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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 301–325 of 431 papers

TitleStatusHype
Towards a Robust Classifier: An MDL-Based Method for Generating Adversarial Examples—0
Towards Deep Federated Defenses Against Malware in Cloud Ecosystems—0
Towards Interpretable Ensemble Learning for Image-based Malware Detection—0
Towards interpreting ML-based automated malware detection models: a survey—0
Towards Novel Malicious Packet Recognition: A Few-Shot Learning Approach—0
Towards Obfuscated Malware Detection for Low Powered IoT Devices—0
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization—0
Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation—0
Toward the Detection of Polyglot Files—0
Traffic Analytics Development Kits (TADK): Enable Real-Time AI Inference in Networking Apps—0
Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Calls—0
Transformers for End-to-End InfoSec Tasks: A Feasibility Study—0
Trust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection—0
Unmasking the Shadows: Pinpoint the Implementations of Anti-Dynamic Analysis Techniques in Malware Using LLM—0
Unraveling the Key of Machine Learning Solutions for Android Malware Detection—0
Unsupervised representation learning with Hebbian synaptic and structural plasticity in brain-like feedforward neural networks—0
Use of Multi-CNNs for Section Analysis in Static Malware Detection—0
Using Deep Neural Network for Android Malware Detection—0
Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks—0
Virus-MNIST: A Benchmark Malware Dataset—0
Weakly Supervised Anomaly Detection via Knowledge-Data Alignment—0
WebEye - Automated Collection of Malicious HTTP Traffic—0
Word Embedding Techniques for Malware Evolution Detection—0
Would a File by Any Other Name Seem as Malicious?—0
Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions—0
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