A sequential scheme for detecting cyber attacks in IoT environment

Yan Naung Soe, Yaokai Feng, Paulus Insap Santosa, Rudy Hartanto, Kouichi Sakurai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the rapid spread of the IoT (Internet of Things) devices, our daily life has been becoming more convenient and efficient. However, the attacker is more easily targeting the IoT devices to make them become attack destinations or bots for attacking other victims. This is because most of the IoT devices have not enough resources, memory and computation skill, to be equipped with an efficient security system. The mainstream of the actual IDSs (Intrusion Detection Systems) for traditional networks/computers and those for the IoT devices are still signature/rule-based. It is well known that such detection systems cannot handle new kind of attacks or new variants. And, the formal rule-based detection techniques would be circumvented by attackers. Moreover, for many IoT devices, so many signatures/rules often cannot be operated. Machine learning-based technologies are attracted much attention from many researchers and developers in recent years. Such methods can detect specific attacks or just detect anomalies. In many related works, one classifier is often trained for detecting multiple kinds of attacks, which is obviously cannot grantee an optimum performance for every kind of attacks. In this study, we proposed a system that detects multiple specific attacks in a sequential manner. That is, each kind of specific attacks is detected using a designated classifier instead of a common one. An artificial neural network as the classifier is trained and used for each kind of the specific attacks. As a result, the multiple classifiers can detect the specific attacks in a sequential manner. Our proposal is explained in detail in this paper and its performance is examined using different activation functions. We also make it clear which activation function is the best choice for our system.

Original languageEnglish
Title of host publicationProceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages238-244
Number of pages7
ISBN (Electronic)9781728130248
DOIs
Publication statusPublished - Aug 2019
Event17th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019 - Fukuoka, Japan
Duration: Aug 5 2019Aug 8 2019

Publication series

NameProceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019

Conference

Conference17th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019
CountryJapan
CityFukuoka
Period8/5/198/8/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Information Systems
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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  • Cite this

    Soe, Y. N., Feng, Y., Santosa, P. I., Hartanto, R., & Sakurai, K. (2019). A sequential scheme for detecting cyber attacks in IoT environment. In Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019 (pp. 238-244). [8890502] (Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00051