Publication Type:

Conference Paper


2015 3rd International Conference on Signal Processing, Communication and Networking (ICSCN) (2015)


accuracy, Adaboost algorihm, Computer crime, hackers, higher dimension dataset, IDS, intruders, intrusion detection approach, Intrusion Detection System, Intrusion Detection Systems, J48 tree algorithm, K-nearest neighbors, kNN, learning (artificial intelligence), lower dimension dataset, Machine learning algorithms, mathematical model, naive Bayes probabilistic classifier, nearest neighbors generalized exemplars algorithm, network attacks, network packet header, PCA, Principal component analysis, random forest tree classification algorithm, Signal processing algorithms, Support vector machines, SVM, System resources, vegetation, voting features interval classification algorithm


<p>This paper induces the prominence of variegated machine learning techniques adapted so far for the identifying different network attacks and suggests a preferable Intrusion Detection System (IDS) with the available system resources while optimizing the speed and accuracy. With booming number of intruders and hackers in todays vast and sophisticated computerized world, it is unceasingly challenging to identify unknown attacks in promising time with no false positive and no false negative. Principal Component Analysis (PCA) curtails the amount of data to be compared by reducing their dimensions prior to classification that results in reduction of detection time. In this paper, PCA is adopted to reduce higher dimension dataset to lower dimension dataset. It is accomplished by converting network packet header fields into a vector then PCA applied over high dimensional dataset to reduce the dimension. The reduced dimension dataset is tested with Support Vector Machines (SVM), K-Nearest Neighbors (KNN), J48 Tree algorithm, Random Forest Tree classification algorithm, Adaboost algorihm, Nearest Neighbors generalized Exemplars algorithm, Navebayes probabilistic classifier and Voting Features Interval classification algorithm. Obtained results demonstrates detection accuracy, computational efficiency with minimal false alarms, less system resources utilization. Experimental results are compared with respect to detection rate and detection time and found that TREE classification algorithms achieved superior results over other algorithms. The whole experiment is conducted by using KDD99 data set.</p>

Cite this Research Publication

K. J. Chabathula, Jaidhar, C. D., and Ajay Kumara, “Comparative study of Principal Component Analysis based Intrusion Detection approach using machine learning algorithms”, in 2015 3rd International Conference on Signal Processing, Communication and Networking (ICSCN), 2015.