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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 276–300 of 431 papers

TitleStatusHype
Integrating Explainable AI for Effective Malware Detection in Encrypted Network Traffic—0
Intelligent Systems Design for Malware Classification Under Adversarial Conditions—0
Interpreting GNN-based IDS Detections Using Provenance Graph Structural Features—0
Collective Intelligence: Decentralized Learning for Android Malware Detection in IoT with Blockchain—0
IoT Malware Detection Architecture using a Novel Channel Boosted and Squeezed CNN—0
Is feature selection secure against training data poisoning?—0
Knowledge Engineering for Planning-Based Hypothesis Generation—0
LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning—0
LAMD: Context-driven Android Malware Detection and Classification with LLMs—0
Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering—0
Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection—0
Learning Temporal Invariance in Android Malware Detectors—0
Learning to Search for Fast Maximum Common Subgraph Detection—0
Leveraging LSTM and GAN for Modern Malware Detection—0
Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints—0
Leveraging VAE-Derived Latent Spaces for Enhanced Malware Detection with Machine Learning Classifiers—0
Light up that Droid! On the Effectiveness of Static Analysis Features against App Obfuscation for Android Malware Detection—0
Lightweight IoT Malware Detection Solution Using CNN Classification—0
Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection—0
LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach—0
Maat: Automatically Analyzing VirusTotal for Accurate Labeling and Effective Malware Detection—0
Machine learning-based malware detection for IoT devices using control-flow data—0
Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research—0
Machine Learning With Feature Selection Using Principal Component Analysis for Malware Detection: A Case Study—0
MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptation—0
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