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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 131–140 of 146 papers

Title	Date	Tasks	Status
Defending Malware Classification Networks Against Adversarial Perturbations with Non-Negative Weight Restrictions	Jun 23, 2018	ClassificationGeneral Classification	—Unverified
Classification of Malware by Using Structural Entropy on Convolutional Neural Networks	Apr 27, 2018	General ClassificationMalware Classification	CodeCode Available
Generative Models for Spear Phishing Posts on Social Media	Feb 14, 2018	ClusteringComputer Security	—Unverified
Learning a Neural-network-based Representation for Open Set Recognition	Feb 12, 2018	General ClassificationMalware Classification	CodeCode Available
Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification	Dec 31, 2017	General ClassificationMalware Classification	CodeCode Available
Computer activity learning from system call time series	Nov 6, 2017	General ClassificationMalware Classification	—Unverified
Convolutional Neural Network for Classification of Malware Assembly Code	Oct 27, 2017	ClassificationGeneral Classification	CodeCode Available
On the (Statistical) Detection of Adversarial Examples	Feb 21, 2017	Intrusion DetectionMalware Classification	—Unverified
SoK: Applying Machine Learning in Security - A Survey	Nov 10, 2016	BIG-bench Machine LearningIntrusion Detection	—Unverified
A multi-task learning model for malware classification with useful file access pattern from API call sequence	Oct 19, 2016	ClassificationDocument Classification	—Unverified

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All datasets Microsoft Malware Classification Challenge Malimg Dataset MaleVis

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MalConv	Accuracy (10-fold)	9,641	—	Unverified
2	TPOT Classifier	Accuracy (5-fold)	98.94	—	Unverified
3	CNN BiLSTM - Reb Sampl	Accuracy (5-fold)	98.2	—	Unverified
4	Ahmadi 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
5	HYDRA	Accuracy (10-fold)	1	—	Unverified
6	Zhang 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
7	Orthrus	Accuracy (10-fold)	0.99	—	Unverified
8	Opcode-based Shallow CNN	Accuracy (10-fold)	0.99	—	Unverified
9	Hierarchical Convolutional Network	Accuracy (10-fold)	0.99	—	Unverified
10	SEA	Accuracy (10-fold)	0.99	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GA Designed Deep CNN	Accuracy	0.99	—	Unverified
2	Gray-scale IMG CNN	Accuracy (10-fold)	0.98	—	Unverified
3	GRU + SVM	Accuracy	0.85	—	Unverified
4	FFNN + SVM	Accuracy	0.8	—	Unverified
5	CNN + SVM	Accuracy	0.77	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Levit-MC	Accuracy	96.6	—	Unverified