SOTAVerified

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 126–150 of 431 papers

TitleStatusHype
Decision-forest voting scheme for classification of rare classes in network intrusion detection—0
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS—0
Analysis of Bayesian Classification based Approaches for Android Malware Detection—0
Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems—0
Assessing Cyclostationary Malware Detection via Feature Selection and Classification—0
Analysing Safety Risks in LLMs Fine-Tuned with Pseudo-Malicious Cyber Security Data—0
Adversarial Patterns: Building Robust Android Malware Classifiers—0
Transferable Cost-Aware Security Policy Implementation for Malware Detection Using Deep Reinforcement Learning—0
A short review on Applications of Deep learning for Cyber security—0
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization—0
Artificial Neural Network for Cybersecurity: A Comprehensive Review—0
A Comparison of Static, Dynamic, and Hybrid Analysis for Malware Detection—0
Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions—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
Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid—0
A Review on The Use of Deep Learning in Android Malware Detection—0
A multi-task learning model for malware classification with useful file access pattern from API call sequence—0
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach—0
Detection of Malicious Android Applications: Classical Machine Learning vs. Deep Neural Network Integrated with Clustering—0
A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods—0
Discovering Malicious Signatures in Software from Structural Interactions—0
Distinguishability of Adversarial Examples—0
DL-Droid: Deep learning based android malware detection using real devices—0
Design of secure and robust cognitive system for malware detection—0
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