Assessing risk of attacks in large networked system with Context Sensitive Probabilistic Modelling

Rahul Madaan, Kanishk Rana, Arun Balaji Buduru

Abstract: The recent trends of security breaches show that monetary or computational constraints no longer limit attackers, and the intent of the attacks are not confined to personal gains anymore. It has become a challenge to detect cyber-attacks in a large networked system due to the complex and distributed nature. In this paper, cyber-attacks will be identified by introducing the notion of risk of an attack. The risk is defined as the possibility that the current state of the system can lead to a breach if preventive measures are not taken. The model used to achieve this involves using Long Short-Term Memory (LSTM) to handle context-sensitivity of the dataset and a reward-based Markov Decision Process (MDP) to identify the risk associated with the current state. For this work, we demonstrate the effectiveness of using MDP and LSTM to detect attacks using CSE-CIC-IDS2018 dataset.