SOTAVerified

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 251–300 of 431 papers

TitleStatusHype
Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints—0
PDF-Malware: An Overview on Threats, Detection and Evasion Attacks—0
Decision-forest voting scheme for classification of rare classes in network intrusion detection—0
Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretability—0
Dynamic detection of mobile malware using smartphone data and machine learning—0
Android Security using NLP Techniques: A Review—0
Data Augmentation for Opcode Sequence Based Malware Detection—0
Artificial Neural Network for Cybersecurity: A Comprehensive Review—0
Evading Malware Classifiers via Monte Carlo Mutant Feature DiscoveryCode0
Towards an Automated Pipeline for Detecting and Classifying Malware through Machine Learning—0
Enhancing Robustness of Neural Networks through Fourier Stabilization—0
MalPhase: Fine-Grained Malware Detection Using Network Flow Data—0
Analyzing Machine Learning Approaches for Online Malware Detection in Cloud—0
A Novel Malware Detection Mechanism based on Features Extracted from Converted Malware Binary Images—0
Early Detection of In-Memory Malicious Activity based on Run-time Environmental Features—0
Word Embedding Techniques for Malware Evolution Detection—0
NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification—0
Malware Classification with GMM-HMM Models—0
Malware Classification Using Long Short-Term Memory Models—0
Detection of Malicious Android Applications: Classical Machine Learning vs. Deep Neural Network Integrated with Clustering—0
Virus-MNIST: A Benchmark Malware Dataset—0
Identification of Significant Permissions for Efficient Android Malware Detection—0
Collective Intelligence: Decentralized Learning for Android Malware Detection in IoT with Blockchain—0
A Non-Intrusive Machine Learning Solution for Malware Detection and Data Theft Classification in Smartphones—0
Robust Android Malware Detection System against Adversarial Attacks using Q-Learning—0
A novel DL approach to PE malware detection: exploring Glove vectorization, MCC_RCNN and feature fusion—0
Towards interpreting ML-based automated malware detection models: a survey—0
Towards Interpretable Ensemble Learning for Image-based Malware Detection—0
Echelon: Two-Tier Malware Detection for Raw Executables to Reduce False Alarms—0
Powershell malware detection method based on features combination—0
Learning to Search for Fast Maximum Common Subgraph Detection—0
A Novel Resampling Technique for Imbalanced Dataset Optimization—0
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS—0
Beyond the Hype: A Real-World Evaluation of the Impact and Cost of Machine Learning-Based Malware DetectionCode0
Malware Detection using Artificial Bee Colony Algorithm—0
Towards Obfuscated Malware Detection for Low Powered IoT Devices—0
A survey on practical adversarial examples for malware classifiers—0
Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers—0
Traffic Refinery: Cost-Aware Data Representation for Machine Learning on Network Traffic—0
Getting Passive Aggressive About False Positives: Patching Deployed Malware Detectors—0
Dos and Don'ts of Machine Learning in Computer Security—0
Lightweight IoT Malware Detection Solution Using CNN Classification—0
Orthrus: A Bimodal Learning Architecture for Malware ClassificationCode0
Towards Accurate Labeling of Android Apps for Reliable Malware Detection—0
Robust and Accurate Authorship Attribution via Program Normalization—0
Maat: Automatically Analyzing VirusTotal for Accurate Labeling and Effective Malware Detection—0
Feature Extraction for Novelty Detection in Network Traffic—0
An Efficient Approach For Malware Detection Using PE Header SpecificationCode0
Adversarial Feature Selection against Evasion AttacksCode0
Arms Race in Adversarial Malware Detection: A Survey—0
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