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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–300 of 431 papers

TitleStatusHype
Parallel Instance Filtering for Malware Detection—0
PDF-Malware: An Overview on Threats, Detection and Evasion Attacks—0
PECAN: A Deterministic Certified Defense Against Backdoor Attacks—0
PhilaeX: Explaining the Failure and Success of AI Models in Malware Detection—0
Powershell malware detection method based on features combination—0
Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware—0
Practical Fast Gradient Sign Attack against Mammographic Image Classifier—0
Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines—0
R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections—0
Randomized Prediction Games for Adversarial Machine Learning—0
Recent Advances in Malware Detection: Graph Learning and Explainability—0
Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection—0
Review of Deep Learning-based Malware Detection for Android and Windows System—0
Revisiting Static Feature-Based Android Malware Detection—0
Packet Inspection Transformer: A Self-Supervised Journey to Unseen Malware Detection with Few Samples—0
RoboMal: Malware Detection for Robot Network Systems—0
Robust Android Malware Detection System against Adversarial Attacks using Q-Learning—0
Robust and Accurate Authorship Attribution via Program Normalization—0
RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization—0
SCORE: Syntactic Code Representations for Static Script Malware Detection—0
Security Evaluation of Support Vector Machines in Adversarial Environments—0
SEdroid: A Robust Android Malware Detector using Selective Ensemble Learning—0
Semantic Adversarial Deep Learning—0
SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using Natural Language Processing (SecureNLP)—0
Semi-supervised classification for dynamic Android malware detection—0
SeqNet: An Efficient Neural Network for Automatic Malware Detection—0
SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment—0
Leveraging Large Language Models to Detect npm Malicious Packages—0
Similarity-based Android Malware Detection Using Hamming Distance of Static Binary Features—0
SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines—0
Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!—0
Arms Race in Adversarial Malware Detection: A Survey—0
Static Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus—0
StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection—0
Strategic Planning for Network Data Analysis—0
Support Vector Machines under Adversarial Label Contamination—0
Survey of Malware Analysis through Control Flow Graph using Machine Learning—0
System Calls for Malware Detection and Classification: Methodologies and Applications—0
Task-Aware Meta Learning-based Siamese Neural Network for Classifying Obfuscated Malware—0
The Curious Case of Machine Learning In Malware Detection—0
The Efficacy of Transformer-based Adversarial Attacks in Security Domains—0
The Naked Sun: Malicious Cooperation Between Benign-Looking Processes—0
There is Limited Correlation between Coverage and Robustness for Deep Neural Networks—0
The State-of-the-Art in AI-Based Malware Detection Techniques: A Review—0
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey—0
Towards Accurate Labeling of Android Apps for Reliable Malware Detection—0
Towards an Automated Pipeline for Detecting and Classifying Malware through Machine Learning—0
Towards an in-depth detection of malware using distributed QCNN—0
Towards a Robust Classifier: An MDL-Based Method for Generating Adversarial Examples—0
Towards Deep Federated Defenses Against Malware in Cloud Ecosystems—0
Show:102550
← PrevPage 6 of 9Next →

No leaderboard results yet.