SOTAVerified

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 151–175 of 431 papers

TitleStatusHype
Adapting Novelty towards Generating Antigens for Antivirus systems—0
A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms—0
Machine Learning for Detecting Malware in PE Files—0
Traffic Refinery: Cost-Aware Data Representation for Machine Learning on Network Traffic—0
Android Malware Detection using Feature Ranking of Permissions—0
BERTDetect: A Neural Topic Modelling Approach for Android Malware Detection—0
Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers—0
Android Malware Detection using Deep Learning on API Method Sequences—0
Adversarial Samples on Android Malware Detection Systems for IoT Systems—0
Behavioral Malware Classification using Convolutional Recurrent Neural Networks—0
Android Malware Detection Using Autoencoder—0
A Visualized Malware Detection Framework with CNN and Conditional GAN—0
Android Malware Detection Based on RGB Images and Multi-feature Fusion—0
ActDroid: An active learning framework for Android malware detection—0
EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning—0
A two-steps approach to improve the performance of Android malware detectors—0
A Natural Language Processing Approach to Malware Classification—0
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
ATWM: Defense against adversarial malware based on adversarial training—0
Efficient Malware Analysis Using Metric Embeddings—0
A Transformer-Based Framework for Payload Malware Detection and Classification—0
Empirical Quantification of Spurious Correlations in Malware Detection—0
A Natural Language Processing Approach for Instruction Set Architecture Identification—0
Enhanced Attacks on Defensively Distilled Deep Neural Networks—0
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective—0
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