Papers Malware Classification
“Malware Classification” 태그가 달린 논문 175편 · 필터 해제
Cluster Analysis and Concept Drift Detection in Malware
Concept drift refers to gradual or sudden changes in the properties of data that affect the accuracy of machine learning models. In this paper, we address the problem of concept drift detection in the malware domain. Spe…
ClusteringDrift DetectionMalware ClassificationDeep Learning-Driven Malware Classification with API Call Sequence Analysis and Concept Drift Handling
Malware classification in dynamic environments presents a significant challenge due to concept drift, where the statistical properties of malware data evolve over time, complicating detection efforts. To address this iss…
ClassificationDeep LearningMalware ClassificationMultimodal Techniques for Malware Classification
The threat of malware is a serious concern for computer networks and systems, highlighting the need for accurate classification techniques. In this research, we experiment with multimodal machine learning approaches for …
ClassificationMalware ClassificationMalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware Classification
Continual Learning (CL) for malware classification tackles the rapidly evolving nature of malware threats and the frequent emergence of new types. Generative Replay (GR)-based CL systems utilize a generative model to pro…
class-incremental learningClass Incremental LearningContinual LearningIncremental Learning+1Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network
The proliferation of malware variants poses a significant challenges to traditional malware detection approaches, such as signature-based methods, necessitating the development of advanced machine learning techniques. In…
Malware ClassificationMalware DetectionImage-Based Malware Classification Using QR and Aztec Codes
In recent years, the use of image-based techniques for malware detection has gained prominence, with numerous studies demonstrating the efficacy of deep learning approaches such as Convolutional Neural Networks (CNN) in …
Feature EngineeringMalware ClassificationMalware DetectionXAI and Android Malware Models
Android malware detection based on machine learning (ML) and deep learning (DL) models is widely used for mobile device security. Such models offer benefits in terms of detection accuracy and efficiency, but it is often …
Android Malware DetectionExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)Malware Classification+1Accelerating Malware Classification: A Vision Transformer Solution
The escalating frequency and scale of recent malware attacks underscore the urgent need for swift and precise malware classification in the ever-evolving cybersecurity landscape. Key challenges include accurately categor…
Classificationimage-classificationImage ClassificationMalware Classification+2MalMixer: Few-Shot Malware Classification with Retrieval-Augmented Semi-Supervised Learning
Recent growth and proliferation of malware have tested practitioners ability to promptly classify new samples according to malware families. In contrast to labor-intensive reverse engineering efforts, machine learning ap…
Data AugmentationMalware ClassificationRetrievalRevisiting Static Feature-Based Android Malware Detection
The increasing reliance on machine learning (ML) in computer security, particularly for malware classification, has driven significant advancements. However, the replicability and reproducibility of these results are oft…
Android Malware DetectionComputer SecurityContinual LearningMalware Analysis+2Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges
Machine learning (ML) has rapidly advanced in recent years, revolutionizing fields such as finance, medicine, and cybersecurity. In malware detection, ML-based approaches have demonstrated high accuracy; however, their l…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Malware AnalysisMalware Classification+2Model-agnostic clean-label backdoor mitigation in cybersecurity environments
The training phase of machine learning models is a delicate step, especially in cybersecurity contexts. Recent research has surfaced a series of insidious training-time attacks that inject backdoors in models designed fo…
backdoor defenseClassificationMalware ClassificationAuthAttLyzer-V2: Unveiling Code Authorship Attribution using Enhanced Ensemble Learning Models & Generating Benchmark Dataset
Source Code Authorship Attribution (SCAA) is crucial for software classification because it provides insights into the origin and behavior of software. By accurately identifying the author or group behind a piece of code…
Authorship AttributionEnsemble LearningMalware ClassificationSource Code Authorship AttributionkNN Classification of Malware Data Dependency Graph Features
Explainability in classification results are dependent upon the features used for classification. Data dependency graph features representing data movement are directly correlated with operational semantics, and subject …
ClassificationMalware ClassificationMulti-class ClassificationCombating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification
Machine learning-based Android malware classifiers achieve high accuracy in stationary environments but struggle with concept drift. The rapid evolution of malware, especially with new families, can depress classificatio…
Drift DetectionMalware ClassificationAssemblage: Automatic Binary Dataset Construction for Machine Learning
Binary code is pervasive, and binary analysis is a key task in reverse engineering, malware classification, and vulnerability discovery. Unfortunately, while there exist large corpora of malicious binaries, obtaining hig…
Malware ClassificationCNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls
In this paper, we propose a novel model for a malware classification system based on Application Programming Interface (API) calls and opcodes, to improve classification accuracy. This system uses a novel design of combi…
ClassificationMalware ClassificationTransfer LearningEvaluating the Efficacy of Prompt-Engineered Large Multimodal Models Versus Fine-Tuned Vision Transformers in Image-Based Security Applications
The success of Large Language Models (LLMs) has led to a parallel rise in the development of Large Multimodal Models (LMMs), which have begun to transform a variety of applications. These sophisticated multimodal models …
Malware ClassificationPrompt EngineeringHolographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection
Malware detection is an interesting and valuable domain to work in because it has significant real-world impact and unique machine-learning challenges. We investigate existing long-range techniques and benchmarks and fin…
Malware ClassificationMalware DetectionTESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)
Machine learning (ML) plays a pivotal role in detecting malicious software. Despite the high F1-scores reported in numerous studies reaching upwards of 0.99, the issue is not completely solved. Malware detectors often ex…
Decision MakingMalware Classification