Attack Detection Over Network Based on C45 and RF Algorithms
In this paper, Intrusion detection is to detect attacks (Intrusions) against a computer system. In the highly networked modern world, conventional techniques of network security such as cryptography, user authentication and intrusion prevention techniques like firewalls are not sufficient to detect new attacks. In this paper, the authors perform experiments on the kddcup99 data set. They perform dimensionality reduction of the data set using PCA (Principal Component Analysis) and clear distinction between normal and anomalous data is observed by using supervised data mining techniques. Primarily experiments with kddcup99 network data show that the supervised techniques such as Na?ve Bayesian, C4.5 can effectively detect anomalous attacks and achieve a low false positive rate.