Master of Engineering Science
Electrical and Computer Engineering
Dr. Jagath Samarabandu
Intrusion detection is only a starting step in securing IT infrastructure. Prediction of intrusions is the next step to provide an active defense against incoming attacks.
Most of the existing intrusion prediction methods mainly focus on prediction of either intrusion type or intrusion category. Also, most of them are built based on domain knowledge and specific scenario knowledge. This thesis proposes an alert prediction framework which provides more detailed information than just the intrusion type or category to initiate possible defensive measures. The proposed algorithm is based on hidden Markov model and it does not depend on specific domain knowledge. Instead, it depends on a training process. Hence the proposed algorithm is adaptable to different conditions. Also, it is based on prediction of the next alert cluster, which contains source IP address, destination IP range, alert type and alert category. Hence, prediction of next alert cluster provides more information about future strategies of the attacker.
Experiments were conducted using a public data set generated over 2500 alert predictions. Proposed alert prediction framework achieved accuracy of 81% and 77% for single step and five step predictions respectively for prediction of the next alert cluster. It also achieved an accuracy of prediction of 95% and 92% for single step and five step predictions respectively for prediction of the next alert category. The proposed methods achieved 5% prediction accuracy improvement for alert category over variable length Markov based alert prediction method, while providing more information for a possible defense.
Miriya Thanthrige, Udaya Sampath Karunathilaka Perera, "Hidden Markov Model Based Intrusion Alert Prediction" (2016). Electronic Thesis and Dissertation Repository. 4044.