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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 201–250 of 431 papers

TitleStatusHype
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
Multifamily Malware Models—0
Malware Detection and Prevention using Artificial Intelligence Techniques—0
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective—0
When a RF Beats a CNN and GRU, Together -- A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic ClassificationCode0
On the impact of dataset size and class imbalance in evaluating machine-learning-based windows malware detection techniques—0
Generative Adversarial Networks and Image-Based Malware Classification—0
Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection—0
Support Vector Machines under Adversarial Label Contamination—0
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware DetectionCode0
BagFlip: A Certified Defense against Data PoisoningCode0
Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine LearningCode0
Fast & Furious: Modelling Malware Detection as Evolving Data StreamsCode0
A two-steps approach to improve the performance of Android malware detectors—0
SeqNet: An Efficient Neural Network for Automatic Malware Detection—0
SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment—0
Stealing and Evading Malware Classifiers and Antivirus at Low False Positive ConditionsCode0
A Natural Language Processing Approach for Instruction Set Architecture Identification—0
Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection—0
Deep Image: A precious image based deep learning method for online malware detection in IoT Environment—0
MERLIN -- Malware Evasion with Reinforcement LearnINg—0
Toward the Detection of Polyglot Files—0
A Comparison of Static, Dynamic, and Hybrid Analysis for Malware Detection—0
Adversarial Patterns: Building Robust Android Malware Classifiers—0
MaMaDroid2.0 -- The Holes of Control Flow GraphsCode0
Improving Radioactive Material Localization by Leveraging Cyber-Security Model Optimizations—0
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks—0
StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection—0
IoT Malware Detection Architecture using a Novel Channel Boosted and Squeezed CNN—0
On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial AttacksCode0
Efficient and Robust Classification for Sparse Attacks—0
RoboMal: Malware Detection for Robot Network Systems—0
Android Malware Detection using Feature Ranking of Permissions—0
Cross-Language Binary-Source Code Matching with Intermediate RepresentationsCode0
Graph Neural Network-based Android Malware Classification with Jumping Knowledge—0
Benchmark Static API Call Datasets for Malware Family Classification—0
ORSA: Outlier Robust Stacked Aggregation for Best- and Worst-Case Approximations of Ensemble Systems\—0
HAPSSA: Holistic Approach to PDF Malware Detection Using Signal and Statistical Analysis—0
OMD: Orthogonal Malware Detection Using Audio, Image, and Static Features—0
"How Does It Detect A Malicious App?" Explaining the Predictions of AI-based Android Malware Detector—0
On the Effectiveness of Interpretable Feedforward Neural Network—0
Task-Aware Meta Learning-based Siamese Neural Network for Classifying Obfuscated Malware—0
A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods—0
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware DetectionCode0
LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach—0
DRo: A data-scarce mechanism to revolutionize the performance of Deep Learning based Security Systems—0
ML-based IoT Malware Detection Under Adversarial Settings: A Systematic Evaluation—0
Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weights—0
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