Artificial Intelligence for Cyber Security

Submission Deadline: 15 December 2022 (closed)

Guest Editors

Dr. Mohammad Tabrez Quasim, University of Bisha, Saudi Arabia.
Dr. Asadullah Shaikh, Najran University, Saudi Arabia.
Dr. Surbhi Bhatia, King Faisal University, Saudi Arabia.

Summary

Artificial intelligence (AI) has been applied widely to address cybersecurity problems. Cyber attacks, however, are growing in volume and complexity. This is of greater concern in the area of sensors and sensing technology, as they are increasingly used in all sorts of platforms inspired by the emerging Internet of Things. There is a critical need for the development of advanced cybersecurity methods to mitigate and eliminate the impacts of cyberattacks. Protecting and defending mechanisms are required to be more responsive, adaptive, and scalable. Advances in AI can be highly capable of solving complex, dynamic, and especially high-dimensional cybersecurity problems. They can help to provide more accurate detection, effective response automation, and proactive protection mechanism.


Keywords

• Cybersecurity
• Cybersituational Awareness
• Cyberthreat Intelligence
• Artificial Intelligence
• Machine Learning
• Formal Knowledge Representation
• Automated Reasoning

Published Papers


  • Open Access

    ARTICLE

    Securing Cloud Computing from Flash Crowd Attack Using Ensemble Intrusion Detection System

    Turke Althobaiti, Yousef Sanjalawe, Naeem Ramzan
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 453-469, 2023, DOI:10.32604/csse.2023.039207
    (This article belongs to this Special Issue: Artificial Intelligence for Cyber Security)
    Abstract Flash Crowd attacks are a form of Distributed Denial of Service (DDoS) attack that is becoming increasingly difficult to detect due to its ability to imitate normal user behavior in Cloud Computing (CC). Botnets are often used by attackers to perform a wide range of DDoS attacks. With advancements in technology, bots are now able to simulate DDoS attacks as flash crowd events, making them difficult to detect. When it comes to application layer DDoS attacks, the Flash Crowd attack that occurs during a Flash Event is viewed as the most intricate issue. This is mainly because it can imitate… More >

  • Open Access

    ARTICLE

    Network Security Situation Prediction Based on TCAN-BiGRU Optimized by SSA and IQPSO

    Junfeng Sun, Chenghai Li, Yafei Song, Peng Ni, Jian Wang
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 993-1021, 2023, DOI:10.32604/csse.2023.039215
    (This article belongs to this Special Issue: Artificial Intelligence for Cyber Security)
    Abstract The accuracy of historical situation values is required for traditional network security situation prediction (NSSP). There are discrepancies in the correlation and weighting of the various network security elements. To solve these problems, a combined prediction model based on the temporal convolution attention network (TCAN) and bi-directional gate recurrent unit (BiGRU) network is proposed, which is optimized by singular spectrum analysis (SSA) and improved quantum particle swarm optimization algorithm (IQPSO). This model first decomposes and reconstructs network security situation data into a series of subsequences by SSA to remove the noise from the data. Furthermore, a prediction model of TCAN-BiGRU… More >

  • Open Access

    ARTICLE

    Efficient Explanation and Evaluation Methodology Based on Hybrid Feature Dropout

    Jingang Kim, Suengbum Lim, Taejin Lee
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 471-490, 2023, DOI:10.32604/csse.2023.038413
    (This article belongs to this Special Issue: Artificial Intelligence for Cyber Security)
    Abstract AI-related research is conducted in various ways, but the reliability of AI prediction results is currently insufficient, so expert decisions are indispensable for tasks that require essential decision-making. XAI (eXplainable AI) is studied to improve the reliability of AI. However, each XAI methodology shows different results in the same data set and exact model. This means that XAI results must be given meaning, and a lot of noise value emerges. This paper proposes the HFD (Hybrid Feature Dropout)-based XAI and evaluation methodology. The proposed XAI methodology can mitigate shortcomings, such as incorrect feature weights and impractical feature selection. There are… More >

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