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  • Open Access

    ARTICLE

    Hybrid Metaheuristics Feature Selection with Stacked Deep Learning-Enabled Cyber-Attack Detection Model

    Mashael M Asiri1, Heba G. Mohamed2, Mohamed K Nour3, Mesfer Al Duhayyim4,*, Amira Sayed A. Aziz5, Abdelwahed Motwakel6, Abu Sarwar Zamani6, Mohamed I. Eldesouki7

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1679-1694, 2023, DOI:10.32604/csse.2023.031063 - 03 November 2022

    Abstract Due to exponential increase in smart resource limited devices and high speed communication technologies, Internet of Things (IoT) have received significant attention in different application areas. However, IoT environment is highly susceptible to cyber-attacks because of memory, processing, and communication restrictions. Since traditional models are not adequate for accomplishing security in the IoT environment, the recent developments of deep learning (DL) models find beneficial. This study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection (HMFS-SDLCAD) model. The major intention of the HMFS-SDLCAD model is to recognize the occurrence of cyberattacks… More >

  • Open Access

    ARTICLE

    DoS Attack Detection Based on Deep Factorization Machine in SDN

    Jing Wang1, Xiangyu Lei1, Qisheng Jiang1, Osama Alfarraj2, Amr Tolba2, Gwang-jun Kim3,*

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1727-1742, 2023, DOI:10.32604/csse.2023.030183 - 03 November 2022

    Abstract Software-Defined Network (SDN) decouples the control plane of network devices from the data plane. While alleviating the problems presented in traditional network architectures, it also brings potential security risks, particularly network Denial-of-Service (DoS) attacks. While many research efforts have been devoted to identifying new features for DoS attack detection, detection methods are less accurate in detecting DoS attacks against client hosts due to the high stealth of such attacks. To solve this problem, a new method of DoS attack detection based on Deep Factorization Machine (DeepFM) is proposed in SDN. Firstly, we select the Growth… More >

  • Open Access

    ARTICLE

    Genetic-based Fuzzy IDS for Feature Set Reduction and Worm Hole Attack Detection

    M. Reji1,*, Christeena Joseph2, K. Thaiyalnayaki2, R. Lathamanju2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1265-1278, 2023, DOI:10.32604/csse.2023.026776 - 03 November 2022

    Abstract The wireless ad-hoc networks are decentralized networks with a dynamic topology that allows for end-to-end communications via multi-hop routing operations with several nodes collaborating themselves, when the destination and source nodes are not in range of coverage. Because of its wireless type, it has lot of security concerns than an infrastructure networks. Wormhole attacks are one of the most serious security vulnerabilities in the network layers. It is simple to launch, even if there is no prior network experience. Signatures are the sole thing that preventive measures rely on. Intrusion detection systems (IDS) and other… More >

  • Open Access

    ARTICLE

    Developing a Secure Framework Using Feature Selection and Attack Detection Technique

    Mahima Dahiya*, Nitin Nitin

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4183-4201, 2023, DOI:10.32604/cmc.2023.032430 - 31 October 2022

    Abstract Intrusion detection is critical to guaranteeing the safety of the data in the network. Even though, since Internet commerce has grown at a breakneck pace, network traffic kinds are rising daily, and network behavior characteristics are becoming increasingly complicated, posing significant hurdles to intrusion detection. The challenges in terms of false positives, false negatives, low detection accuracy, high running time, adversarial attacks, uncertain attacks, etc. lead to insecure Intrusion Detection System (IDS). To offset the existing challenge, the work has developed a secure Data Mining Intrusion detection system (DataMIDS) framework using Functional Perturbation (FP) feature… More >

  • Open Access

    ARTICLE

    A Novel Approach for Network Vulnerability Analysis in IIoT

    K. Sudhakar*, S. Senthilkumar

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 263-277, 2023, DOI:10.32604/csse.2023.029680 - 16 August 2022

    Abstract Industrial Internet of Things (IIoT) offers efficient communication among business partners and customers. With an enlargement of IoT tools connected through the internet, the ability of web traffic gets increased. Due to the raise in the size of network traffic, discovery of attacks in IIoT and malicious traffic in the early stages is a very demanding issues. A novel technique called Maximum Posterior Dichotomous Quadratic Discriminant Jaccardized Rocchio Emphasis Boost Classification (MPDQDJREBC) is introduced for accurate attack detection with minimum time consumption in IIoT. The proposed MPDQDJREBC technique includes feature selection and categorization. First, the… More >

