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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 101–125 of 431 papers

TitleStatusHype
Detecting DGA domains with recurrent neural networks and side informationCode0
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep LearningCode0
ALOHA: Auxiliary Loss Optimization for Hypothesis AugmentationCode0
Deep learning at the shallow end: Malware classification for non-domain expertsCode0
A learning model to detect maliciousness of portable executable using integrated feature setCode0
Generating Adversarial Malware Examples for Black-Box Attacks Based on GANCode0
A Novel Approach to Malicious Code Detection Using CNN-BiLSTM and Feature Fusion—0
A Non-Intrusive Machine Learning Solution for Malware Detection and Data Theft Classification in Smartphones—0
AiDroid: When Heterogeneous Information Network Marries Deep Neural Network for Real-time Android Malware Detection—0
An MDL-Based Classifier for Transactional Datasets with Application in Malware Detection—0
An investigation of the classifiers to detect android malicious apps—0
A Hierarchical Convolutional Neural Network for Malware Classification—0
Cross-Language Binary-Source Code Matching with Intermediate Representations—0
An investigation of a deep learning based malware detection system—0
Counteracting Concept Drift by Learning with Future Malware Predictions—0
A New Malware Detection System Using a High Performance-ELM method—0
A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection—0
Contextual Weisfeiler-Lehman Graph Kernel For Malware Detection—0
Context-aware, Adaptive and Scalable Android Malware Detection through Online Learning (extended version)—0
A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detection—0
Agent-based Vs Agent-less Sandbox for Dynamic Behavioral Analysis—0
Data Augmentation for Opcode Sequence Based Malware Detection—0
Feature Extraction for Novelty Detection in Network Traffic—0
Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples—0
Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies—0
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