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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 201–225 of 431 papers

TitleStatusHype
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
Android Malware Detection using Feature Ranking of Permissions—0
RoboMal: Malware Detection for Robot Network Systems—0
Graph Neural Network-based Android Malware Classification with Jumping Knowledge—0
Cross-Language Binary-Source Code Matching with Intermediate Representations—0
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-ArtCode1
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
Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?Code1
DRo: A data-scarce mechanism to revolutionize the performance of Deep Learning based Security Systems—0
DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of BytecodeCode1
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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