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  • Open Access

    ARTICLE

    Privacy Preserving Blockchain with Optimal Deep Learning Model for Smart Cities

    K. Pradeep Mohan Kumar1, Jenifer Mahilraj2, D. Swathi3, R. Rajavarman4, Subhi R. M. Zeebaree5, Rizgar R. Zebari6, Zryan Najat Rashid7, Ahmed Alkhayyat8,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5299-5314, 2022, DOI:10.32604/cmc.2022.030825

    Abstract Recently, smart cities have emerged as an effective approach to deliver high-quality services to the people through adaptive optimization of the available resources. Despite the advantages of smart cities, security remains a huge challenge to be overcome. Simultaneously, Intrusion Detection System (IDS) is the most proficient tool to accomplish security in this scenario. Besides, blockchain exhibits significance in promoting smart city designing, due to its effective characteristics like immutability, transparency, and decentralization. In order to address the security problems in smart cities, the current study designs a Privacy Preserving Secure Framework using Blockchain with Optimal Deep Learning (PPSF-BODL) model. The… More >

  • Open Access

    ARTICLE

    A Collaborative Approach for Secured Routing in Mobile Ad-Hoc Network

    W. Gracy Theresa1,*, A. Gayathri2, P. Rama3

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1337-1351, 2023, DOI:10.32604/iasc.2023.028425

    Abstract Mobile computing is the most powerful application for network communication and connectivity, given recent breakthroughs in the field of wireless networks or Mobile Ad-hoc networks (MANETs). There are several obstacles that effective networks confront and the networks must be able to transport data from one system to another with adequate precision. For most applications, a framework must ensure that the retrieved data reflects the transmitted data. Before driving to other nodes, if the frame between the two nodes is deformed in the data-link layer, it must be repaired. Most link-layer protocols immediately disregard the frame and enable the high-layer protocols… More >

  • Open Access

    ARTICLE

    WOA-DNN for Intelligent Intrusion Detection and Classification in MANET Services

    C. Edwin Singh1,*, S. Maria Celestin Vigila2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1737-1751, 2023, DOI:10.32604/iasc.2023.028022

    Abstract Mobile ad-hoc networks (MANET) are garnering a lot of attention because of their potential to provide low-cost solutions to real-world communications. MANETs are more vulnerable to security threats. Changes in nodes, bandwidth limits, and centralized control and management are some of the characteristics. IDS (Intrusion Detection System) are the aid for detection, determination, and identification of illegal system activity such as use, copying, modification, and destruction of data. To address the identified issues, academics have begun to concentrate on building IDS-based machine learning algorithms. Deep learning is a type of machine learning that can produce exceptional outcomes. This study proposes… More >

  • Open Access

    ARTICLE

    Intrusion Detection System Using a Distributed Ensemble Design Based Convolutional Neural Network in Fog Computing

    Aiming Wu1, Shanshan Tu1,*, Muhammad Wagas1,2,3, Yongjie Yang1, Yihe Zhang1, Xuetao Bai1

    Journal of Information Hiding and Privacy Protection, Vol.4, No.1, pp. 25-39, 2022, DOI:10.32604/jihpp.2022.029922

    Abstract With the rapid development of the Internet of Things (IoT), all kinds of data are increasing exponentially. Data storage and computing on cloud servers are increasingly restricted by hardware. This has prompted the development of fog computing. Fog computing is to place the calculation and storage of data at the edge of the network, so that the entire Internet of Things system can run more efficiently. The main function of fog computing is to reduce the burden of cloud servers. By placing fog nodes in the IoT network, the data in the IoT devices can be transferred to the fog… More >

  • Open Access

    ARTICLE

    Adversarial Training Against Adversarial Attacks for Machine Learning-Based Intrusion Detection Systems

    Muhammad Shahzad Haroon*, Husnain Mansoor Ali

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3513-3527, 2022, DOI:10.32604/cmc.2022.029858

    Abstract Intrusion detection system plays an important role in defending networks from security breaches. End-to-end machine learning-based intrusion detection systems are being used to achieve high detection accuracy. However, in case of adversarial attacks, that cause misclassification by introducing imperceptible perturbation on input samples, performance of machine learning-based intrusion detection systems is greatly affected. Though such problems have widely been discussed in image processing domain, very few studies have investigated network intrusion detection systems and proposed corresponding defence. In this paper, we attempt to fill this gap by using adversarial attacks on standard intrusion detection datasets and then using adversarial samples… More >

