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A comparison of adversarial malware generators
Machine learning has proven to be a valuable tool for automated malware detection, but machine learning systems have also been shown to be subject to...
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Stacking Ensemble-Based Approach for Malware Detection
The rapid growth of Internet connectivity has resulted in a significant increase in digital attack events, many of which have devastating and severe...
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Efficient malware detection through inter-component communication analysis
With the development of science and technology, the number of smartphones has increased dramatically. This also exposes Android-based smartphones to...
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An Overview of Techniques for Obfuscated Android Malware Detection
Obfuscation is a method to hide coding strategies for security and privacy. Despite its positive use, malware experts have also used this technique...
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Classification and online clustering of zero-day malware
A large amount of new malware is constantly being generated, which must not only be distinguished from benign samples, but also classified into...
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Creating valid adversarial examples of malware
Because of its world-class results, machine learning (ML) is becoming increasingly popular as a go-to solution for many tasks. As a result, antivirus...
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Framework for understanding intention-unbreakable malware
The anti-analysis technology of malware has always been the focus in the cyberspace security field. As malware analysis techniques evolve, malware...
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Use of cryptography in malware obfuscation
Malware authors often use cryptographic tools such as XOR encryption and block ciphers like AES to obfuscate part of the malware to evade detection....
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Zero-Day Malware Classification and Detection Using Machine Learning
A zero-day vulnerability is a weakness of the computer software and hardware that has yet to be discovered by people who might be interested in...
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Design and implementation of a sandbox for facilitating and automating IoT malware analysis with techniques to elicit malicious behavior: case studies of functionalities for dissecting IoT malware
As malware poses a significant threat to IoT devices, the technology to combat IoT malware, like sandbox, has not received enough attention. The...
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A comprehensive review on permissions-based Android malware detection
The first Android-ready “G1” phone debuted in late October 2008. Since then, the growth of Android malware has been explosive, analogous to the rise...
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MADESANT: malware detection and severity analysis in industrial environments
Malware remains a persistent threat to industrial operations, causing disruptions and financial losses. Traditional malware detection approaches...
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Malware detection for container runtime based on virtual machine introspection
The isolation technique of containers introduces uncertain security risks to malware detection in the current container environment. In this paper,...
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Unmasking the common traits: an ensemble approach for effective malware detection
Malware detection has become a critical aspect of ensuring the security and integrity of computer systems. With the ever-evolving landscape of...
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A natural language processing approach to Malware classification
Many different machine learning and deep learning techniques have been successfully employed for malware detection and classification. Examples of...
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iOS mobile malware analysis: a state-of-the-art
In earlier years, most malware attacks were against Android smartphones. Unfortunately, for the past few years, the trend has shifted towards attacks...
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Android malware detection using time-aware machine learning approach
In today’s rapidly evolving digital landscape, the surge in smartphone usage is paralleled by an increasing wave of cyberthreats, highlighting the...
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IPAnalyzer: A novel Android malware detection system using ranked Intents and Permissions
Android malware has been growing in scale and complexity, spurred by the unabated uptake of smartphones worldwide. Millions of malicious Android...
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A comparison of graph neural networks for malware classification
Managing the threat posed by malware requires accurate detection and classification techniques. Traditional detection strategies, such as signature...
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Enhancing malware detection performance: leveraging K-Nearest Neighbors with Firefly Optimization Algorithm
Malware detection plays a crucial role in ensuring robust cybersecurity amidst the ever-evolving cyber threats. This research paper delves into the...
