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Deep Learning Based Security Management of Information Systems: A Comparative Study

Cem B. Cebi 1, Fatma S. Bulut 1, Hazal Firat 1, Ozgur Koray Sahingoz 1, and Gozde Karatas 2
1. Computer Engineering, Istanbul Kultur University, Istanbul, Turkey
2. Mathematics and Computer Sciences, Istanbul Kultur University, Istanbul, Turkey

Abstract—In recent years, there is a growing trend of internetization, which is a relatively new word for our global economy that aims to connect each market sector (or even devices) by using the worldwide network architecture as the Internet. Although this connectivity enables excellent opportunities in the marketplace, it results in many security vulnerabilities for admins of the computer networks. Firewalls and Antivirus systems are preferred as the first line of defense mechanism; they are not sufficient to protect the systems from all types of attacks. Intrusion Detection Systems (IDSs), which can train themselves and improve their knowledge base, can be used as an extra line of the defense mechanism of the network. Due to its dynamic structure, IDSs are one of the most preferred solution models to protect the networks against attacks. Traditionally, standard machine learning methods are preferred for training the system. However, in recent years, there is a growing trend to transfer these standard machine learning based systems to the deep learning models. Therefore, in this paper, IDSs with four different deep learning models are proposed, and their performance is compared. The experimental results showed that proposed models result in very high and acceptable accuracy rates with KDD Cup 99 Dataset.
Index Terms—cyber security, intrusion detection systems, deep learning, BiRNN, BiLSTM, CNN-LSTM, GRU, KDDCup99

Cite: Cem B. Cebi, Fatma S. Bulut, Hazal Firat, Ozgur Koray Sahingoz, and Gozde Karatas, "Deep Learning Based Security Management of Information Systems: A Comparative Study," Journal of Advances in Information Technology, Vol. 11, No. 3, pp. 135-142, August 2020. doi: 10.12720/jait.11.3.135-142

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.