Special Issues

Intelligent Uni-modal and Multi-modal Agents against Adversarial Cyber Attacks

Submission Deadline: 04 April 2023 (closed) View: 10

Guest Editors

Dr. Sana Ullah Jan, Edinburgh Napier University, UK.
Dr. Suhaib Durrani, Lahore Garrison University, Pakistan.
Dr. Muhammad Asif, Lahore Garrison University.

Summary

This special issue will attract articles that focus on designing intelligent agents to identify stealthy and adversarial cyber attacks. The expected results will provide higher capabilities of distinguishing between the legitimate event and the illegitimate pattern using state-of-the-art technologies such as Artificial Intelligence or Machine Learning. We expect the developments by researchers in terms of uni-modal algorithms as well as multi-modal agents that can extract useful knowledge from input observations from different aspects. These advances will lead to reaching a trade-off between computational complexity and efficiency of the system.


Keywords

adversarial cyber attacks
multi-modal learning
artificial intelligence
machine learning

Published Papers


  • Open Access

    ARTICLE

    An Efficient Character-Level Adversarial Attack Inspired by Textual Variations in Online Social Media Platforms

    Jebran Khan, Kashif Ahmad, Kyung-Ah Sohn
    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2869-2894, 2023, DOI:10.32604/csse.2023.040159
    (This article belongs to the Special Issue: Intelligent Uni-modal and Multi-modal Agents against Adversarial Cyber Attacks)
    Abstract In recent years, the growing popularity of social media platforms has led to several interesting natural language processing (NLP) applications. However, these social media-based NLP applications are subject to different types of adversarial attacks due to the vulnerabilities of machine learning (ML) and NLP techniques. This work presents a new low-level adversarial attack recipe inspired by textual variations in online social media communication. These variations are generated to convey the message using out-of-vocabulary words based on visual and phonetic similarities of characters and words in the shortest possible form. The intuition of the proposed scheme… More >

  • Open Access

    ARTICLE

    Detecting and Classifying Darknet Traffic Using Deep Network Chains

    Amr Munshi, Majid Alotaibi, Saud Alotaibi, Wesam Al-Sabban, Nasser Allheeib
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 891-902, 2023, DOI:10.32604/csse.2023.039374
    (This article belongs to the Special Issue: Intelligent Uni-modal and Multi-modal Agents against Adversarial Cyber Attacks)
    Abstract The anonymity of the darknet makes it attractive to secure communication lines from censorship. The analysis, monitoring, and categorization of Internet network traffic are essential for detecting darknet traffic that can generate a comprehensive characterization of dangerous users and assist in tracing malicious activities and reducing cybercrime. Furthermore, classifying darknet traffic is essential for real-time applications such as the timely monitoring of malware before attacks occur. This paper presents a two-stage deep network chain for detecting and classifying darknet traffic. In the first stage, anonymized darknet traffic, including VPN and Tor traffic related to hidden… More >

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