Advanced detection Denial of Service attack in the Internet of Things network based on MQTT protocol using fuzzy logic

Authors

  • Mochamad Soebagja Budiana Telkom University, Bandung
  • Ridha Muldina Negara Telkom University, Bandung
  • Arif Indra Irawan Telkom University, Bandung
  • Harashta Tatimma Larasati Pusan National University, Busan

DOI:

https://doi.org/10.26594/register.v7i2.2340

Keywords:

denial of service, fuzzy-logic, IoT, message queuing telemetry transport, MQTT

Abstract

Message Queuing Telemetry Transport (MQTT) is one of the popular protocols used on the Internet of Things (IoT) networks because of its lightweight nature. With the increasing number of devices connected to the internet, the number of cybercrimes on IoT networks will increase. One of the most popular attacks is the Denial of Service (DoS) attack. Standard security on MQTT uses SSL/TLS, but SSL/TLS is computationally wasteful for low-powered devices. The use of fuzzy logic algorithms with the Intrusion Detection System (IDS) scheme is suitable for detecting DoS because of its simple nature. This paper uses a fuzzy logic algorithm embedded in a node to detect DoS in the MQTT protocol with feature selection nodes. This paper's contribution is that the nodes feature selection used will monitor SUBSCRIBE and SUBACK traffic and provide this information to fuzzy input nodes to detect DoS attacks. Fuzzy performance evaluation is measured against changes in the number of nodes and attack intervals. The results obtained are that the more the number of nodes and the higher the traffic intensity, the fuzzy performance will decrease, and vice versa. However, the number of nodes and traffic intensity will affect fuzzy performance.

Author Biographies

Mochamad Soebagja Budiana, Telkom University, Bandung

School of Electrical Engineering

Ridha Muldina Negara, Telkom University, Bandung

School of Electrical Engineering

Arif Indra Irawan, Telkom University, Bandung

School of Electrical Engineering

Harashta Tatimma Larasati, Pusan National University, Busan

School of Computer Science and Engineering

References

[1] A. P. Haripriya and K. Kulothungan, "Secure-MQTT: an efficient fuzzy logic-based approach to detect DoS attack in MQTT protocol for internet of things," J. Wireless Com. Network, vol. 90, 2019.

[2] A. Velinov and A. Mileva, "Running and Testing Applications for Contiki OS Using Cooja Simulator," in International Conference on Information Technology and Development of Education – ITRO 2016, Zrenjanin, Republic of Serbia, 2016.

[3] Y. Maleh, A. Ezzati and M. Belaissaoui, "An Enhanced DTLS Protocol for Internet of Things Applications," in 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM), Fez, Morocco, 2016.

[4] P. Kasinathan, C. Pastrone, M. A. Spirito and M. Vinkovits, "Denial-of-Service detection in 6LoWPAN based Internet of Things," in 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Lyon, France, 2013.

[5] W. Li, S. Tug, W. Meng and Y. Wang, "Designing collaborative blockchained signature-based intrusion detection in IoT environments," Future Generation Computer Systems, vol. 96, pp. 481-489, 2019.

[6] M. S. Harsha, B. M. Bhavani and K. R. Kundhavai, "Analysis of vulnerabilities in MQTT security using Shodan API and implementation of its countermeasures via authentication and ACLs," in 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India, 2018.

[7] S. Andy, B. Rahardjo and B. Hanindhito, "Attack scenarios and security analysis of MQTT communication protocol in IoT system," in 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Yogyakarta, Indonesia, 2017.

[8] S. Shin, K. Kobara, C.-C. Chuang and W. Huang, "A security framework for MQTT," in 2016 IEEE Conference on Communications and Network Security (CNS), Philadelphia, PA, USA, 2016.

[9] G. Potrino, F. d. Rango and A. F. Santamaria, "Modeling and evaluation of a new IoT security system for mitigating DoS attacks to the MQTT broker," in 2019 IEEE Wireless Communications and Networking Conference (WCNC), Marrakesh, Morocco, 2019.

[10] S. Hameed, F. I. Khan and B. H. R. J. o. C. N. a. C. 2019., "Understanding Security Requirements and Challenges in Internet of Things (IoT): A Review," Journal of Computer Networks and Communications, 2019.

[11] G. Potrino, F. D. Rango and P. Fazio, "A Distributed Mitigation Strategy against DoS attacks in Edge Computing," in 2019 Wireless Telecommunications Symposium (WTS), New York, NY, USA, 2019.

[12] K. A. d. Costa, J. P. Papa, C. O. Lisboa, R. Munoz and V. H. C. d. Albuquerque, "Internet of Things: A survey on machine learning-based intrusion detection approaches," Computer Networks, vol. 151, pp. 147-157, 2019.

[13] G. Karatas, O. Demir and O. K. Sahingoz, "Deep Learning in Intrusion Detection Systems," in 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), Ankara, Turkey, 2018.

[14] M. S. Munir, I. S. Bajwa and S. M. Cheema, "An intelligent and secure smart watering system using fuzzy logic and blockchain," Computers & Electrical Engineering, vol. 77, pp. 109-119, 2019.

[15] S. K. Yee and J. V. Milanović, "Fuzzy logic controller for decentralized stabilization of multimachine power systems," IEEE Transactions on Fuzzy Systems, vol. 16, no. 4, pp. 971-981, 2008.

[16] M. Alali, A. Almogren, M. M. Hassan, I. A. Rassan and M. Z. A. Bhuiyan, "Improving risk assessment model of cyber security using fuzzy logic inference system," Computers & Security, vol. 74, pp. 323-339, 2018.

[17] S. Dick, "Toward complex fuzzy logic," IEEE Transactions on Fuzzy Systems, vol. 13, no. 3, pp. 405-414, 2005.

[18] I. B. d. Medeiros, M. A. S. Machado, W. J. Damasceno, A. M. Caldeira, R. C. d. Santos and J. B. d. S. Filho, "A Fuzzy Inference System to Support Medical Diagnosis in Real Time," Procedia Computer Science, vol. 122, pp. 167-173, 2017.

[19] K. Ozera, K. Bylykbashi, Y. Liu and L. Barolli, "A fuzzy-based approach for cluster management in VANETs: Performance evaluation for two fuzzy-based systems," Internet of Things, vol. 3–4, pp. 120-133, 2018.

[20] A. F. Santamaria, F. D. Rango, A. Serianni and P. Raimondo, "A real IoT device deployment for e-Health applications under lightweight communication protocols, activity classifier and edge data filtering," Computer Communication, vol. 128, pp. 60-73, 2018.

[21] I. Vaccari, M. Aiello and E. Cambiaso, "SlowITe, a Novel Denial of Service Attack Affecting MQTT," Sensors, vol. 20, no. 10, 2020.

[22] I. Vaccari, M. Aiello and E. Cambiaso, "SlowTT: A Slow Denial of Service Against IoT Networks," Information, vol. 11, no. 9, 2020.

[23] S. H. Ramos, M. T. Villalba and R. Lacuesta, "MQTT Security: A Novel Fuzzing Approach," Wireless Communications and Mobile Computing, 2018.

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Published

2021-04-19

How to Cite

[1]
M. S. Budiana, R. M. Negara, A. I. Irawan, and H. T. Larasati, “Advanced detection Denial of Service attack in the Internet of Things network based on MQTT protocol using fuzzy logic”, regist. j. ilm. teknol. sist. inf., vol. 7, no. 2, pp. 95–106, Apr. 2021.

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