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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 76–100 of 431 papers

TitleStatusHype
Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network—0
Crystal ball: From innovative attacks to attack effectiveness classifierCode0
Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies—0
Image-Based Malware Classification Using QR and Aztec Codes—0
Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU) Learning in Cybersecurity—0
Explainable Malware Detection through Integrated Graph Reduction and Learning Techniques—0
Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection—0
XAI and Android Malware Models—0
On the Cost of Model-Serving Frameworks: An Experimental Evaluation—0
SCORE: Syntactic Code Representations for Static Script Malware Detection—0
Unmasking the Shadows: Pinpoint the Implementations of Anti-Dynamic Analysis Techniques in Malware Using LLM—0
Metamorphic Malware Evolution: The Potential and Peril of Large Language Models—0
Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems—0
Classification under strategic adversary manipulation using pessimistic bilevel optimisation—0
A Novel Reinforcement Learning Model for Post-Incident Malware Investigations—0
Deep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challenges—0
A Novel Approach to Malicious Code Detection Using CNN-BiLSTM and Feature Fusion—0
Decoding Android Malware with a Fraction of Features: An Attention-Enhanced MLP-SVM Approach—0
Accelerating Malware Classification: A Vision Transformer SolutionCode0
Packet Inspection Transformer: A Self-Supervised Journey to Unseen Malware Detection with Few Samples—0
A Visualized Malware Detection Framework with CNN and Conditional GAN—0
Towards Novel Malicious Packet Recognition: A Few-Shot Learning Approach—0
Revisiting Static Feature-Based Android Malware Detection—0
Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges—0
DetectBERT: Towards Full App-Level Representation Learning to Detect Android MalwareCode0
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