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Malware Detection

Malware Detection is a significant part of endpoint security including workstations, servers, cloud instances, and mobile devices. Malware Detection is used to detect and identify malicious activities caused by malware. With the increase in the variety of malware activities on CMS based websites such as malicious malware redirects on WordPress site (Aka, WordPress Malware Redirect Hack) where the site redirects to spam, being the most widespread, the need for automatic detection and classifier amplifies as well. The signature-based Malware Detection system is commonly used for existing malware that has a signature but it is not suitable for unknown malware or zero-day malware

Source: The Threat of Adversarial Attacks on Machine Learning in Network Security - A Survey

Papers

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Showing 181–190 of 431 papers

TitleStatusHype
Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning—0
A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes—0
Evasion Attacks against Machine Learning at Test Time—0
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems—0
Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions—0
Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges—0
Explainable Malware Detection through Integrated Graph Reduction and Learning Techniques—0
Explainable Malware Detection with Tailored Logic Explained Networks—0
Efficient and Robust Classification for Sparse Attacks—0
Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection—0
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