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 401–431 of 431 papers

TitleStatusHype
Generating Adversarial Malware Examples for Black-Box Attacks Based on GANCode0
Creating Valid Adversarial Examples of MalwareCode0
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved FeaturesCode0
On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial AttacksCode0
An Efficient Approach For Malware Detection Using PE Header SpecificationCode0
Convolutional Neural Network for Classification of Malware Assembly CodeCode0
Multitask Learning for Network Traffic ClassificationCode0
A learning model to detect maliciousness of portable executable using integrated feature setCode0
Black-Box Attacks against RNN based Malware Detection AlgorithmsCode0
Beyond the Hype: A Real-World Evaluation of the Impact and Cost of Machine Learning-Based Malware DetectionCode0
Detecting DGA domains with recurrent neural networks and side informationCode0
Deep learning at the shallow end: Malware classification for non-domain expertsCode0
Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity DetectionCode0
Adversarial Feature Selection against Evasion AttacksCode0
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep LearningCode0
Reliable Malware Analysis and Detection using Topology Data AnalysisCode0
How to 0wn NAS in Your Spare TimeCode0
How to 0wn the NAS in Your Spare TimeCode0
How to Train your Antivirus: RL-based Hardening through the Problem-SpaceCode0
DetectBERT: Towards Full App-Level Representation Learning to Detect Android MalwareCode0
Hyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN PerformanceCode0
Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable BytesCode0
Statistical Estimation of Malware Detection Metrics in the Absence of Ground TruthCode0
Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection TechniqueCode0
Imbalanced malware classification: an approach based on dynamic classifier selectionCode0
Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious CorrelationsCode0
Stealing and Evading Malware Classifiers and Antivirus at Low False Positive ConditionsCode0
Improving Malware Detection Accuracy by Extracting Icon InformationCode0
Sequential Embedding-based Attentive (SEA) classifier for malware classificationCode0
Orthrus: A Bimodal Learning Architecture for Malware ClassificationCode0
Accelerating Malware Classification: A Vision Transformer SolutionCode0
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