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  • Open Access


    Optimal Wavelet Neural Network-Based Intrusion Detection in Internet of Things Environment

    Heba G. Mohamed1, Fadwa Alrowais2, Mohammed Abdullah Al-Hagery3, Mesfer Al Duhayyim4,*, Anwer Mustafa Hilal5, Abdelwahed Motwakel5

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4467-4483, 2023, DOI:10.32604/cmc.2023.036822

    Abstract As the Internet of Things (IoT) endures to develop, a huge count of data has been created. An IoT platform is rather sensitive to security challenges as individual data can be leaked, or sensor data could be used to cause accidents. As typical intrusion detection system (IDS) studies can be frequently designed for working well on databases, it can be unknown if they intend to work well in altering network environments. Machine learning (ML) techniques are depicted to have a higher capacity at assisting mitigate an attack on IoT device and another edge system with reasonable accuracy. This article introduces… More >

  • Open Access


    An IoT Environment Based Framework for Intelligent Intrusion Detection

    Hamza Safwan1, Zeshan Iqbal1, Rashid Amin1, Muhammad Attique Khan2, Majed Alhaisoni3, Abdullah Alqahtani4, Ye Jin Kim5, Byoungchol Chang6,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2365-2381, 2023, DOI:10.32604/cmc.2023.033896

    Abstract Software-defined networking (SDN) represents a paradigm shift in network traffic management. It distinguishes between the data and control planes. APIs are then used to communicate between these planes. The controller is central to the management of an SDN network and is subject to security concerns. This research shows how a deep learning algorithm can detect intrusions in SDN-based IoT networks. Overfitting, low accuracy, and efficient feature selection is all discussed. We propose a hybrid machine learning-based approach based on Random Forest and Long Short-Term Memory (LSTM). In this study, a new dataset based specifically on Software Defined Networks is used… More >

  • Open Access


    Network Intrusion Detection Model Using Fused Machine Learning Technique

    Fahad Mazaed Alotaibi*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2479-2490, 2023, DOI:10.32604/cmc.2023.033792

    Abstract With the progress of advanced technology in the industrial revolution encompassing the Internet of Things (IoT) and cloud computing, cyberattacks have been increasing rapidly on a large scale. The rapid expansion of IoT and networks in many forms generates massive volumes of data, which are vulnerable to security risks. As a result, cyberattacks have become a prevalent and danger to society, including its infrastructures, economy, and citizens’ privacy, and pose a national security risk worldwide. Therefore, cyber security has become an increasingly important issue across all levels and sectors. Continuous progress is being made in developing more sophisticated and efficient… More >

  • Open Access


    Intrusion Detection Method Based on Active Incremental Learning in Industrial Internet of Things Environment

    Zeyong Sun1, Guo Ran2, Zilong Jin1,3,*

    Journal on Internet of Things, Vol.4, No.2, pp. 99-111, 2022, DOI:10.32604/jiot.2022.037416

    Abstract Intrusion detection is a hot field in the direction of network security. Classical intrusion detection systems are usually based on supervised machine learning models. These offline-trained models usually have better performance in the initial stages of system construction. However, due to the diversity and rapid development of intrusion techniques, the trained models are often difficult to detect new attacks. In addition, very little noisy data in the training process often has a considerable impact on the performance of the intrusion detection system. This paper proposes an intrusion detection system based on active incremental learning with the adaptive capability to solve… More >

  • Open Access


    Performance Analysis of Intrusion Detection System in the IoT Environment Using Feature Selection Technique

    Moody Alhanaya, Khalil Hamdi Ateyeh Al-Shqeerat*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3709-3724, 2023, DOI:10.32604/iasc.2023.036856

