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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 226–250 of 431 papers

TitleStatusHype
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 Representations—0
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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