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

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Most implemented papers

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

Content

A New Burrows Wheeler Transform Markov Distance

Learning a Neural-network-based Representation for Open Set Recognition

Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers

A Hierarchical Convolutional Neural Network for Malware Classification

Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification

Classification of Malware by Using Structural Entropy on Convolutional Neural Networks

Robust Neural Malware Detection Models for Emulation Sequence Learning

Deep learning at the shallow end: Malware classification for non-domain experts

Malware Classification Using Static Disassembly and Machine Learning