. Network Intrusion Detection System (NIDS) is essential to protect the network from unauthorized access and against other cyber-attacks. The existing NIDS systems are based on the Backward Oracle Matching (BOM) algorithm, which minimizes the false alarm rate and causes of high packet drop ratio. This paper discussed the More >

  • Open Access

    ARTICLE

    Improved Ant Colony Optimization and Machine Learning Based Ensemble Intrusion Detection Model

    S. Vanitha1,*, P. Balasubramanie2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 849-864, 2023, DOI:10.32604/iasc.2023.032324

    Abstract Internet of things (IOT) possess cultural, commercial and social effect in life in the future. The nodes which are participating in IOT network are basically attracted by the cyber-attack targets. Attack and identification of anomalies in IoT infrastructure is a growing problem in the IoT domain. Machine Learning Based Ensemble Intrusion Detection (MLEID) method is applied in order to resolve the drawback by minimizing malicious actions in related botnet attacks on Message Queue Telemetry Transport (MQTT) and Hyper-Text Transfer Protocol (HTTP) protocols. The proposed work has two significant contributions which are a selection of features… More >

  • Open Access

    ARTICLE

    Anomaly Classification Using Genetic Algorithm-Based Random Forest Model for Network Attack Detection

    Adel Assiri*

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 767-778, 2021, DOI:10.32604/cmc.2020.013813

    Abstract Anomaly classification based on network traffic features is an important task to monitor and detect network intrusion attacks. Network-based intrusion detection systems (NIDSs) using machine learning (ML) methods are effective tools for protecting network infrastructures and services from unpredictable and unseen attacks. Among several ML methods, random forest (RF) is a robust method that can be used in ML-based network intrusion detection solutions. However, the minimum number of instances for each split and the number of trees in the forest are two key parameters of RF that can affect classification accuracy. Therefore, optimal parameter selection… More >

  • Open Access

    ARTICLE

    High Speed Network Intrusion Detection System (NIDS) Using Low Power Precomputation Based Content Addressable Memory

    R. Mythili1, *, P. Kalpana2

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1097-1107, 2020, DOI:10.32604/cmc.2020.08396

    Abstract NIDS (Network Intrusion Detection Systems) plays a vital role in security threats to computers and networks. With the onset of gigabit networks, hardware-based Intrusion Detection System gains popularity because of its high performance when compared to the software-based NIDS. The software-based system limits parallel execution, which in turn confines the performance of a modern network. This paper presents a signature-based lookup technique using reconfigurable hardware. Content Addressable Memory (CAM) is used as a lookup table architecture to improve the speed instead of search algorithms. To minimize the power and to increase the speed, precomputation based… More >

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