Improving Detection Rate Using Misuse Detection and Machine Learning

Network security is the provision made in an underlying computer network or rules made by the administrator to protect the network and its resources from unauthorized access. Network Security is becoming a crucial issue for all the firms and companies and with the increase in knowledge of intruders and hackers they have made many prosperous attempts to bring down web services and high-profile company networks. Misuse detection detects intrusions by matching the network traffic with a database of stored signatures and anomaly detection looks for behavior deviating from normal or common behavior for detecting intrusions. The primary objective of this paper is to combine both these techniques. The KDD dataset is used for this purpose. Finally, the data is processed by classification algorithms to obtain the results. The results show a high percentage of correct classification and accuracy. Experimental evaluation shows that the combined approach of Machine learning and misuse detection gives better performance.