  With the growing amount of cyber threats, the need for development of
high-assurance cyber systems is becoming increasingly important. The objective
of this paper is to address the challenges of modeling and detecting
sophisticated network attacks, such as multiple interleaved attacks. We present
the interleaving concept and investigate how interleaving multiple attacks can
deceive intrusion detection systems. Using one of the important statistical
machine learning (ML) techniques, Hidden Markov Models (HMM), we develop two
architectures that take into account the stealth nature of the interleaving
attacks, and that can detect and track the progress of these attacks. These
architectures deploy a database of HMM templates of known attacks and exhibit
varying performance and complexity. For performance evaluation, in the presence
of multiple multi-stage attack scenarios, various metrics are proposed which
include (1) attack risk probability, (2) detection error rate, and (3) the
number of correctly detected stages. Extensive simulation experiments are used
to demonstrate the efficacy of the proposed architectures.
