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 301–350 of 431 papers

TitleStatusHype
A Review of Computer Vision Methods in Network Security—0
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?—0
How to 0wn the NAS in Your Spare TimeCode0
pAElla: Edge-AI based Real-Time Malware Detection in Data CentersCode0
AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks—0
How to 0wn NAS in Your Spare TimeCode0
Analyzing CNN Based Behavioural Malware Detection Techniques on Cloud IaaS—0
MDEA: Malware Detection with Evolutionary Adversarial Learning—0
GLSearch: Maximum Common Subgraph Detection via Learning to Search—0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks—0
Interpreting Machine Learning Malware Detectors Which Leverage N-gram AnalysisCode0
Practical Fast Gradient Sign Attack against Mammographic Image Classifier—0
Towards Deep Federated Defenses Against Malware in Cloud Ecosystems—0
Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable BytesCode0
Towards a Robust Classifier: An MDL-Based Method for Generating Adversarial Examples—0
Coda: An End-to-End Neural Program Decompiler—0
DL-Droid: Deep learning based android malware detection using real devices—0
Classification with Costly Features in Hierarchical Deep SetsCode0
There is Limited Correlation between Coverage and Robustness for Deep Neural Networks—0
The Naked Sun: Malicious Cooperation Between Benign-Looking Processes—0
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey—0
Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection TechniqueCode0
Heterogeneous Graph Matching Networks—0
Would a File by Any Other Name Seem as Malicious?—0
An MDL-Based Classifier for Transactional Datasets with Application in Malware Detection—0
A Hierarchical Convolutional Neural Network for Malware Classification—0
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection—0
I-MAD: Interpretable Malware Detector Using Galaxy Transformer—0
Effectiveness of Adversarial Examples and Defenses for Malware Classification—0
SEdroid: A Robust Android Malware Detector using Selective Ensemble Learning—0
On Defending Against Label Flipping Attacks on Malware Detection Systems—0
Similarity-based Android Malware Detection Using Hamming Distance of Static Binary Features—0
New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning—0
Dynamic Malware Analysis with Feature Engineering and Feature LearningCode0
Intelligent Systems Design for Malware Classification Under Adversarial Conditions—0
A Neural-based Program Decompiler—0
A New Malware Detection System Using a High Performance-ELM method—0
Multitask Learning for Network Traffic ClassificationCode0
Evaluating Explanation Methods for Deep Learning in SecurityCode0
Transferable Cost-Aware Security Policy Implementation for Malware Detection Using Deep Reinforcement Learning—0
The Curious Case of Machine Learning In Malware Detection—0
Automatic Malware Description via Attribute Tagging and Similarity EmbeddingCode0
Distinguishability of Adversarial Examples—0
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach—0
Malware Evasion Attack and Defense—0
Malware Detection using Machine Learning and Deep Learning—0
Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions—0
ALOHA: Auxiliary Loss Optimization for Hypothesis AugmentationCode0
Agent-based Vs Agent-less Sandbox for Dynamic Behavioral Analysis—0
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems—0
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