Papers Android Malware Detection
“Android Malware Detection” 태그가 달린 논문 76편 · 필터 해제
MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptation
We present MADCAT, a self-supervised approach designed to address the concept drift problem in malware detection. MADCAT employs an encoder-decoder architecture and works by test-time training of the encoder on a small, …
Android Malware DetectionDecoderMalware DetectionTest-time AdaptationEvaluating the Robustness of Adversarial Defenses in Malware Detection Systems
Machine learning is a key tool for Android malware detection, effectively identifying malicious patterns in apps. However, ML-based detectors are vulnerable to evasion attacks, where small, crafted changes bypass detecti…
Adversarial AttackAdversarial RobustnessAndroid Malware DetectionMalware DetectionOpCode-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 DetectionBERTDetect: A Neural Topic Modelling Approach for Android Malware Detection
Web access today occurs predominantly through mobile devices, with Android representing a significant share of the mobile device market. This widespread usage makes Android a prime target for malicious attacks. Despite e…
Android Malware DetectionClusteringMalware DetectionTrust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection
Machine learning (ML) malware detectors rely heavily on crowd-sourced AntiVirus (AV) labels, with platforms like VirusTotal serving as a trusted source of malware annotations. But what if attackers could manipulate these…
Android Malware DetectionMalware DetectionLAMD: Context-driven Android Malware Detection and Classification with LLMs
The rapid growth of mobile applications has escalated Android malware threats. Although there are numerous detection methods, they often struggle with evolving attacks, dataset biases, and limited explainability. Large L…
Android Malware DetectionMalware AnalysisMalware DetectionDefending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems
Android malware presents a persistent threat to users' privacy and data integrity. To combat this, researchers have proposed machine learning-based (ML-based) Android malware detection (AMD) systems. However, adversarial…
Adversarial RobustnessAndroid Malware DetectionMalware DetectionCrystal ball: From innovative attacks to attack effectiveness classifier
Android OS is one of the most popular operating systems worldwide, making it a desirable target for malware attacks. Some of the latest and most important defensive systems are based on machine learning (ML) and cyberc…
Android Malware DetectionMalware 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+1MASKDROID: Robust Android Malware Detection with Masked Graph Representations
Android malware attacks have posed a severe threat to mobile users, necessitating a significant demand for the automated detection system. Among the various tools employed in malware detection, graph representations (e.g…
Android Malware DetectionGraph Neural NetworkMalware DetectionDecoding Android Malware with a Fraction of Features: An Attention-Enhanced MLP-SVM Approach
The escalating sophistication of Android malware poses significant challenges to traditional detection methods, necessitating innovative approaches that can efficiently identify and classify threats with high precision. …
Android Malware DetectionMalware DetectionRevisiting 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+2Android Malware Detection Based on RGB Images and Multi-feature Fusion
With the widespread adoption of smartphones, Android malware has become a significant challenge in the field of mobile device security. Current Android malware detection methods often rely on feature engineering to const…
Android Malware DetectionEdge DetectionFeature Engineeringimage-classification+2DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware
Recent advancements in ML and DL have significantly improved Android malware detection, yet many methodologies still rely on basic static analysis, bytecode, or function call graphs that often fail to capture complex mal…
Android Malware DetectionMalware DetectionMultiple Instance LearningRepresentation LearningImproving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations
Machine learning (ML) has demonstrated significant advancements in Android malware detection (AMD); however, the resilience of ML against realistic evasion attacks remains a major obstacle for AMD. One of the primary fac…
Adversarial RobustnessAndroid Malware DetectionDomain AdaptationMalware DetectionImproving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks
Generative Adversarial Networks (GANs) have demonstrated their versatility across various applications, including data augmentation and malware detection. This research explores the effectiveness of utilizing GAN-generat…
Android Malware DetectionData AugmentationGenerative Adversarial NetworkMalware DetectionUnraveling the Key of Machine Learning Solutions for Android Malware Detection
Android malware detection serves as the front line against malicious apps. With the rapid advancement of machine learning (ML), ML-based Android malware detection has attracted increasing attention due to its capability …
Android Malware DetectionFeature EngineeringMalware DetectionActDroid: An active learning framework for Android malware detection
The growing popularity of Android requires malware detection systems that can keep up with the pace of new software being released. According to a recent study, a new piece of malware appears online every 12 seconds. To …
Active LearningAndroid Malware DetectionMalware DetectionAndroid Malware Detection with Unbiased Confidence Guarantees
The impressive growth of smartphone devices in combination with the rising ubiquity of using mobile platforms for sensitive applications such as Internet banking, have triggered a rapid increase in mobile malware. In rec…
Android Malware DetectionConformal PredictionMalware DetectionvalidMalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks
Machine learning (ML) has gained significant adoption in Android malware detection to address the escalating threats posed by the rapid proliferation of malware attacks. However, recent studies have revealed the inherent…
Adversarial PurificationAndroid Malware DetectionDenoisingMalware Detection+1