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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 351–400 of 431 papers

TitleStatusHype
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization—0
Analysing Safety Risks in LLMs Fine-Tuned with Pseudo-Malicious Cyber Security Data—0
Analysis of Bayesian Classification based Approaches for Android Malware Detection—0
Analyzing CNN Based Behavioural Malware Detection Techniques on Cloud IaaS—0
Analyzing Machine Learning Approaches for Online Malware Detection in Cloud—0
A Natural Language Processing Approach for Instruction Set Architecture Identification—0
A Natural Language Processing Approach to Malware Classification—0
Android Malware Detection Based on RGB Images and Multi-feature Fusion—0
Android Malware Detection Using Autoencoder—0
Android Malware Detection using Deep Learning on API Method Sequences—0
Android Malware Detection using Feature Ranking of Permissions—0
Android Malware Detection Using Machine Learning on Image Patterns—0
Android Malware Detection Using Parallel Machine Learning Classifiers—0
Android Malware Detection with Unbiased Confidence Guarantees—0
Android Security using NLP Techniques: A Review—0
An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content—0
A Neural-based Program Decompiler—0
A New Android Malware Detection Approach Using Bayesian Classification—0
A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detection—0
A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection—0
A New Malware Detection System Using a High Performance-ELM method—0
An investigation of a deep learning based malware detection system—0
An investigation of the classifiers to detect android malicious apps—0
An MDL-Based Classifier for Transactional Datasets with Application in Malware Detection—0
A Non-Intrusive Machine Learning Solution for Malware Detection and Data Theft Classification in Smartphones—0
A Novel Approach to Malicious Code Detection Using CNN-BiLSTM and Feature Fusion—0
A novel DL approach to PE malware detection: exploring Glove vectorization, MCC_RCNN and feature fusion—0
A Novel Malware Detection Mechanism based on Features Extracted from Converted Malware Binary Images—0
A Novel Reinforcement Learning Model for Post-Incident Malware Investigations—0
A Novel Resampling Technique for Imbalanced Dataset Optimization—0
apk2vec: Semi-supervised multi-view representation learning for profiling Android applications—0
Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU) Learning in Cybersecurity—0
A Review of Computer Vision Methods in Network Security—0
A Review on The Use of Deep Learning in Android Malware Detection—0
Artificial Neural Network for Cybersecurity: A Comprehensive Review—0
A short review on Applications of Deep learning for Cyber security—0
Transferable Cost-Aware Security Policy Implementation for Malware Detection Using Deep Reinforcement Learning—0
Assessing Cyclostationary Malware Detection via Feature Selection and Classification—0
Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems—0
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS—0
A Survey of Malware Detection Using Deep Learning—0
A Survey on Cross-Architectural IoT Malware Threat Hunting—0
A Survey on Malware Detection with Graph Representation Learning—0
A survey on practical adversarial examples for malware classifiers—0
A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes—0
A Transformer-Based Framework for Payload Malware Detection and Classification—0
ATWM: Defense against adversarial malware based on adversarial training—0
A two-steps approach to improve the performance of Android malware detectors—0
A Visualized Malware Detection Framework with CNN and Conditional GAN—0
Behavioral Malware Classification using Convolutional Recurrent Neural Networks—0
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