Journal Article

Intrusion detection model using machine learning algorithm on Big Data environment

Ba-Alwi, Fadl Mutaher, Al-Hashida, Amal Y., Othman, Suad Mohammed, Alsohybe, Nabeel T.

2018

Overview

Recently, the huge amounts of data and its incremental increase have changed the importance of information security and data analysis systems for Big Data. Intrusion detection system (IDS) is a system that monitors and analyzes data to detect any intrusion in the system or network. High volume, variety and high speed of data generated in the network have made the data analysis process to detect attacks by traditional techniques very difficult. Big Data techniques are used in IDS to deal with Big Data for accurate and efficient data analysis process. This paper introduced Spark-Chi-SVM model for intrusion detection. In this model, we have used ChiSqSelector for feature selection, and built an intrusion detection model by using support vector machine (SVM) classifier on Apache Spark Big Data platform. We used KDD99 to train and test the model. In the experiment, we introduced a comparison between Chi-SVM classifier and Chi-Logistic Regression classifier. The results of the experiment showed that Spark-Chi-SVM model has high performance, reduces the training time and is efficient for Big Data.

• Publisher: Springer International Publishing,Springer Nature B.V,SpringerOpen
• Subject: Apache Spark / Big Data / ChiSqSelector / Classifiers / Communications Engineering / Computational Science and Engineering / Computer Science / Cybersecurity / Data analysis / Data management / Data Mining and Knowledge Discovery / Database Management / Information Storage and Retrieval / Intrusion / Intrusion detection / Intrusion detection systems / Machine learning / Mathematical Applications in Computer Science / Model testing / Networks / Regression analysis / Support vector machine (SVM) / Support vector machines