Towards data fusion-based big data analytics for intrusion detection

ABSTRACT

Intrusion detection is seen as the most promising way for computer security. It is used to protect computer networks against different types of attacks. The major problem in the literature is the classification of data into two main classes: normal and intrusion. To solve this problem, several approaches have been proposed but the problem of false alarms is still present. To provide a solution to this problem, we have proposed a new intrusion detection approach based on data fusion. The main objective of this work is to suggest an approach of data fusion-based Big Data analytics to detect intrusions; It is to build one dataset which combines various datasets and contains all the attack types. This research consists in merging the heterogeneous datasets and removing redundancy information using Big Data analytics tools: Hadoop/MapReduce and Neo4j. In the next step, machine learning algorithms are implemented for learning. The first algorithm, called SSDM (Semantically Similar Data Miner), uses fuzzy logic to generate association rules between the different item sets. The second algorithm, called K2, is a score-based greedy search algorithm for learning Bayesian networks from data. Experimentation results prove that – in both cases – data fusion contributes to having very good results.

KEYWORDS:

• Intrusion detection
• data fusion
• Big Data analytics
• machine learning

Google Scholar profile

https://scholar.google.com/citations?user=eDZP7zcAAAAJ&hl=fr

Scopus profile

https://www.scopus.com/authid/detail.uri?authorId=22034669800

Web of Science profile

https://www.webofscience.com/wos/author/record/2421263.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Farah Jemili

Farah Jemili had the Engineer degree in Computer Science in 2002, the master degree in 2004, and the Ph.D degree in 2010 from the National School of Computer Science (ENSI, Tunisia). Since 2007, she is an Assistant Professor at the Higher Institute of Computer Science and Telecom of Hammam Sousse (ISITCOM, Tunisia). She started research since 2002 at RIADI Laboratory (ENSI, Tunisia). Since 2010, she is a senior Researcher at MARS Laboratory (ISITCOM, Tunisia). Her research interests include Artificial Intelligence, Cyber Security, Big Data Analysis and Distributed Systems. She supervised almost 20 Master students, and 5 Ph.D students. She served as a reviewer for many international conferences and journals. She has over 30 publications in international journals and conferences and has presented many invited and contributed talks at international conferences.

She has been member of the Scientific Council of ISITCOM for 3 years (2011-2014), and head of the Department of Computer Science at ISITCOM for 3 years (2017-2020). Since February 2019, she is a member of the PMO (Project Management Office) of the University of Sousse and she has participated to CBHE Erasmus + projects, Horizon 2020 programmes and Horizon Europe programmes.