  • Open Access

    ARTICLE

    An Optimized and Hybrid Framework for Image Processing Based Network Intrusion Detection System

    Murtaza Ahmed Siddiqi, Wooguil Pak*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3921-3949, 2022, DOI:10.32604/cmc.2022.029541

    Abstract The network infrastructure has evolved rapidly due to the ever-increasing volume of users and data. The massive number of online devices and users has forced the network to transform and facilitate the operational necessities of consumers. Among these necessities, network security is of prime significance. Network intrusion detection systems (NIDS) are among the most suitable approaches to detect anomalies and assaults on a network. However, keeping up with the network security requirements is quite challenging due to the constant mutation in attack patterns by the intruders. This paper presents an effective and prevalent framework for NIDS by merging image processing… More >

  • Open Access

    ARTICLE

    An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

    G. Rajeshkumar1,*, M. Vinoth Kumar2, K. Sailaja Kumar3, Surbhi Bhatia4, Arwa Mashat5, Pankaj Dadheech6

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1187-1200, 2023, DOI:10.32604/csse.2023.026137

    Abstract A Mobile Ad hoc Network (MANET) is a group of low-power consumption of wireless mobile nodes that configure a wireless network without the assistance of any existing infrastructure/centralized organization. The primary aim of MANETs is to extend flexibility into the self-directed, mobile, and wireless domain, in which a cluster of autonomous nodes forms a MANET routing system. An Intrusion Detection System (IDS) is a tool that examines a network for malicious behavior/policy violations. A network monitoring system is often used to report/gather any suspicious attacks/violations. An IDS is a software program or hardware system that monitors network/security traffic for malicious… More >

  • Open Access

    ARTICLE

    Intelligent Intrusion Detection System for Industrial Internet of Things Environment

    R. Gopi1, R. Sheeba2, K. Anguraj3, T. Chelladurai4, Haya Mesfer Alshahrani5, Nadhem Nemri6,*, Tarek Lamoudan7

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1567-1582, 2023, DOI:10.32604/csse.2023.025216

    Abstract Rapid increase in the large quantity of industrial data, Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation, data sensing and collection, real-time data processing, and high request arrival rates. The classical intrusion detection system (IDS) is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity. To resolve these issues, this paper designs a new Chaotic Cuckoo Search Optimization Algorithm (CCSOA) with optimal wavelet kernel extreme learning machine (OWKELM) named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform. The CCSOA-OWKELM technique focuses on the design of feature selection with classification… More >

  • Open Access

    ARTICLE

    Intrusion Detection Using Ensemble Wrapper Filter Based Feature Selection with Stacking Model

    D. Karthikeyan1,*, V. Mohan Raj2, J. Senthilkumar2, Y. Suresh2

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 645-659, 2023, DOI:10.32604/iasc.2023.027039

    Abstract The number of attacks is growing tremendously in tandem with the growth of internet technologies. As a result, protecting the private data from prying eyes has become a critical and tough undertaking. Many intrusion detection solutions have been offered by researchers in order to decrease the effect of these attacks. For attack detection, the prior system has created an SMSRPF (Stacking Model Significant Rule Power Factor) classifier. To provide creative instance detection, the SMSRPF combines the detection of trained classifiers such as DT (Decision Tree) and RF (Random Forest). Nevertheless, it does not generate any accurate findings that are adequate.… More >

  • Open Access

    ARTICLE

    An Intrusion Detection System for SDN Using Machine Learning

    G. Logeswari*, S. Bose, T. Anitha

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 867-880, 2023, DOI:10.32604/iasc.2023.026769

    Abstract Software Defined Networking (SDN) has emerged as a promising and exciting option for the future growth of the internet. SDN has increased the flexibility and transparency of the managed, centralized, and controlled network. On the other hand, these advantages create a more vulnerable environment with substantial risks, culminating in network difficulties, system paralysis, online banking frauds, and robberies. These issues have a significant detrimental impact on organizations, enterprises, and even economies. Accuracy, high performance, and real-time systems are necessary to achieve this goal. Using a SDN to extend intelligent machine learning methodologies in an Intrusion Detection System (IDS) has stimulated… More >

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