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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 51–100 of 431 papers

TitleStatusHype
Feature Extraction for Novelty Detection in Network Traffic—0
A New Android Malware Detection Approach Using Bayesian Classification—0
A Neural-based Program Decompiler—0
Adaptive and Scalable Android Malware Detection through Online Learning—0
An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content—0
A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection—0
A Feature Set of Small Size for the PDF Malware Detection—0
An investigation of a deep learning based malware detection system—0
A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms—0
An MDL-Based Classifier for Transactional Datasets with Application in Malware Detection—0
A Non-Intrusive Machine Learning Solution for Malware Detection and Data Theft Classification in Smartphones—0
A Novel Approach to Malicious Code Detection Using CNN-BiLSTM and Feature Fusion—0
Machine Learning for Detecting Malware in PE Files—0
Android Security using NLP Techniques: A Review—0
Android Malware Detection with Unbiased Confidence Guarantees—0
AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks—0
Android Malware Detection Using Parallel Machine Learning Classifiers—0
Adversary Resistant Deep Neural Networks with an Application to Malware Detection—0
Adapting Novelty towards Generating Antigens for Antivirus systems—0
Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection—0
Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection—0
Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies—0
Adversarial Samples on Android Malware Detection Systems for IoT Systems—0
Android Malware Detection using Deep Learning on API Method Sequences—0
Clipping free attacks against artificial neural networks—0
ActDroid: An active learning framework for Android malware detection—0
Android Malware Detection Based on RGB Images and Multi-feature Fusion—0
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection—0
Clipping Free Attacks Against Neural Networks—0
A Natural Language Processing Approach to Malware Classification—0
ATWM: Defense against adversarial malware based on adversarial training—0
A Transformer-Based Framework for Payload Malware Detection and Classification—0
A Natural Language Processing Approach for Instruction Set Architecture Identification—0
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective—0
A two-steps approach to improve the performance of Android malware detectors—0
A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes—0
A survey on practical adversarial examples for malware classifiers—0
A Visualized Malware Detection Framework with CNN and Conditional GAN—0
Behavioral Malware Classification using Convolutional Recurrent Neural Networks—0
Android Malware Detection Using Autoencoder—0
Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers—0
BERTDetect: A Neural Topic Modelling Approach for Android Malware Detection—0
Traffic Refinery: Cost-Aware Data Representation for Machine Learning on Network Traffic—0
Android Malware Detection using Feature Ranking of Permissions—0
Android Malware Detection Using Machine Learning on Image Patterns—0
Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations—0
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?—0
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach—0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks—0
Analyzing Machine Learning Approaches for Online Malware Detection in Cloud—0
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