Malware Detection
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Benchmarks
Android Malware Dataset
MalNet
Most implemented
Malware Detection by Eating a Whole EXE
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
Automatic Malware Description via Attribute Tagging and Similarity Embedding
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
Papers
A Hybrid Approach to Malware Detection: Integrating Few-Shot Model-Agnostic Meta-Learning with Autoencoders
Ransomware has emerged as a major cybersecurity threat, with incidents increasing in frequency and impact across critical sectors. These attacks are typically launched through phishing emails, malicious downloads, or exp…
Binary ClassificationMalware DetectionA Structured State Space Sequence Model for Multi-Class Classification of Malware
By 2030, Internet of Things (IoT) devices are projected to reach 40 billion, with fast-paced technological advancements in fields such as industry, healthcare, agriculture, automobiles, and building/home automation syste…
Multi-class ClassificationMalware DetectionREPLICANT: Learning Policies for Evading and Hardening Malware Detectors
To determine the real-world effectiveness of machine learning based malware detection, it is vital to evaluate its robustness against highly capable adversaries. However, state-of-the-art attacks do not effectively model…
Reinforcement LearningMalware DetectionConcept Drift Detection and Adaptive Retraining of Malware Classification Models
Concept drift refers to changes over time in the statistical properties of data, as compared to the data that was used to train a learning model. Machine learning models for malware detection or classification are partic…
Malware ClassificationMalware DetectionShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection
The rapid advancement of modern technology has led to a significant increase in the use of smart devices, such as smartphones and tablets, resulting in the widespread adoption of mobile applications. Although application…
Malware DetectionGuarding Organizations Against Malware Risk: A Novel Graph-Based Malware Detection Method
Organizational digitalization expands cybersecurity risks, making cybersecurity an increasingly important research area in Information Systems (IS). Among these risks, malware has become a pervasive and destructive threa…
Graph Representation LearningMalware Detection