Attack Scenario Prediction Methodology

Abstract

Intrusion detection system generates significant data about malicious activities run against network. Generated data by IDS are stored in IDS database. This data represent attacks scenarios history against network. Main goal of IDS system is to enhance network defense technologies. Other techniques are also used to enhance the defense of network such as Attack graph. Network attack graph are used for many goals such as attacker next attack step prediction. In this paper we propose a real time prediction methodology for predicting most possible attack steps and attack scenarios. Proposed methodology benefits from attacks history against network and from attack graph source data. it comes without considerable computation overload such as checking of attack plans library. It provides parallel prediction for parallel attack scenarios.