Papers Malware Detection
“Malware Detection” 태그가 달린 논문 525편 · 필터 해제
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 DetectionTaming the Security-Energy Paradox: A Green AI Approach to Optimized Android Malware Detection
An increase in advanced Android malware requires the use of deep learning models, which can run on Android devices. But there is a trade-off between security and energy use, as strong detection models can drain the batte…
Malware DetectionLeveraging Interpretable Tsetlin Machine for PDF Malware Detection
In the digital era, Portable Document Format (PDF) is one of the most widely used file formats for storing and exchanging digital documents due to its platform independence and rich functionality. However, these same cap…
Computational EfficiencyMalware 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 DetectionA 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 DetectionViPER: 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 DetectionAMD-FCG: An Enhanced Function Call Graph Dataset with Integrated Topological Features for Malware Detection and Classification
As malware illustrates a complex structure and behavior, detection of these has been a significant challenge in the domain of cybersecurity along with related services in daily life. So, it becomes crucial to have a reli…
Malware DetectionMalTree: Tracing Malware Evolution from Embeddings at Scale
Malware detection remains largely reactive: machine learning models trained on known samples degrade as threats evolve. Understanding evolutionary relationships among malware families can inform proactive defense, but tr…
Malware 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 DetectionClawHub Security Signals: When VirusTotal, Static Analysis, and SkillSpector Disagree
Agent skills extend AI agents with reusable instructions, tools, scripts, references, and workflows, establishing a security boundary distinct from both model safety and traditional package-malware detection. ClawHub Sec…
Malware 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 DetectionSEED: Semi-supervised Continual MalwarE Detection for Tackling ConcEpt Drift on a BuDget
Machine learning based malware detectors become obsolete over time due to concept drift in benign and malware applications. Recent methods rely on fully labeled data and use hierarchical contrastive loss (HCL) with activ…
Continual LearningMalware DetectionActive LearningAdversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection
We present a longitudinal, drift-aware evaluation of adversarial robustness across more than a decade of Android applications using static and dynamic feature representations extracted from emulator and real-device execu…
Adversarial RobustnessMalware DetectionConcept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection
Android malware detectors often degrade after deployment because of concept drift, while full retraining at each maintenance step is costly. We propose a chronological adaptive maintenance framework that models deploymen…
Self-Supervised LearningReinforcement LearningMalware DetectionFreeMOCA: Memory-Free Continual Learning for Malicious Code Analysis
As over 200 million new malware samples are identified each year, antivirus systems must continuously adapt to the evolving threat landscape. However, retraining solely on new samples leads to catastrophic forgetting and…
Continual LearningMalware Detection