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Malware Detection

Malware Detection is a significant part of endpoint security including workstations, servers, cloud instances, and mobile devices. Malware Detection is used to detect and identify malicious activities caused by malware. With the increase in the variety of malware activities on CMS based websites such as malicious malware redirects on WordPress site (Aka, WordPress Malware Redirect Hack) where the site redirects to spam, being the most widespread, the need for automatic detection and classifier amplifies as well. The signature-based Malware Detection system is commonly used for existing malware that has a signature but it is not suitable for unknown malware or zero-day malware

Source: The Threat of Adversarial Attacks on Machine Learning in Network Security - A Survey

Papers

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Showing 1–50 of 431 papers

TitleStatusHype
EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware ClassifiersCode2
LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift AnalysisCode1
CyberLLMInstruct: A New Dataset for Analysing Safety of Fine-Tuned LLMs Using Cyber Security DataCode1
MASKDROID: Robust Android Malware Detection with Masked Graph RepresentationsCode1
Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4Code1
MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion AttacksCode1
Nebula: Self-Attention for Dynamic Malware AnalysisCode1
Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge SettingCode1
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model PerformanceCode1
Recasting Self-Attention with Holographic Reduced RepresentationsCode1
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified RobustnessCode1
PAD: Towards Principled Adversarial Malware Detection Against Evasion AttacksCode1
Continuous Learning for Android Malware DetectionCode1
UniASM: Binary Code Similarity Detection without Fine-tuningCode1
Avast-CTU Public CAPE DatasetCode1
Self-Supervised Vision Transformers for Malware DetectionCode1
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-ArtCode1
Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?Code1
DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of BytecodeCode1
heterogeneous temporal graph transformer: an intelligent system for evolving android malware detectionCode1
Multi-Task Hierarchical Learning Based Network Traffic AnalyticsCode1
Learning Security Classifiers with Verified Global Robustness PropertiesCode1
Federated Learning for Malware Detection in IoT DevicesCode1
Deep Learning for Android Malware Defenses: a Systematic Literature ReviewCode1
MalNet: A Large-Scale Image Database of Malicious SoftwareCode1
Malware Detection Using Frequency Domain-Based Image Visualization and Deep LearningCode1
Classifying Sequences of Extreme Length with Constant Memory Applied to Malware DetectionCode1
Against All Odds: Winning the Defense Challenge in an Evasion Competition with DiversificationCode1
Data Augmentation Based Malware Detection using Convolutional Neural NetworksCode1
Dataset Optimization Strategies for MalwareTraffic DetectionCode1
Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware DetectionCode1
Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware DetectionCode1
Probabilistic Jacobian-based Saliency Maps AttacksCode1
Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware DetectionCode1
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacksCode1
HYDRA: A multimodal deep learning framework for malware classificationCode1
NetML: A Challenge for Network Traffic AnalyticsCode1
Why an Android App is Classified as Malware? Towards Malware Classification InterpretationCode1
A Framework for Enhancing Deep Neural Networks Against Adversarial MalwareCode1
Mind Your Weight(s): A Large-scale Study on Insufficient Machine Learning Model Protection in Mobile AppsCode1
Learning from Context: Exploiting and Interpreting File Path Information for Better Malware DetectionCode1
Malware Detection by Eating a Whole EXECode1
Learning the PE Header, Malware Detection with Minimal Domain KnowledgeCode1
subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large GraphsCode1
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
Empirical Quantification of Spurious Correlations in Malware Detection—0
Network Threat Detection: Addressing Class Imbalanced Data with Deep Forest—0
System Calls for Malware Detection and Classification: Methodologies and Applications—0
Dynamic Malware Classification of Windows PE Files using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion—0
Adapting Novelty towards Generating Antigens for Antivirus systems—0
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