ABIDS system using Hidden Markov Model

Today Internet security has become a serious issue for anyone connected to the Internet. The internet is a great tool for many things, but unfortunately it can also pose a security risk for personal information and privacy. Hidden Markov Model (HMM) based applications are common in various areas, but the incorporation of HMM´s for anomaly detection is still in its infancy. There are many approaches for building the IDS; here we are chosen Hidden Markov Model approach for building Anomaly based Intrusion Detection System (ABIDS) as a network security tool. This model has two phases, in the first phase the model is trained and in the second phase the model is tested. In both phases we have used a standard masquerade dataset. This dataset contains 50 users and each user has 15000 records. The first 5000 records have been used to train the model and the remaining 10000 records have been used for evaluation (testing) of the model. This model works for even high dimensional data streams with high performance detection rate and robust to noise.