Malware Classification
41 papers with code • 2 benchmarks • 5 datasets
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
Benchmarks
These leaderboards are used to track progress in Malware Classification
EMBER
Malimg
IoT-23
Subtasks
Latest papers with no code
Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection
Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real world problems.
Quantum Machine Learning for Malware Classification
We implement two models of Quantum Machine Learning algorithms, and we compare them to classical models for the classification of a dataset composed of malicious and benign executable files.
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?
With the increasing number and sophistication of malware attacks, malware detection systems based on machine learning (ML) grow in importance.
A Comparison of Graph Neural Networks for Malware Classification
Traditional detection strategies, such as signature scanning, rely on manual analysis of malware to extract relevant features, which is labor intensive and requires expert knowledge.
Lempel-Ziv Networks
Recurrent neural nets have been successful in processing sequences for a number of tasks; however, they are known to be both ineffective and computationally expensive when applied to very long sequences.
A Novel Feature Representation for Malware Classification
In this study we have presented a novel feature representation for malicious programs that can be used for malware classification.
Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification
In recent years, deep Convolutional Neural Networks (CNNs) have shown great potential in malware classification.
AI-based Malware and Ransomware Detection Models
Cybercrime is one of the major digital threats of this century.
Generative Adversarial Networks and Image-Based Malware Classification
We also evaluate the utility of the GAN generative model for adversarial attacks on image-based malware detection.
Representation learning with function call graph transformations for malware open set recognition
In this paper, we introduce a self-supervised pre-training approach for the OSR problem in malware classification.
