Malware Classification
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Benchmarks
Most implemented
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
A New Burrows Wheeler Transform Markov Distance
Learning a Neural-network-based Representation for Open Set Recognition
Assemblage: Automatic Binary Dataset Construction for Machine Learning
Malware Classification Using Static Disassembly and Machine Learning
Papers
Adapter-Based Few-Shot Continual Learning for Malicious Packet Recognition
The continual evolution of malware variants necessitates detection systems that can adapt to new threats without retraining from scratch. However, continually updating models on new data often leads to catastrophic forge…
Few-Shot Class-Incremental LearningSelf-Supervised LearningMalware ClassificationContinual LearningConcept Drift Detection and Adaptive Retraining of Malware Classification Models
Concept drift refers to changes over time in the statistical properties of data, as compared to the data that was used to train a learning model. Machine learning models for malware detection or classification are partic…
Malware ClassificationMalware DetectionThreatVisionAI: A Hybrid CNN-ViT Framework for Image-Based Malware Classification
Traditional malware detection methods struggle to generalize to obfuscated or previously unseen threats. This paper introduces ThreatVisionAI, a hybrid malware family classification framework that integrates a raw-image …
Malware ClassificationMalware DetectionHamm-Grams: An Algorithm for Mining Regular Expressions of Bytes
Malware poses a critical and ever-evolving threat, and robust and effective systems for detecting and classifying malware are of essential importance. $n$-grams features are among the common static features used in effec…
Malware ClassificationA Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution
Malware classification remains a challenging problem due to its inherent heterogeneity, the presence of packed binaries, and the diverse distribution of malware families. Traditional single-model detection mechanisms oft…
Malware ClassificationMalware DetectionMulti-View Decompilation for LLM-Based Malware Classification
Malware analysts often inspect compiled binaries through decompiled pseudo-C, when source code is unavailable. Recent work suggests that large language models (LLMs) can assist this process by classifying decompiled code…
Malware Classification