Internet of Things (IoTs) Security: Intrusion Detection using Deep Learning

Authors

DOI:

https://doi.org/10.13052/jwe1540-9589.2062

Keywords:

convolutional neural networks, Deep Learning, Imbalanced Datasets, Internet of Things, Web Security

Abstract

With the development of sensor and communication technologies, the use of connected devices in industrial applications has been common for a long time. Reduction of costs during this period and the definition of Internet of Things (IoTs) concept have expanded the application area of small connected devices to the level of end-users. This paved the way for IoT technology to provide a wide variety of application alternative and become a part of daily life. Therefore, a poorly protected IoT network is not sustainable and has a negative effect on not only devices but also the users of the system. In this case, protection mechanisms which use conventional intrusion detection approaches become inadequate. As the intruders’ level of expertise increases, identification and prevention of new kinds of attacks are becoming more challenging. Thus, intelligent algorithms, which are capable of learning from the natural flow of data, are necessary to overcome possible security breaches. Many studies suggesting models on individual attack types have been successful up to a point in recent literature. However, it is seen that most of the studies aiming to detect multiple attack types cannot successfully detect all of these attacks with a single model. In this study, it is aimed to suggest an all-in-one intrusion detection mechanism for detecting multiple intrusive behaviors and given network attacks. For this aim, a custom deep neural network is designed and implemented to classify a number of different types of network attacks in IoT systems with high accuracy and F1-score. As a test-bed for comparable results, one of the up-to-date dataset (CICIDS2017), which is highly imbalanced, is used and the reached results are compared with the recent literature. While the initial propose was successful for most of the classes in the dataset, it was noted that achievement was low in classes with a small number of samples. To overcome imbalanced data problem, we proposed a number of augmentation techniques and compared all the results. Experimental results showed that the proposed methods yield highest efficiency among observed literature.

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Author Biographies

Ozgur Koray Sahingoz, Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Biruni University, Istanbul, Turkey

Ozgur Koray Sahingoz received the B.Sc. degree from the Computer Engineering Department, Bogazici University, in 1993, and the M.S. and Ph.D. degrees from the Computer Engineering Department, Istanbul Technical University, in 1998 and 2006, respectively. He is currently working as Professor with the Computer Engineering Department, Biruni University/Istanbul. He is the author of more than 100 articles. He has been working in two research projects. He graduated more than 13 M.Sc. students and supervised around six Ph.D. students. He has reviewed more than 80 national projects especially related to TUBITAK, KOSGEB-Ministry of Industry and Technology, Turkey. He is also a regular Reviewer for more than 40 Science Citation Index (/Expanded) international journals. His research interests include artificial intelligence, machine/deep learning, data science, software engineering, and UAV networking. Dr. Sahingoz has also been very active in scientific conferences, organized and/or works as program committee members more than 100 conferences/workshops on different research areas, especially on artificial intelligence and information sciences. He has developed and taught around 20 different academic courses.

Ugur Cekmez, Chooch Intelligence Technologies Co., California, USA

Ugur Cekmez was born in Istanbul, Turkey, in 1989. He received the B.Sc. degree from the Computer Science Department, Istanbul Bilgi University, in 2012, and the M.S. degree from the Computer Engineering Department, Turkish Air Force Academy, in 2014. He is currently pursuing the Ph.D. degree with the Computer Engineering Department, Marmara University. He is an experienced Research Scientist with a demonstrated history of working in the information technology industry. Skilled in AI, Data Intelligence, Container Technology, Python and node.js. His research interests include AI and Data Science, Evolutionary Algorithms, Cloud Technologies, E-commerce and Finance Technologies. He has been co-founding projects and start-ups in digital concepts. He previously worked as a Research Assistant at Yildiz Technical University, as a Senior Researcher at TUBITAK, as an R&D engineer at SIEMENS, Turkey and as a Senior Research Engineer at Turkish Television and Radio Corporation (TRT). He is currently working as a Senior Researcher at Chooch Intelligence Technologies Co, the USA.

Ali Buldu, Department of Computer Engineering, Faculty of Technology, Marmara University, Istanbul, Turkey

Ali Buldu was born in 1971 in Kayseri, Turkey. He received the B.Sc. degree from Marmara University Technical Education Faculty Electronic and Computer Department. He received M.Sc. and Ph.D. degrees from Marmara University Institute for Graduate Studies in Pure and Applied Sciences in 1996 and 2003, respectively. Dr. Buldu has been Professor with the Computer Engineering Department since October 2019. His research interests focus on Computer Hardware, Circuit Design with Embedded Systems Computer Aided Education, Information Technologies and Computer Programming Languages.

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Published

2021-10-13

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Articles