An Intrusion detection System is software that is used for the malicious activities performed in the network whether in wired or in wireless. Although there are various techniques implemented for the detection of intrusions but still various techniques are yet to be implemented for the accurate detection of intrusion such that the false positive rate can be minimized. Hidden Markov model is a technique which consists of number of states having initial transition of data and at each transition from one state to another a probability is calculated, this technique can be considered for the detection of intrusions. Here in this paper a complete survey of all the technique implemented for the intrusion detection and their various advantages and disadvantages are discussed such that a new technique can be implemented in future.
Intrusion detection system is a technique of identifying unwanted packets that creates harm in the network; hence various IDS are implemented for the security of network traffic flow. Here in this paper an efficient technique of identifying intrusions is implemented using hidden markov model and then classification of these intrusions is done. The methodology sis applied on KDDCup 99 dataset where the dataset is first clustered using K-means algorithms and then a number of attributes is selected which are used for the detection of intrusion is passed to the HMM, after calculating probability from each of the states, these probabilities are fused to get the resultant final probability and also overall probability is calculated from dataset on the basis of which intrusions are classified as low, medium or high.
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