Real Time Intrusion Detection System using Machine Learning
Ajit M. Kalekar, Niranjan Kshatriya, Sneha Chakranarayan, Snehal Wadekar · 2014
Abstract--Today, world has come closer due to rapid increase internet. As technology has been developed many threats are emerged for the data security which is not at all good for sensi-tive data transactions, but as we know that the network security also posses equal importance in the computer infrastructure. Because of the intruders the security of the network has become serious problem. Thus to overcome this we are proposing this paper which is based on machine learning algorithm for intru-sion detection using Naïve Bayesian Classifier, which is based on probabilistic model. This algorithm performs balance detections and keeps false positive rate at acceptable level for different types of real time networking attacks. In this, the system is trained by arranging the data attributes in a characterised for-mat which eliminates the redundancy resulting in the reduction of complexity.