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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 1–25 of 431 papers

TitleStatusHype
EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware ClassifiersCode2
Learning Security Classifiers with Verified Global Robustness PropertiesCode1
Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware DetectionCode1
Federated Learning for Malware Detection in IoT DevicesCode1
LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift AnalysisCode1
Learning from Context: Exploiting and Interpreting File Path Information for Better Malware DetectionCode1
Data Augmentation Based Malware Detection using Convolutional Neural NetworksCode1
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model PerformanceCode1
DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of BytecodeCode1
A Framework for Enhancing Deep Neural Networks Against Adversarial MalwareCode1
HYDRA: A multimodal deep learning framework for malware classificationCode1
Continuous Learning for Android Malware DetectionCode1
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-ArtCode1
Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware DetectionCode1
Deep Learning for Android Malware Defenses: a Systematic Literature ReviewCode1
CyberLLMInstruct: A New Dataset for Analysing Safety of Fine-Tuned LLMs Using Cyber Security DataCode1
Dataset Optimization Strategies for MalwareTraffic DetectionCode1
Against All Odds: Winning the Defense Challenge in an Evasion Competition with DiversificationCode1
heterogeneous temporal graph transformer: an intelligent system for evolving android malware detectionCode1
Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge SettingCode1
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified RobustnessCode1
Avast-CTU Public CAPE DatasetCode1
Classifying Sequences of Extreme Length with Constant Memory Applied to Malware DetectionCode1
Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?Code1
Learning the PE Header, Malware Detection with Minimal Domain KnowledgeCode1
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