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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 151–175 of 431 papers

TitleStatusHype
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized DeletionCode0
Behavioural Reports of Multi-Stage MalwareCode0
Investigating Feature and Model Importance in Android Malware Detection: An Implemented Survey and Experimental Comparison of ML-Based Methods—0
PECAN: A Deterministic Certified Defense Against Backdoor Attacks—0
New Approach to Malware Detection Using Optimized Convolutional Neural Network—0
Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning—0
A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detection—0
Machine Learning for Detecting Malware in PE Files—0
Efficient Malware Analysis Using Metric Embeddings—0
Transformers for End-to-End InfoSec Tasks: A Feasibility Study—0
Mask Off: Analytic-based Malware Detection By Transfer Learning and Model Personalization—0
Clustering based opcode graph generation for malware variant detection—0
Reliable Malware Analysis and Detection using Topology Data AnalysisCode0
UniASM: Binary Code Similarity Detection without Fine-tuningCode1
Flexible Android Malware Detection Model based on Generative Adversarial Networks with Code Tensor—0
The State-of-the-Art in AI-Based Malware Detection Techniques: A Review—0
Avast-CTU Public CAPE DatasetCode1
Instance Attack:An Explanation-based Vulnerability Analysis Framework Against DNNs for Malware Detection—0
Traffic Analytics Development Kits (TADK): Enable Real-Time AI Inference in Networking Apps—0
Self-Supervised Vision Transformers for Malware DetectionCode1
Design of secure and robust cognitive system for malware detection—0
Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware—0
AI-based Malware and Ransomware Detection Models—0
PhilaeX: Explaining the Failure and Success of AI Models in Malware Detection—0
Parallel Instance Filtering for Malware Detection—0
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