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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
LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift AnalysisCode1
CyberLLMInstruct: A New Dataset for Analysing Safety of Fine-Tuned LLMs Using Cyber Security DataCode1
MASKDROID: Robust Android Malware Detection with Masked Graph RepresentationsCode1
Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4Code1
MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion AttacksCode1
Nebula: Self-Attention for Dynamic Malware AnalysisCode1
Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge SettingCode1
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model PerformanceCode1
Recasting Self-Attention with Holographic Reduced RepresentationsCode1
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified RobustnessCode1
PAD: Towards Principled Adversarial Malware Detection Against Evasion AttacksCode1
Continuous Learning for Android Malware DetectionCode1
UniASM: Binary Code Similarity Detection without Fine-tuningCode1
Avast-CTU Public CAPE DatasetCode1
Self-Supervised Vision Transformers for Malware DetectionCode1
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-ArtCode1
Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?Code1
DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of BytecodeCode1
heterogeneous temporal graph transformer: an intelligent system for evolving android malware detectionCode1
Multi-Task Hierarchical Learning Based Network Traffic AnalyticsCode1
Learning Security Classifiers with Verified Global Robustness PropertiesCode1
Federated Learning for Malware Detection in IoT DevicesCode1
Deep Learning for Android Malware Defenses: a Systematic Literature ReviewCode1
MalNet: A Large-Scale Image Database of Malicious SoftwareCode1
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