Faculty Publications
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Item Robust Detection of Evasive Fileless Powershell Malware: A Machine Learning Approach(2025 International Conference on Artificial intelligence and Emerging Technologies (ICAIET), Artificial intelligence and Emerging Technologies (ICAIET), 2025 International Conference on,20250828, IEEE Xplore Digital Library, 2025) Meher, Manish Kumar; Rath, Adyasha; Panda, Ganapati; Thanapati, Biswa Bhusana; Puthal, Deepak KumarIn the growing age of cybersecurity, the most obnoxious attack type is PowerShell-based fileless attacks. PowerShell provides the most favored environment to perform advanced tasks. This feature leads to its misuse, especially in the case of fileless attacks. The traditional methods uses signature based detection, are not able to identify the malware. Modern-day scripts are complex and obfuscated, which avoids detection. This paper proposed a machine learning (ML)-based model for malicious sample detection using feature analysis. It efficiently differentiates the benign and malicious samples with a considerable degree of accuracy. To enhance the detection further, the mutual information (MI) technique was applied to retrieve the most efficient and relevant features. This extensive experiment evaluation demonstrated that the proposed ML-based model achieved improved accuracy of 97.64 % and a robust performance.Item Artificial Intelligence-Based Cybersecurity for the Metaverse: Research Challenges and Opportunities(2025-04) Awadallah, Abeer; Eledlebi, Khouloud; Zemerly, Mohamed Jamal; Puthal, Deepak; Damiani, Ernesto; Taha, KamalThe metaverse, known as the next-generation 3D Internet, represents virtual environments that mirror the physical world. It is supported by innovative technologies such as digital twins and extended reality (XR), which elevate user experiences across various fields. However, the metaverse also introduces significant cybersecurity and privacy challenges that remain underexplored. Due to its complex multi-tech infrastructure, the metaverse requires sophisticated, automated, and intelligent cybersecurity measures to mitigate emerging threats effectively. Therefore, this paper is the first to explore Artificial Intelligence (AI)-driven cybersecurity techniques for the metaverse, examining academic and industrial perspectives. First, we provide an overview of the metaverse, presenting a detailed system model, diverse use cases, and insights into its current industrial status. We then present attack models and cybersecurity threats derived from the unique characteristics and technologies of the metaverse. Next, we review AI-driven cybersecurity solutions based on three critical aspects: User authentication, intrusion detection systems (IDS), and the security of digital assets, specifically for Blockchain and Non-fungible Tokens (NFTs). Finally, we highlight challenges and suggest future research opportunities to enhance metaverse security, privacy, and digital asset transactions