    Abstract The increasing number of security holes in the Internet of Things (IoT) networks creates a question about the reliability of existing network intrusion detection systems. This problem has led to the developing of a research area focused on improving network-based intrusion detection system (NIDS) technologies. According to the analysis of different businesses, most researchers focus on improving the classification results of NIDS datasets by combining machine learning and feature reduction techniques. However, these techniques are not suitable for every type of network. In light of this, whether the optimal algorithm and feature reduction techniques can be generalized across various datasets… More >

  • Open Access


    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

    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 defenders visit a system multiple… More >

  • Open Access


    An Improved Reptile Search Algorithm Based on Cauchy Mutation for Intrusion Detection

    Salahahaldeen Duraibi*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2509-2525, 2023, DOI:10.32604/csse.2023.036119

    Abstract With the growth of the discipline of digital communication, the topic has acquired more attention in the cybersecurity medium. The Intrusion Detection (ID) system monitors network traffic to detect malicious activities. The paper introduces a novel Feature Selection (FS) approach for ID. Reptile Search Algorithm (RSA)—is a new optimization algorithm; in this method, each agent searches a new region according to the position of the host, which makes the algorithm suffers from getting stuck in local optima and a slow convergence rate. To overcome these problems, this study introduces an improved RSA approach by integrating Cauchy Mutation (CM) into the… More >

  • Open Access


    Modified Buffalo Optimization with Big Data Analytics Assisted Intrusion Detection Model

    R. Sheeba1,*, R. Sharmila2, Ahmed Alkhayyat3, Rami Q. Malik4

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1415-1429, 2023, DOI:10.32604/csse.2023.034321

    Abstract Lately, the Internet of Things (IoT) application requires millions of structured and unstructured data since it has numerous problems, such as data organization, production, and capturing. To address these shortcomings, big data analytics is the most superior technology that has to be adapted. Even though big data and IoT could make human life more convenient, those benefits come at the expense of security. To manage these kinds of threats, the intrusion detection system has been extensively applied to identify malicious network traffic, particularly once the preventive technique fails at the level of endpoint IoT devices. As cyberattacks targeting IoT have… More >

  • Open Access


    Modified Garden Balsan Optimization Based Machine Learning for Intrusion Detection

    Mesfer Al Duhayyim1,*, Jaber S. Alzahrani2, Hanan Abdullah Mengash3, Mrim M. Alnfiai4, Radwa Marzouk3, Gouse Pasha Mohammed5, Mohammed Rizwanullah5, Amgad Atta Abdelmageed5

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1471-1485, 2023, DOI:10.32604/csse.2023.034137

    Abstract The Internet of Things (IoT) environment plays a crucial role in the design of smart environments. Security and privacy are the major challenging problems that exist in the design of IoT-enabled real-time environments. Security susceptibilities in IoT-based systems pose security threats which affect smart environment applications. Intrusion detection systems (IDS) can be used for IoT environments to mitigate IoT-related security attacks which use few security vulnerabilities. This paper introduces a modified garden balsan optimization-based machine learning model for intrusion detection (MGBO-MLID) in the IoT cloud environment. The presented MGBO-MLID technique focuses on the identification and classification of intrusions in the… More >

  • Open Access


    Internet of Things Intrusion Detection System Based on Convolutional Neural Network

    Jie Yin1,2,3,*, Yuxuan Shi1, Wen Deng1, Chang Yin1, Tiannan Wang1, Yuchen Song1, Tianyao Li1, Yicheng Li1

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2119-2135, 2023, DOI:10.32604/cmc.2023.035077

    Abstract In recent years, the Internet of Things (IoT) technology has developed by leaps and bounds. However, the large and heterogeneous network structure of IoT brings high management costs. In particular, the low cost of IoT devices exposes them to more serious security concerns. First, a convolutional neural network intrusion detection system for IoT devices is proposed. After cleaning and preprocessing the NSL-KDD dataset, this paper uses feature engineering methods to select appropriate features. Then, based on the combination of DCNN and machine learning, this paper designs a cloud-based loss function, which adopts a regularization method to prevent overfitting. The model… More >

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