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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 276–300 of 431 papers

TitleStatusHype
Lightweight IoT Malware Detection Solution Using CNN Classification—0
Data Augmentation Based Malware Detection using Convolutional Neural NetworksCode1
Orthrus: A Bimodal Learning Architecture for Malware ClassificationCode0
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
Robust and Accurate Authorship Attribution via Program Normalization—0
Maat: Automatically Analyzing VirusTotal for Accurate Labeling and Effective Malware Detection—0
Towards Accurate Labeling of Android Apps for Reliable Malware Detection—0
Feature Extraction for Novelty Detection in Network Traffic—0
Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware DetectionCode1
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacksCode1
An Efficient Approach For Malware Detection Using PE Header SpecificationCode0
Adversarial Feature Selection against Evasion AttacksCode0
Arms Race in Adversarial Malware Detection: A Survey—0
HYDRA: A multimodal deep learning framework for malware classificationCode1
A Review of Computer Vision Methods in Network Security—0
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?—0
How to 0wn the NAS in Your Spare TimeCode0
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
pAElla: Edge-AI based Real-Time Malware Detection in Data CentersCode0
AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks—0
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