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Malware Classification

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Malware Classification is the process of assigning a malware sample to a specific malware family. Malware within a family shares similar properties that can be used to create signatures for detection and classification. Signatures can be categorized as static or dynamic based on how they are extracted. A static signature can be based on a byte-code sequence, binary assembly instruction, or an imported Dynamic Link Library (DLL). Dynamic signatures can be based on file system activities, terminal commands, network communications, or function and system call sequences.

Source: Behavioral Malware Classification using Convolutional Recurrent Neural Networks

논문

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Malware Classification

논문

Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy

Efficient Malware Detection with Optimized Learning on High-Dimensional Features

Semantic Preprocessing for LLM-based Malware Analysis

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

Structure-based Anomaly Detection and Clustering

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

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings

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

Scalable APT Malware Classification via Parallel Feature Extraction and GPU-Accelerated Learning

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 Classification from Memory Dumps Using Machine Learning, Transformers, and Large Language Models

Cluster Analysis and Concept Drift Detection in Malware

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