Security Metric Methods for Network Multistep Attacks Using AMC and Big Data Correlation Analysis

Abstract

Network security metrics allow quantitatively evaluating the overall resilience of networked systems against attacks. From this aim, security metrics are of great importance to the security-related decision-making process of enterprises. In this paper, we employ absorbing Markov chain (AMC) to estimate the network security combining with the technique of big data correlation analysis. Specifically, we construct the model of AMC using a large amount of alert data to describe the scenario of multistep attacks in the real world. In addition, we implement big data correlation analysis to generate the transition probability matrix from alert stream, which defines the probabilities of transferring from one attack action to another according to a given scenario before reaching one of some attack targets. Based on the probability reasoning, two metric algorithms are designed to estimate the attack scenario as well as the attackers, namely, the expected number of visits (ENV) and the expected success probability (ESP). The superiority is that the proposed model and algorithms assist the administrator in building new scenarios, prioritizing alerts, and ranking them.