SOTAVerified

Malware Classification

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

Papers

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Showing 1–10 of 146 papers

TitleStatusHype
Malware Classification Leveraging NLP & Machine Learning for Enhanced AccuracyCode0
Semantic Preprocessing for LLM-based Malware Analysis—0
EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware ClassifiersCode2
Dynamic Malware Classification of Windows PE Files using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion—0
Structure-based Anomaly Detection and Clustering—0
Malware families discovery via Open-Set Recognition on Android manifest permissions—0
Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings—0
Scalable APT Malware Classification via Parallel Feature Extraction and GPU-Accelerated Learning—0
OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques—0
Imbalanced malware classification: an approach based on dynamic classifier selectionCode0
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All datasetsMicrosoft Malware Classification ChallengeMalimg DatasetMaleVis

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Levit-MCAccuracy96.6—Unverified