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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 351–400 of 431 papers

TitleStatusHype
On the impact of dataset size and class imbalance in evaluating machine-learning-based windows malware detection techniques—0
On the Security Risks of ML-based Malware Detection Systems: A Survey—0
OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques—0
Open Image Content Disarm And Reconstruction—0
Optimization of Lightweight Malware Detection Models For AIoT Devices—0
Optimized Approaches to Malware Detection: A Study of Machine Learning and Deep Learning Techniques—0
ORSA: Outlier Robust Stacked Aggregation for Best- and Worst-Case Approximations of Ensemble Systems\—0
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks—0
Parallel Instance Filtering for Malware Detection—0
PDF-Malware: An Overview on Threats, Detection and Evasion Attacks—0
PECAN: A Deterministic Certified Defense Against Backdoor Attacks—0
PhilaeX: Explaining the Failure and Success of AI Models in Malware Detection—0
Powershell malware detection method based on features combination—0
Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware—0
Practical Fast Gradient Sign Attack against Mammographic Image Classifier—0
Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines—0
R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections—0
Efficient Formal Safety Analysis of Neural NetworksCode0
Behavioural Reports of Multi-Stage MalwareCode0
Malware Detection based on API callsCode0
Efficient Concept Drift Handling for Batch Android Malware Detection ModelsCode0
Dynamic Malware Analysis with Feature Engineering and Feature LearningCode0
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced DatasetsCode0
Interpreting Machine Learning Malware Detectors Which Leverage N-gram AnalysisCode0
pAElla: Edge-AI based Real-Time Malware Detection in Data CentersCode0
Evaluating Explanation Methods for Deep Learning in SecurityCode0
When a RF Beats a CNN and GRU, Together -- A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic ClassificationCode0
Adversarially Robust Learning with Optimal Transport Regularized DivergencesCode0
Robust Neural Malware Detection Models for Emulation Sequence LearningCode0
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware DetectionCode0
Crystal ball: From innovative attacks to attack effectiveness classifierCode0
Evading Malware Classifiers via Monte Carlo Mutant Feature DiscoveryCode0
Evaluating the Robustness of Adversarial Defenses in Malware Detection SystemsCode0
Evasion Attacks against Machine Learning at Test TimeCode0
DeepXplore: Automated Whitebox Testing of Deep Learning SystemsCode0
Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine LearningCode0
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized DeletionCode0
The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement LearningCode0
Deep Transfer Learning for Static Malware ClassificationCode0
DeepSign: Deep Learning for Automatic Malware Signature Generation and ClassificationCode0
MaMaDroid2.0 -- The Holes of Control Flow GraphsCode0
Classification with Costly Features in Hierarchical Deep SetsCode0
Automatic Malware Description via Attribute Tagging and Similarity EmbeddingCode0
Fast & Furious: Modelling Malware Detection as Evolving Data StreamsCode0
Cross-Language Binary-Source Code Matching with Intermediate RepresentationsCode0
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware DetectionCode0
Transfer Learning for Image-Based Malware ClassificationCode0
BagFlip: A Certified Defense against Data PoisoningCode0
Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput ComputingCode0
ALOHA: Auxiliary Loss Optimization for Hypothesis AugmentationCode0
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