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 401–431 of 431 papers

TitleStatusHype
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved FeaturesCode0
Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity DetectionCode0
Evasion Attacks against Machine Learning at Test Time—0
Context-aware, Adaptive and Scalable Android Malware Detection through Online Learning (extended version)—0
Black-Box Attacks against RNN based Malware Detection AlgorithmsCode0
DeepXplore: Automated Whitebox Testing of Deep Learning SystemsCode0
R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections—0
Semi-supervised classification for dynamic Android malware detection—0
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization—0
EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning—0
Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking—0
Generating Adversarial Malware Examples for Black-Box Attacks Based on GANCode0
A learning model to detect maliciousness of portable executable using integrated feature setCode0
N-gram Opcode Analysis for Android Malware Detection—0
"Influence Sketching": Finding Influential Samples In Large-Scale Regressions—0
A multi-task learning model for malware classification with useful file access pattern from API call sequence—0
Adversary Resistant Deep Neural Networks with an Application to Malware Detection—0
One-Class SVM with Privileged Information and its Application to Malware Detection—0
Randomized Prediction Games for Adversarial Machine Learning—0
Analysis of Bayesian Classification based Approaches for Android Malware Detection—0
A New Android Malware Detection Approach Using Bayesian Classification—0
High Accuracy Android Malware Detection Using Ensemble Learning—0
N-opcode Analysis for Android Malware Classification and Categorization—0
Android Malware Detection Using Parallel Machine Learning Classifiers—0
Adaptive and Scalable Android Malware Detection through Online Learning—0
Contextual Weisfeiler-Lehman Graph Kernel For Malware Detection—0
Adversarial Perturbations Against Deep Neural Networks for Malware Classification—0
Feature Cross-Substitution in Adversarial Classification—0
Knowledge Engineering for Planning-Based Hypothesis Generation—0
Security Evaluation of Support Vector Machines in Adversarial Environments—0
Strategic Planning for Network Data Analysis—0
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