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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 146 papers

TitleStatusHype
One-Class SVM with Privileged Information and its Application to Malware Detection—0
On the (Statistical) Detection of Adversarial Examples—0
OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques—0
Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections—0
Trade-offs between membership privacy & adversarially robust learning—0
Quantum Machine Learning for Malware Classification—0
Random Forest for Malware Classification—0
Representation learning with function call graph transformations for malware open set recognition—0
Revisiting Static Feature-Based Android Malware Detection—0
RNNSecureNet: Recurrent neural networks for Cyber security use-cases—0
RNNSecureNet: Recurrent neural networks for Cybersecurity use-cases—0
Scalable APT Malware Classification via Parallel Feature Extraction and GPU-Accelerated Learning—0
Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings—0
Semantic Preprocessing for LLM-based Malware Analysis—0
Generation & Evaluation of Adversarial Examples for Malware Obfuscation—0
SoK: Applying Machine Learning in Security - A Survey—0
Steganographic Capacity of Deep Learning Models—0
Structure-based Anomaly Detection and Clustering—0
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time—0
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)—0
To believe or not to believe: Validating explanation fidelity for dynamic malware analysis—0
Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks—0
Realizable Universal Adversarial Perturbations for Malware—0
Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions—0
XAI and Android Malware Models—0
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All datasetsMicrosoft Malware Classification ChallengeMalimg DatasetMaleVis

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MalConvAccuracy (10-fold)9,641—Unverified
2TPOT ClassifierAccuracy (5-fold)98.94—Unverified
3CNN BiLSTM - Reb SamplAccuracy (5-fold)98.2—Unverified
4Ahmadi et al. (2016): ENT, Bytes 1-G, STR, IMG1, IMG2, MD1, MISC, OPC, SEC, REG, DP, API, SYM, MD2 IMG and Opcode N-Grams + Ensemble Learning (XGBoost)Accuracy (10-fold)1—Unverified
5HYDRAAccuracy (10-fold)1—Unverified
6Zhang et al. (2016): Total lines of each Section, Operation Code Count, API Usage, Special Symbols Count, Asm File Pixel Intensity Feature, Bytes File Block Size Distribution, Bytes File N-Gram + Ensemble Learning (XGBoost)Accuracy (10-fold)1—Unverified
7OrthrusAccuracy (10-fold)0.99—Unverified
8Opcode-based Shallow CNNAccuracy (10-fold)0.99—Unverified
9Hierarchical Convolutional NetworkAccuracy (10-fold)0.99—Unverified
10SEAAccuracy (10-fold)0.99—Unverified
#ModelMetricClaimedVerifiedStatus
1GA Designed Deep CNNAccuracy0.99—Unverified
2Gray-scale IMG CNNAccuracy (10-fold)0.98—Unverified
3GRU + SVMAccuracy0.85—Unverified
4FFNN + SVMAccuracy0.8—Unverified
5CNN + SVMAccuracy0.77—Unverified
#ModelMetricClaimedVerifiedStatus
1Levit-MCAccuracy96.6—Unverified