LAUSR

Cyberthreats are continually evolving and growing in numbers and extreme complexities with the increasing connectivity of the Internet of Things (IoT). Existing cyber-defense tools seem not to deter the number of successful cyberattacks reported worldwide. If defense tools are not seldom, why does the cyber-chase trend favor bad actors? Although cyber-defense tools monitor and try to diffuse intrusion attempts, research shows the required agility speed against evolving threats is way too slow. One of the reasons is that many intrusion detection tools focus on anomaly alerts’ accuracy, assuming that preobserved attacks and subsequent security patches are adequate. Well, that is not the case. In fact, there is a need for techniques that go beyond intrusion accuracy against specific vulnerabilities to the prediction of cyber-defense performance for improved proactivity. This article proposes a combination of cyberattack projection and cyber-defense agility estimation to dynamically but reliably augur intrusion detection performance. Since cyber-security is buffeted with many unknown parameters and rapidly changing trends, we apply a machine-learning (ML)-based hidden Markov model (HMM) to predict intrusion detection agility. HMM is best known for robust prediction of temporal relationships mid noise and training brevity corroborating our high prediction accuracy on three major open-source network intrusion detection systems, namely, Zeek, OSSEC, and Suricata. Specifically, we present a novel approach for combined projection, prediction, and cyber-visualization to enable precise agility analysis of cyber defense. We also evaluate the performance of the developed approach using numerical results.