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Malware Detection

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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

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Malware Detection

논문

Efficient Malware Detection with Optimized Learning on High-Dimensional Features

Empirical Quantification of Spurious Correlations in Malware Detection

Network Threat Detection: Addressing Class Imbalanced Data with Deep Forest

EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers

System Calls for Malware Detection and Classification: Methodologies and Applications

Dynamic Malware Classification of Windows PE Files using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion

Adapting Novelty towards Generating Antigens for Antivirus systems

LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift Analysis

MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptation

Malware families discovery via Open-Set Recognition on Android manifest permissions

On the Security Risks of ML-based Malware Detection Systems: A Survey

Analysing Safety Risks in LLMs Fine-Tuned with Pseudo-Malicious Cyber Security Data

Evaluating the Robustness of Adversarial Defenses in Malware Detection Systems

Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets

Dual Explanations via Subgraph Matching for Malware Detection

Optimized Approaches to Malware Detection: A Study of Machine Learning and Deep Learning Techniques

On the Consistency of GNN Explanations for Malware Detection

OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques

Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering

Malware Detection in Docker Containers: An Image is Worth a Thousand Logs

Imbalanced malware classification: an approach based on dynamic classifier selection

Leveraging VAE-Derived Latent Spaces for Enhanced Malware Detection with Machine Learning Classifiers

BERTDetect: A Neural Topic Modelling Approach for Android Malware Detection

Trust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection

CyberLLMInstruct: A New Dataset for Analysing Safety of Fine-Tuned LLMs Using Cyber Security Data

Malware Detection at the Edge with Lightweight LLMs: A Performance Evaluation

Malware Detection based on API calls

LAMD: Context-driven Android Malware Detection and Classification with LLMs

Recent Advances in Malware Detection: Graph Learning and Explainability

RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization

Learning Temporal Invariance in Android Malware Detectors

Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems

Integrating Explainable AI for Effective Malware Detection in Encrypted Network Traffic

Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines

Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies