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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 51–75 of 431 papers

TitleStatusHype
A Novel Approach to Malicious Code Detection Using CNN-BiLSTM and Feature Fusion—0
MASKDROID: Robust Android Malware Detection with Masked Graph RepresentationsCode1
Decoding Android Malware with a Fraction of Features: An Attention-Enhanced MLP-SVM Approach—0
Accelerating Malware Classification: A Vision Transformer SolutionCode0
Packet Inspection Transformer: A Self-Supervised Journey to Unseen Malware Detection with Few Samples—0
A Visualized Malware Detection Framework with CNN and Conditional GAN—0
Towards Novel Malicious Packet Recognition: A Few-Shot Learning Approach—0
Revisiting Static Feature-Based Android Malware Detection—0
Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges—0
DetectBERT: Towards Full App-Level Representation Learning to Detect Android MalwareCode0
Android Malware Detection Based on RGB Images and Multi-feature Fusion—0
Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious CorrelationsCode0
Obfuscated Memory Malware Detection—0
Natural Language Outlines for Code: Literate Programming in the LLM Era—0
A Survey of Malware Detection Using Deep Learning—0
Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions—0
A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes—0
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach—0
On the Abuse and Detection of Polyglot Files—0
Unsupervised representation learning with Hebbian synaptic and structural plasticity in brain-like feedforward neural networks—0
Hyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN PerformanceCode0
SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines—0
A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection—0
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities—0
Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Calls—0
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