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.