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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 26–50 of 431 papers

TitleStatusHype
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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