SOTAVerified

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 301–350 of 431 papers

TitleStatusHype
Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weights—0
Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection—0
MalPhase: Fine-Grained Malware Detection Using Network Flow Data—0
MalProtect: Stateful Defense Against Adversarial Query Attacks in ML-based Malware Detection—0
Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretability—0
AI-based Malware and Ransomware Detection Models—0
Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network—0
Malware Classification using Deep Neural Networks: Performance Evaluation and Applications in Edge Devices—0
Malware Classification Using Long Short-Term Memory Models—0
Malware Classification with GMM-HMM Models—0
Malware Detection and Prevention using Artificial Intelligence Techniques—0
Malware Detection at the Edge with Lightweight LLMs: A Performance Evaluation—0
Malware Detection in Docker Containers: An Image is Worth a Thousand Logs—0
Malware Detection in IOT Systems Using Machine Learning Techniques—0
Malware Detection using Artificial Bee Colony Algorithm—0
Malware Detection Using Dynamic Birthmarks—0
Malware Detection using Machine Learning and Deep Learning—0
Malware Evasion Attack and Defense—0
Malware families discovery via Open-Set Recognition on Android manifest permissions—0
Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection—0
Mask Off: Analytic-based Malware Detection By Transfer Learning and Model Personalization—0
MDEA: Malware Detection with Evolutionary Adversarial Learning—0
MERLIN -- Malware Evasion with Reinforcement LearnINg—0
Metamorphic Malware Evolution: The Potential and Peril of Large Language Models—0
ML-based IoT Malware Detection Under Adversarial Settings: A Systematic Evaluation—0
MORPH: Towards Automated Concept Drift Adaptation for Malware Detection—0
Multifamily Malware Models—0
Natural Language Outlines for Code: Literate Programming in the LLM Era—0
Optimized Deep Learning Models for Malware Detection under Concept Drift—0
New Approach to Malware Detection Using Optimized Convolutional Neural Network—0
Benchmark Static API Call Datasets for Malware Family Classification—0
New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning—0
NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification—0
N-gram Opcode Analysis for Android Malware Detection—0
N-opcode Analysis for Android Malware Classification and Categorization—0
NtMalDetect: A Machine Learning Approach to Malware Detection Using Native API System Calls—0
Obfuscated Malware Detection: Investigating Real-world Scenarios through Memory Analysis—0
Obfuscated Memory Malware Detection—0
OMD: Orthogonal Malware Detection Using Audio, Image, and Static Features—0
On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities—0
On Defending Against Label Flipping Attacks on Malware Detection Systems—0
One-Class SVM with Privileged Information and its Application to Malware Detection—0
Online Clustering of Known and Emerging Malware Families—0
On Security and Sparsity of Linear Classifiers for Adversarial Settings—0
On the Abuse and Detection of Polyglot Files—0
On the Consistency of GNN Explanations for Malware Detection—0
On the Cost of Model-Serving Frameworks: An Experimental Evaluation—0
On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors—0
On the Effectiveness of Interpretable Feedforward Neural Network—0
On the Effectiveness of System API-Related Information for Android Ransomware Detection—0
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