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Comparative Study between Big Data Analysis Techniques in Intrusion Detection

by Mounir Hafsa 1,* and Farah Jemili 2,*
1
Higher Institute of Computer Science and Telecom (ISITCOM), University of Sousse, Hammam Sousse 4011, Tunisia
2
MARS Research Lab LR17ES05, Higher Institute of Computer Science and Telecom (ISITCOM), University of Sousse, Hammam Sousse 4011, Tunisia
*
Authors to whom correspondence should be addressed.
Big Data Cogn. Comput. 2019, 3(1), 1; https://doi.org/10.3390/bdcc3010001
Received: 12 November 2018 / Revised: 13 December 2018 / Accepted: 15 December 2018 / Published: 20 December 2018
Cybersecurity ventures expect that cyber-attack damage costs will rise to $11.5 billion in 2019 and that a business will fall victim to a cyber-attack every 14 seconds. Notice here that the time frame for such an event is seconds. With petabytes of data generated each day, this is a challenging task for traditional intrusion detection systems (IDSs). Protecting sensitive information is a major concern for both businesses and governments. Therefore, the need for a real-time, large-scale and effective IDS is a must. In this work, we present a cloud-based, fault tolerant, scalable and distributed IDS that uses Apache Spark Structured Streaming and its Machine Learning library (MLlib) to detect intrusions in real-time. To demonstrate the efficacy and effectivity of this system, we implement the proposed system within Microsoft Azure Cloud, as it provides both processing power and storage capabilities. A decision tree algorithm is used to predict the nature of incoming data. For this task, the use of the MAWILab dataset as a data source will give better insights about the system capabilities against cyber-attacks. The experimental results showed a 99.95% accuracy and more than 55,175 events per second were processed by the proposed system on a small cluster. View Full-Text
Keywords: intrusion detection system; machine learning; Apache Spark; Structured Streaming; Big Data; Decision Trees; Microsoft Azure Cloud intrusion detection system; machine learning; Apache Spark; Structured Streaming; Big Data; Decision Trees; Microsoft Azure Cloud
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Hafsa, M.; Jemili, F. Comparative Study between Big Data Analysis Techniques in Intrusion Detection. Big Data Cogn. Comput. 2019, 3, 1.

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