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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 251–275 of 431 papers

TitleStatusHype
Detection of Malicious Android Applications: Classical Machine Learning vs. Deep Neural Network Integrated with Clustering—0
Virus-MNIST: A Benchmark Malware Dataset—0
Collective Intelligence: Decentralized Learning for Android Malware Detection in IoT with Blockchain—0
A Non-Intrusive Machine Learning Solution for Malware Detection and Data Theft Classification in Smartphones—0
MalNet: A Large-Scale Image Database of Malicious SoftwareCode1
Robust Android Malware Detection System against Adversarial Attacks using Q-Learning—0
Malware Detection Using Frequency Domain-Based Image Visualization and Deep LearningCode1
A novel DL approach to PE malware detection: exploring Glove vectorization, MCC_RCNN and feature fusion—0
Towards interpreting ML-based automated malware detection models: a survey—0
Towards Interpretable Ensemble Learning for Image-based Malware Detection—0
Echelon: Two-Tier Malware Detection for Raw Executables to Reduce False Alarms—0
Powershell malware detection method based on features combination—0
Learning to Search for Fast Maximum Common Subgraph Detection—0
A Novel Resampling Technique for Imbalanced Dataset Optimization—0
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS—0
Classifying Sequences of Extreme Length with Constant Memory Applied to Malware DetectionCode1
Beyond the Hype: A Real-World Evaluation of the Impact and Cost of Machine Learning-Based Malware DetectionCode0
Malware Detection using Artificial Bee Colony Algorithm—0
A survey on practical adversarial examples for malware classifiers—0
Towards Obfuscated Malware Detection for Low Powered IoT Devices—0
Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers—0
Traffic Refinery: Cost-Aware Data Representation for Machine Learning on Network Traffic—0
Getting Passive Aggressive About False Positives: Patching Deployed Malware Detectors—0
Dos and Don'ts of Machine Learning in Computer Security—0
Against All Odds: Winning the Defense Challenge in an Evasion Competition with DiversificationCode1
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