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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 251–275 of 431 papers

TitleStatusHype
FGAM:Fast Adversarial Malware Generation Method Based on Gradient Sign—0
Flexible Android Malware Detection Model based on Generative Adversarial Networks with Code Tensor—0
Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking—0
From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy—0
Generative Adversarial Networks and Image-Based Malware Classification—0
Generative Adversarial Networks for Malware Detection: a Survey—0
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities—0
Getting Passive Aggressive About False Positives: Patching Deployed Malware Detectors—0
Graph Neural Network-based Android Malware Classification with Jumping Knowledge—0
HAPSSA: Holistic Approach to PDF Malware Detection Using Signal and Statistical Analysis—0
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples—0
HeNet: A Deep Learning Approach on Intel^ Processor Trace for Effective Exploit Detection—0
Heterogeneous Graph Matching Networks—0
Hidden Markov Models with Random Restarts vs Boosting for Malware Detection—0
High Accuracy Android Malware Detection Using Ensemble Learning—0
Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection—0
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses—0
"How Does It Detect A Malicious App?" Explaining the Predictions of AI-based Android Malware Detector—0
Identification of Significant Permissions for Efficient Android Malware Detection—0
I-MAD: Interpretable Malware Detector Using Galaxy Transformer—0
Image-Based Malware Classification Using QR and Aztec Codes—0
Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks—0
Improving Radioactive Material Localization by Leveraging Cyber-Security Model Optimizations—0
"Influence Sketching": Finding Influential Samples In Large-Scale Regressions—0
Instance Attack:An Explanation-based Vulnerability Analysis Framework Against DNNs for Malware Detection—0
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