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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 151–200 of 431 papers

TitleStatusHype
Assessing Cyclostationary Malware Detection via Feature Selection and Classification—0
Analysing Safety Risks in LLMs Fine-Tuned with Pseudo-Malicious Cyber Security Data—0
Dos and Don'ts of Machine Learning in Computer Security—0
DRo: A data-scarce mechanism to revolutionize the performance of Deep Learning based Security Systems—0
Dual Explanations via Subgraph Matching for Malware Detection—0
Dynamic detection of mobile malware using smartphone data and machine learning—0
A Survey of Malware Detection Using Deep Learning—0
Dynamic Malware Classification of Windows PE Files using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion—0
Early Detection of In-Memory Malicious Activity based on Run-time Environmental Features—0
Echelon: Two-Tier Malware Detection for Raw Executables to Reduce False Alarms—0
Generative AI-Based Effective Malware Detection for Embedded Computing Systems—0
Effectiveness of Adversarial Examples and Defenses for Malware Classification—0
Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection—0
Efficient and Robust Classification for Sparse Attacks—0
Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning—0
A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes—0
A Review on The Use of Deep Learning in Android Malware Detection—0
Efficient Malware Analysis Using Metric Embeddings—0
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods—0
Design of secure and robust cognitive system for malware detection—0
Empirical Quantification of Spurious Correlations in Malware Detection—0
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems—0
Enhanced Attacks on Defensively Distilled Deep Neural Networks—0
A Review of Computer Vision Methods in Network Security—0
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples—0
Enhancing Malware Detection by Integrating Machine Learning with Cuckoo Sandbox—0
Enhancing Robustness of Neural Networks through Fourier Stabilization—0
Behavioral Malware Classification using Convolutional Recurrent Neural Networks—0
Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach—0
Android Malware Detection Using Autoencoder—0
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection—0
Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers—0
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems—0
Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions—0
Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges—0
Explainable Malware Detection through Integrated Graph Reduction and Learning Techniques—0
Explainable Malware Detection with Tailored Logic Explained Networks—0
Explaining Black-box Android Malware Detection—0
Explaining high-dimensional text classifiers—0
Exploring Adversarial Examples in Malware Detection—0
GLSearch: Maximum Common Subgraph Detection via Learning to Search—0
Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations—0
Feature Cross-Substitution in Adversarial Classification—0
Getting Passive Aggressive About False Positives: Patching Deployed Malware Detectors—0
FGAM:Fast Adversarial Malware Generation Method Based on Gradient Sign—0
Flexible Android Malware Detection Model based on Generative Adversarial Networks with Code Tensor—0
Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking—0
From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy—0
Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU) Learning in Cybersecurity—0
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