20 May 2009 Using qualia and novel representations in malware detection
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Abstract
Detecting network intruders and malicious software is a significant problem for network administrators and security experts. New threats are emerging at an increasing rate, and current signature and statistics-based techniques are failing to keep pace. Intelligent systems that can adapt to new threats are needed to mitigate these new strains of malware as they are released. This research develops a system that uses contextual relationships and information across different layers of abstraction to detect malware based on its qualia, or essence. By looking for the underlying concepts that make a piece of software malicious, this system avoids the pitfalls of static solutions that focus on predefined signatures or anomaly thresholds. If successful, this type of qualia-based system would provide a framework for developing intelligent classification and decision-making systems for any number of application areas.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bobby Birrer, Richard A. Raines, Rusty O. Baldwin, Mark E. Oxley, Steven K. Rogers, "Using qualia and novel representations in malware detection", Proc. SPIE 7352, Intelligent Sensing, Situation Management, Impact Assessment, and Cyber-Sensing, 73520W (20 May 2009); doi: 10.1117/12.821082; https://doi.org/10.1117/12.821082
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KEYWORDS
Data modeling

Mathematical modeling

Intelligence systems

Computing systems

Systems modeling

Classification systems

Multilayers

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