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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 376–400 of 431 papers

TitleStatusHype
pAElla: Edge-AI based Real-Time Malware Detection in Data CentersCode0
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
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware DetectionCode0
Robust Neural Malware Detection Models for Emulation Sequence LearningCode0
Evading Malware Classifiers via Monte Carlo Mutant Feature DiscoveryCode0
Evaluating the Robustness of Adversarial Defenses in Malware Detection SystemsCode0
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced DatasetsCode0
DetectBERT: Towards Full App-Level Representation Learning to Detect Android MalwareCode0
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
DeepXplore: Automated Whitebox Testing of Deep Learning SystemsCode0
Deep Transfer Learning for Static Malware ClassificationCode0
MaMaDroid2.0 -- The Holes of Control Flow GraphsCode0
DeepSign: Deep Learning for Automatic Malware Signature Generation and ClassificationCode0
Automatic Malware Description via Attribute Tagging and Similarity EmbeddingCode0
Fast & Furious: Modelling Malware Detection as Evolving Data StreamsCode0
Crystal ball: From innovative attacks to attack effectiveness classifierCode0
Evaluating Explanation Methods for Deep Learning in SecurityCode0
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
Generating Adversarial Malware Examples for Black-Box Attacks Based on GANCode0
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