Special lssues

Soft Computing in Intrusion Detection

Submission Deadline: 30 September 2020 (closed)

Guest Editors

Dr. Iftikhar Ahmad, Professor of computer science department at Mohi ud Din Islamic University, Pakistan.
Dr. Aneel Rahim, Lecturer at school of computing, Dublin Institute of Technology, Dublin, Ireland.
Dr. Mohsin Iftikhar, Lecturer in Computing at Charles Sturt University, Australia.


Nowadays Computer and Network systems are facing many security issues, one of which considered important is an intrusion. To prevent such intrusion, a mechanism for optimal intrusion detection is deemed necessary. A number of tools and techniques are available, yet most of them still face a main problem that is on performance. The performance of intrusion detection can be enhanced using soft computing techniques based on their proven effectiveness in various applications, such as pattern detection, data segmentation, data mining, adaptive control, and image processing. Based on their effectiveness, recently, soft computing techniques are widely used in intrusion detection such as feature transformation, feature extraction, feature selection, classification, and optimization. This special issue aims to bring together research work in the area of intrusion detection using soft computing techniques, investigate the novel solutions, and discuss the future trends in this field.


Warm reminder: Please select Special Issue: Soft Computing in Intrusion Detection when you submit your article in IASC submission system


• Intrusion Detection System
• Security Attacks
• Benchmark datasets for intrusion detection
• Classification of attacks
• Feature Selection in intrusion detection and security countermeasures

Published Papers

  • Open Access


    Hybrid Multimodal Biometric Template Protection

    Naima Bousnina, Sanaa Ghouzali, Mounia Mikram, Maryam Lafkih, Ohoud Nafea, Muna Al-Razgan, Wadood Abdul
    Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 35-51, 2021, DOI:10.32604/iasc.2021.014694
    (This article belongs to this Special Issue: Soft Computing in Intrusion Detection)
    Abstract Biometric template disclosure starts gaining an important concern in deploying practical biometric authentication systems, where an assailant compromises the database for illegitimate access. To protect biometric templates from disclosure attacks, biometric authentication systems should meet these four requirements: security, diversity, revocability, and performance. Different methods have been suggested in the literature such as feature transformation techniques and biometric cryptosystems. However, no single method could satisfy the four requirements, giving rise to the deployment of hybrid mechanisms. In this context, the current paper proposes a hybrid system for multimodal biometric template protection to provide robustness against template database attacks. Herein, a… More >

  • Open Access


    Soft Computing Based Evolutionary Multi-Label Classification

    Rubina Aslam, Manzoor Illahi Tamimy, Waqar Aslam
    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1233-1249, 2020, DOI:10.32604/iasc.2020.013086
    (This article belongs to this Special Issue: Soft Computing in Intrusion Detection)
    Abstract Machine Learning (ML) has revolutionized intelligent systems that range from self-driving automobiles, search engines, business/market analysis, fraud detection, network intrusion investigation, and medical diagnosis. Classification lies at the core of Machine Learning and Multi-label Classification (MLC) is the closest to real-life problems related to heuristics. It is a type of classification problem where multiple labels or classes can be assigned to more than one instance simultaneously. The level of complexity in MLC is increased by factors such as data imbalance, high dimensionality, label correlations, and noise. Conventional MLC techniques such as ensembles-based approaches, Multi-label Stacking, Random k-label sets, and Hierarchy… More >

  • Open Access


    A Pursuit of Sustainable Privacy Protection in Big Data Environment by an Optimized Clustered-Purpose Based Algorithm

    Norjihan Binti Abdul Ghani, Muneer Ahmad, Zahra Mahmoud, Raja Majid Mehmood
    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1217-1231, 2020, DOI:10.32604/iasc.2020.011731
    (This article belongs to this Special Issue: Soft Computing in Intrusion Detection)
    Abstract Achievement of sustainable privacy preservation is mostly very challenging in a resource shared computer environment. This challenge demands a dedicated focus on the exponential growth of big data. Despite the existence of specific privacy preservation policies at the organizational level, still sustainable protection of a user’s data at various levels, i.e., data collection, utilization, reuse, and disclosure, etc. have not been implemented to its spirit. For every personal data being collected and used, organizations must ensure that they are complying with their defined obligations. We are proposing a new clustered-purpose based access control for users’ sustainable data privacy protection in… More >

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