Papers Malware Classification
“Malware Classification” 태그가 달린 논문 175편 · 필터 해제
Adapter-Based Few-Shot Continual Learning for Malicious Packet Recognition
The continual evolution of malware variants necessitates detection systems that can adapt to new threats without retraining from scratch. However, continually updating models on new data often leads to catastrophic forge…
Few-Shot Class-Incremental LearningSelf-Supervised LearningMalware ClassificationContinual LearningConcept 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 DetectionThreatVisionAI: A Hybrid CNN-ViT Framework for Image-Based Malware Classification
Traditional malware detection methods struggle to generalize to obfuscated or previously unseen threats. This paper introduces ThreatVisionAI, a hybrid malware family classification framework that integrates a raw-image …
Malware ClassificationMalware DetectionHamm-Grams: An Algorithm for Mining Regular Expressions of Bytes
Malware poses a critical and ever-evolving threat, and robust and effective systems for detecting and classifying malware are of essential importance. $n$-grams features are among the common static features used in effec…
Malware ClassificationA Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution
Malware classification remains a challenging problem due to its inherent heterogeneity, the presence of packed binaries, and the diverse distribution of malware families. Traditional single-model detection mechanisms oft…
Malware ClassificationMalware DetectionMulti-View Decompilation for LLM-Based Malware Classification
Malware analysts often inspect compiled binaries through decompiled pseudo-C, when source code is unavailable. Recent work suggests that large language models (LLMs) can assist this process by classifying decompiled code…
Malware ClassificationViPER: Vision-based Packing-Aware Encoder for Robust Malware Detection
Visualization-based malware detection maps raw binary bytes to grayscale images and applies learned visual classifiers, providing an evasion-resistant and disassembly-free alternative to conventional analysis pipelines. …
Malware ClassificationMalware DetectionA Hybrid Approach For Malware Classification Using Secondary Features Fusion
The number of malware (either variant or novel) is rapidly increasing, making malware detection and mitigation a complex problem. One approach to improving malware mitigation is automatic detection and malware family cla…
Multi-class ClassificationMalware ClassificationMalware DetectionBuilding an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation
We present a dataset of adversarial malware samples derived from the public RawMal-TF collection of real-world malware binaries. Using a suite of adversarial malware generators, we construct two sets of adversarial PE fi…
Malware ClassificationMalware DetectionDetecting Concept Drift in Evolving Malware Families Using Rule-Based Classifier Representations
This work proposes a structural approach to concept drift detection in malware classification using decision tree rulesets. Classifiers are trained across temporal windows on the EMBER2024 dataset, and drift is quantifie…
Malware ClassificationFeature ImportanceGenerating Synthetic Malware Samples Using Generative AI
Malware attacks have a significant negative impact on organizations of varied scales in the field of cybersecurity. Recently, malware researchers have increasingly turned to machine learning techniques to combat sophisti…
Malware ClassificationEXHIB: A Benchmark for Realistic and Diverse Evaluation of Function Similarity in the Wild
Binary Function Similarity Detection (BFSD) is a core problem in software security, supporting tasks such as vulnerability analysis, malware classification, and patch provenance. In the past few decades, numerous models …
Malware ClassificationFARM: Few-shot Adaptive Malware Family Classification under Concept Drift
Malware classification models often suffer performance degradation under concept drift due to evolving threat landscapes and the emergence of novel malware families. This paper presents FARM (Few-shot Adaptive Recognitio…
Malware ClassificationPDFInspect: A Unified Feature Extraction Framework for Malicious Document Detection
The increasing prevalence of malicious Portable Document Format (PDF) files necessitates robust and comprehensive feature extraction techniques for effective detection and analysis. This work presents a unified framework…
Malware ClassificationAnomaly DetectionMalware Classification using Diluted Convolutional Neural Network with Fast Gradient Sign Method
Android malware has become an increasingly critical threat to organizations, society and individuals, posing significant risks to privacy, data security and infrastructure. As malware continues to evolve in terms of comp…
Malware ClassificationBetter Call Graphs: A New Dataset of Function Call Graphs for Malware Classification
Function call graphs (FCGs) have emerged as a powerful abstraction for malware detection, capturing the behavioral structure of applications beyond surface-level signatures. Their utility in traditional program analysis …
Malware ClassificationMalware DetectionEnhancing Decision-Making in Windows PE Malware Classification During Dataset Shifts with Uncertainty Estimation
Artificial intelligence techniques have achieved strong performance in classifying Windows Portable Executable (PE) malware, but their reliability often degrades under dataset shifts, leading to misclassifications with s…
Malware ClassificationMAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection and Classification
Out of distribution (OOD) detection remains a critical challenge in malware classification due to the substantial intra family variability introduced by polymorphic and metamorphic malware variants. Most existing deep le…
Out of Distribution (OOD) DetectionMalware ClassificationMalware DetectionSynthetic Data: AI's New Weapon Against Android Malware
The ever-increasing number of Android devices and the accelerated evolution of malware, reaching over 35 million samples by 2024, highlight the critical importance of effective detection methods. Attackers are now using …
Synthetic Data GenerationComputational EfficiencyMalware ClassificationMalware DetectionAccuracy and Efficiency Trade-Offs in LLM-Based Malware Detection and Explanation: A Comparative Study of Parameter Tuning vs. Full Fine-Tuning
This study examines whether Low-Rank Adaptation (LoRA) fine-tuned Large Language Models (LLMs) can approximate the performance of fully fine-tuned models in generating human-interpretable decisions and explanations for m…
Malware ClassificationSemantic SimilarityMalware Detection