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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 226–250 of 431 papers

TitleStatusHype
New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning—0
NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification—0
N-gram Opcode Analysis for Android Malware Detection—0
N-opcode Analysis for Android Malware Classification and Categorization—0
NtMalDetect: A Machine Learning Approach to Malware Detection Using Native API System Calls—0
Obfuscated Malware Detection: Investigating Real-world Scenarios through Memory Analysis—0
Obfuscated Memory Malware Detection—0
OMD: Orthogonal Malware Detection Using Audio, Image, and Static Features—0
On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities—0
On Defending Against Label Flipping Attacks on Malware Detection Systems—0
One-Class SVM with Privileged Information and its Application to Malware Detection—0
Online Clustering of Known and Emerging Malware Families—0
On Security and Sparsity of Linear Classifiers for Adversarial Settings—0
On the Abuse and Detection of Polyglot Files—0
On the Consistency of GNN Explanations for Malware Detection—0
On the Cost of Model-Serving Frameworks: An Experimental Evaluation—0
On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors—0
On the Effectiveness of Interpretable Feedforward Neural Network—0
On the Effectiveness of System API-Related Information for Android Ransomware Detection—0
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
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