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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 26–50 of 431 papers

TitleStatusHype
subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large GraphsCode1
Against All Odds: Winning the Defense Challenge in an Evasion Competition with DiversificationCode1
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-ArtCode1
Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware DetectionCode1
Probabilistic Jacobian-based Saliency Maps AttacksCode1
Federated Learning for Malware Detection in IoT DevicesCode1
Avast-CTU Public CAPE DatasetCode1
Classifying Sequences of Extreme Length with Constant Memory Applied to Malware DetectionCode1
Continuous Learning for Android Malware DetectionCode1
Dataset Optimization Strategies for MalwareTraffic DetectionCode1
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model PerformanceCode1
NetML: A Challenge for Network Traffic AnalyticsCode1
Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge SettingCode1
heterogeneous temporal graph transformer: an intelligent system for evolving android malware detectionCode1
HYDRA: A multimodal deep learning framework for malware classificationCode1
Learning from Context: Exploiting and Interpreting File Path Information for Better Malware DetectionCode1
Learning Security Classifiers with Verified Global Robustness PropertiesCode1
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified RobustnessCode1
MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion AttacksCode1
AiDroid: When Heterogeneous Information Network Marries Deep Neural Network for Real-time Android Malware Detection—0
A Hierarchical Convolutional Neural Network for Malware Classification—0
Agent-based Vs Agent-less Sandbox for Dynamic Behavioral Analysis—0
Feature Extraction for Novelty Detection in Network Traffic—0
Adaptive and Scalable Android Malware Detection through Online Learning—0
A Feature Set of Small Size for the PDF Malware Detection—0
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