  • Open Access

    ARTICLE

    Swarm Intelligence Based Routing with Black Hole Attack Detection in MANET

    S. A. Arunmozhi*, S. Rajeswari, Y. Venkataramani

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2337-2347, 2023, DOI:10.32604/csse.2023.024340 - 01 August 2022

    Abstract Mobile Ad hoc Network (MANET) possesses unique characteristics which makes it vulnerable to security threats. In MANET, it is highly challenging to protect the nodes from cyberattacks. Power conservation improves both life time of nodes as well as the network. Computational capabilities and memory constraints are critical issues in the implementation of cryptographic techniques. Energy and security are two important factors that need to be considered for improving the performance of MANET. So, the incorporation of an energy efficient secure routing protocol becomes inevitable to ensure appropriate action upon the network. The nodes present in More >

  • Open Access

    ARTICLE

    Cyberattack Detection Framework Using Machine Learning and User Behavior Analytics

    Abdullah Alshehri1,*, Nayeem Khan1, Ali Alowayr1, Mohammed Yahya Alghamdi2

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1679-1689, 2023, DOI:10.32604/csse.2023.026526 - 15 June 2022

    Abstract This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior Analytics. The framework models the user behavior as sequences of events representing the user activities at such a network. The represented sequences are then fitted into a recurrent neural network model to extract features that draw distinctive behavior for individual users. Thus, the model can recognize frequencies of regular behavior to profile the user manner in the network. The subsequent procedure is that the recurrent neural network would detect abnormal behavior by classifying unknown behavior to either regular or… More >

  • Open Access

    ARTICLE

    Iterative Dichotomiser Posteriori Method Based Service Attack Detection in Cloud Computing

    B. Dhiyanesh1,*, K. Karthick2, R. Radha3, Anita Venaik4

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1099-1107, 2023, DOI:10.32604/csse.2023.024691 - 15 June 2022

    Abstract Cloud computing (CC) is an advanced technology that provides access to predictive resources and data sharing. The cloud environment represents the right type regarding cloud usage model ownership, size, and rights to access. It introduces the scope and nature of cloud computing. In recent times, all processes are fed into the system for which consumer data and cache size are required. One of the most security issues in the cloud environment is Distributed Denial of Service (DDoS) attacks, responsible for cloud server overloading. This proposed system ID3 (Iterative Dichotomiser 3) Maximum Multifactor Dimensionality Posteriori Method… More >

  • Open Access

    ARTICLE

    Hybrid Deep Learning Based Attack Detection for Imbalanced Data Classification

    Rasha Almarshdi1,2,*, Laila Nassef1, Etimad Fadel1, Nahed Alowidi1

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 297-320, 2023, DOI:10.32604/iasc.2023.026799 - 06 June 2022

    Abstract Internet of Things (IoT) is the most widespread and fastest growing technology today. Due to the increasing of IoT devices connected to the Internet, the IoT is the most technology under security attacks. The IoT devices are not designed with security because they are resource constrained devices. Therefore, having an accurate IoT security system to detect security attacks is challenging. Intrusion Detection Systems (IDSs) using machine learning and deep learning techniques can detect security attacks accurately. This paper develops an IDS architecture based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) deep learning… More >

  • Open Access

    ARTICLE

    Novel DoS Attack Detection Based on Trust Mode Authentication for IoT

    D. Yuvaraj1, S. Shanmuga Priya2,*, M. Braveen3, S. Navaneetha Krishnan4, S. Nachiyappan5, Abolfazl Mehbodniya6, A. Mohamed Uvaze Ahamed7, M. Sivaram8

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1505-1522, 2022, DOI:10.32604/iasc.2022.022151 - 25 May 2022

    Abstract Wireless sensor networks are extensively utilized as a communication mechanism in the field of the Internet of Things (IoT). Along with these services, numerous IoT based applications need stabilized transmission or delivery over unbalanced wireless connections. To ensure the stability of data packets delivery, prevailing works exploit diverse geographical routing with multi-hop forwarders in WSNs. Furthermore, critical Denial of Service (DoS) attacks frequently has an impact on these techniques, where an enormous amount of invalid data starts replicating and transmitted to receivers to prevent Wireless Sensor Networks (WSN) communication. In this investigation, a novel adaptive… More >

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