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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 101–150 of 431 papers

TitleStatusHype
Evaluating Explanation Methods for Deep Learning in SecurityCode0
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware DetectionCode0
Dynamic Malware Analysis with Feature Engineering and Feature LearningCode0
How to Train your Antivirus: RL-based Hardening through the Problem-SpaceCode0
Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine LearningCode0
DeepXplore: Automated Whitebox Testing of Deep Learning SystemsCode0
A Novel Approach to Malicious Code Detection Using CNN-BiLSTM and Feature Fusion—0
A Non-Intrusive Machine Learning Solution for Malware Detection and Data Theft Classification in Smartphones—0
AiDroid: When Heterogeneous Information Network Marries Deep Neural Network for Real-time Android Malware Detection—0
An MDL-Based Classifier for Transactional Datasets with Application in Malware Detection—0
An investigation of the classifiers to detect android malicious apps—0
A Hierarchical Convolutional Neural Network for Malware Classification—0
Cross-Language Binary-Source Code Matching with Intermediate Representations—0
An investigation of a deep learning based malware detection system—0
Counteracting Concept Drift by Learning with Future Malware Predictions—0
A New Malware Detection System Using a High Performance-ELM method—0
A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection—0
Contextual Weisfeiler-Lehman Graph Kernel For Malware Detection—0
Context-aware, Adaptive and Scalable Android Malware Detection through Online Learning (extended version)—0
A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detection—0
Agent-based Vs Agent-less Sandbox for Dynamic Behavioral Analysis—0
Data Augmentation for Opcode Sequence Based Malware Detection—0
Feature Extraction for Novelty Detection in Network Traffic—0
Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples—0
Decentralised firewall for malware detection—0
Decision-forest voting scheme for classification of rare classes in network intrusion detection—0
Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies—0
Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection—0
A New Android Malware Detection Approach Using Bayesian Classification—0
Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection—0
Coda: An End-to-End Neural Program Decompiler—0
A Neural-based Program Decompiler—0
Clustering based opcode graph generation for malware variant detection—0
Clipping Free Attacks Against Neural Networks—0
An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content—0
Clipping free attacks against artificial neural networks—0
A Feature Set of Small Size for the PDF Malware Detection—0
Adaptive and Scalable Android Malware Detection through Online Learning—0
Classification under strategic adversary manipulation using pessimistic bilevel optimisation—0
Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing—0
Android Security using NLP Techniques: A Review—0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks—0
Android Malware Detection with Unbiased Confidence Guarantees—0
AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks—0
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach—0
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?—0
Android Malware Detection Using Parallel Machine Learning Classifiers—0
Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations—0
Android Malware Detection Using Machine Learning on Image Patterns—0
Adversary Resistant Deep Neural Networks with an Application to Malware Detection—0
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