Advances In Malware Detection- An Overview
Heena (1, 2) ((1) Center of excellence in cybersecurity, Institute for, Development, Research in Banking Technology (IDRBT), Hyderabad, India, (2), School of Computer Science, Information Sciences (SCIS), University of, Hyderabad, Hyderabad, India)

TL;DR
This paper reviews various malware detection techniques, highlighting their strengths and limitations, and discusses datasets and tools to improve early detection of advanced and zero-day malware threats.
Contribution
It provides a comprehensive literature review of malware detection methods, comparing their effectiveness and discussing future research directions.
Findings
Behavior-based detection is more effective than static analysis for unknown malware.
Current methods still struggle to detect all zero-day malware.
Datasets and tools are crucial for advancing malware detection research.
Abstract
Malware has become a widely used means in cyber attacks in recent decades because of various new obfuscation techniques used by malwares. In order to protect the systems, data and information, detection of malware is needed as early as possible. There are various studies on malware detection techniques that have been done but there is no method which can detect the malware completely and make malware detection problematic. Static Malware analysis is very effective for known malwares but it does not work for zero day malware which leads to the need of dynamic malware detection and the behaviour based malware detection is comparatively good among all detection techniques like signature based, deep learning based, mobile/IOT and cloud based detection but still it is not able to detect all zero day malware which shows the malware detection is very challenging task and need more techniques…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Network Security and Intrusion Detection · Spam and Phishing Detection
