Papers Malware Classification
“Malware Classification” 태그가 달린 논문 175편 · 필터 해제
MH-1M: A 1.34 Million-Sample Comprehensive Multi-Feature Android Malware Dataset for Machine Learning, Deep Learning, Large Language Models, and Threat Intelligence Research
We present MH-1M, one of the most comprehensive and up-to-date datasets for advanced Android malware research. The dataset comprises 1,340,515 applications, encompassing a wide range of features and extensive metadata. T…
Malware ClassificationBEACON: Behavioral Malware Classification with Large Language Model Embeddings and Deep Learning
Malware is becoming increasingly complex and widespread, making it essential to develop more effective and timely detection methods. Traditional static analysis often fails to defend against modern threats that employ co…
Malware ClassificationMalware DetectionBeyond Classification: Evaluating LLMs for Fine-Grained Automatic Malware Behavior Auditing
Automated malware classification has achieved strong detection performance. Yet, malware behavior auditing seeks causal and verifiable explanations of malicious activities -- essential not only to reveal what malware doe…
Malware ClassificationA Comparison of Selected Image Transformation Techniques for Malware Classification
Recently, a considerable amount of malware research has focused on the use of powerful image-based machine learning techniques, which generally yield impressive results. However, before image-based techniques can be appl…
Malware ClassificationSignal-Based Malware Classification Using 1D CNNs
Malware classification is a contemporary and ongoing challenge in cyber-security: modern obfuscation techniques are able to evade traditional static analysis, while dynamic analysis is too resource intensive to be deploy…
Malware ClassificationTowards Quantum Machine Learning for Malicious Code Analysis
Classical machine learning (CML) has been extensively studied for malware classification. With the emergence of quantum computing, quantum machine learning (QML) presents a paradigm-shifting opportunity to improve malwar…
Quantum Machine LearningMalware ClassificationBinary ClassificationMalware DetectionQuantifying the Generalization Gap: A New Benchmark for Out-of-Distribution Graph-Based Android Malware Classification
While graph-based Android malware classifiers achieve over 94% accuracy on standard benchmarks, they exhibit a significant generalization gap under distribution shift, suffering up to 45% performance degradation when enc…
Malware ClassificationMalware DetectionADAPT: A Pseudo-labeling Approach to Combat Concept Drift in Malware Detection
Machine learning models are commonly used for malware classification; however, they suffer from performance degradation over time due to concept drift. Adapting these models to changing data distributions requires freque…
Malware ClassificationMalware DetectionActive LearningAdaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification
Traditional malware detection methods exhibit computational inefficiency due to exhaustive feature extraction requirements, creating accuracy-efficiency trade-offs that limit real-time deployment. We formulate malware cl…
Dimensionality ReductionComputational EfficiencyMalware ClassificationReinforcement LearningMalware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
This paper investigates the application of natural language processing (NLP)-based n-gram analysis and machine learning techniques to enhance malware classification. We explore how NLP can be used to extract and analyze …
Classificationfeature selectionMalware ClassificationSemantic Preprocessing for LLM-based Malware Analysis
In a context of malware analysis, numerous approaches rely on Artificial Intelligence to handle a large volume of data. However, these techniques focus on data view (images, sequences) and not on an expert's view. Notici…
Language ModelingLanguage ModellingLarge Language ModelMalware Analysis+1EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers
A lack of accessible data has historically restricted malware analysis research, and practitioners have relied heavily on datasets provided by industry sources to advance. Existing public datasets are limited by narrow s…
Malware AnalysisMalware ClassificationMalware DetectionDynamic Malware Classification of Windows PE Files using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion
Malware detection and classification remains a topic of concern for cybersecurity, since it is becoming common for attackers to use advanced obfuscation on their malware to stay undetected. Conventional static analysis i…
Malware ClassificationMalware DetectionStructure-based Anomaly Detection and Clustering
Anomaly detection is a fundamental problem in domains such as healthcare, manufacturing, and cybersecurity. This thesis proposes new unsupervised methods for anomaly detection in both structured and streaming data settin…
Anomaly DetectionClusteringMalware ClassificationOpen Set LearningMalware families discovery via Open-Set Recognition on Android manifest permissions
Malware are malicious programs that are grouped into families based on their penetration technique, source code, and other characteristics. Classifying malware programs into their respective families is essential for bui…
Malware ClassificationMalware DetectionOpen Set LearningSemantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings
The rapid evolution of malware variants requires robust classification methods to enhance cybersecurity. While Large Language Models (LLMs) offer potential for generating malware descriptions to aid family classification…
AttributeMalware ClassificationMeta-LearningRobust classificationScalable APT Malware Classification via Parallel Feature Extraction and GPU-Accelerated Learning
This paper presents an underlying framework for both automating and accelerating malware classification, more specifically, mapping malicious executables to known Advanced Persistent Threat (APT) groups. The main feature…
GPUMalware ClassificationOpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques
This technical report presents a comprehensive analysis of malware classification using OpCode sequences. Two distinct approaches are evaluated: traditional machine learning using n-gram analysis with Support Vector Mach…
Android Malware DetectionDeep LearningMalware ClassificationMalware DetectionImbalanced malware classification: an approach based on dynamic classifier selection
In recent years, the rise of cyber threats has emphasized the need for robust malware detection systems, especially on mobile devices. Malware, which targets vulnerabilities in devices and user data, represents a substan…
Malware ClassificationMalware DetectionMalware Classification from Memory Dumps Using Machine Learning, Transformers, and Large Language Models
This study investigates the performance of various classification models for a malware classification task using different feature sets and data configurations. Six models-Logistic Regression, K-Nearest Neighbors (KNN), …
Deep Learningfeature selectionFew-Shot LearningMalware Classification