  Anomaly-based intrusion detection (AID) techniques are useful for detecting
novel intrusions into computing resources. One of the most successful AID
detectors proposed to date is stide, which is based on analysis of system call
sequences. In this paper, we present a detailed formal framework to analyze,
understand and improve the performance of stide and similar AID techniques.
Several important properties of stide-like detectors are established through
formal proofs, and validated by carefully conducted experiments using test
datasets. Finally, the framework is utilized to design two applications to
improve the cost and performance of stide-like detectors which are based on
sequence analysis. The first application reduces the cost of developing AID
detectors by identifying the critical sections in the training dataset, and the
second application identifies the intrusion context in the intrusive dataset,
that helps to fine-tune the detectors. Such fine-tuning in turn helps to
improve detection rate and reduce false alarm rate, thereby increasing the
effectiveness and efficiency of the intrusion detectors.
