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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 151–175 of 431 papers

TitleStatusHype
Assessing Cyclostationary Malware Detection via Feature Selection and Classification—0
Analysing Safety Risks in LLMs Fine-Tuned with Pseudo-Malicious Cyber Security Data—0
Dos and Don'ts of Machine Learning in Computer Security—0
DRo: A data-scarce mechanism to revolutionize the performance of Deep Learning based Security Systems—0
Dual Explanations via Subgraph Matching for Malware Detection—0
Dynamic detection of mobile malware using smartphone data and machine learning—0
A Survey of Malware Detection Using Deep Learning—0
Dynamic Malware Classification of Windows PE Files using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion—0
Early Detection of In-Memory Malicious Activity based on Run-time Environmental Features—0
Echelon: Two-Tier Malware Detection for Raw Executables to Reduce False Alarms—0
Generative AI-Based Effective Malware Detection for Embedded Computing Systems—0
Effectiveness of Adversarial Examples and Defenses for Malware Classification—0
Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection—0
Efficient and Robust Classification for Sparse Attacks—0
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
Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid—0
Efficient Malware Analysis Using Metric Embeddings—0
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
A Review on The Use of Deep Learning in Android Malware Detection—0
A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods—0
Empirical Quantification of Spurious Correlations in Malware Detection—0
Design of secure and robust cognitive system for malware detection—0
Enhanced Attacks on Defensively Distilled Deep Neural Networks—0
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems—0
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