Abstract
Recent advancements in Internet of Things (IoT) infrastructures attribute a rise in undesirable issues specific to network security. As the number of IoT devices connected to the network rises daily, the network is more vulnerable to cyber-attacks. Hence, an intrusion detection system (IDS) is vital for detecting the type of cyber-attacks automatically in a time-bound manner. Moreover, the network often uses the MQTT protocol to deploy communication among IoT devices. This work proposes a CatBoost algorithm, a variant of machine learning (ML) algorithms, to classify the given attack into SlowITe, Malformed, Brute force, Flood, Dos, and Legimate. The algorithm is trained on a publicly available MQTT network dataset by creating a balancing dataset. Despite the significant disparity in the number of labeled records for each dataset class, the algorithm achieves state-of-the-art performance. The test result suggested that the algorithm can classify the type of attack with an accuracy of 94% within 78.45 s in the balanced dataset.
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References
Minerva R, Biru A, Rotondi D (2015) Towards a definition of the internet of things (IoT). IEEE Internet Initiat 1–86
Al-Masri E, Kalyanam KR, Batts J, Kim J, Singh S, Vo T, Yan C (2020) Investigating messaging protocols for the internet of things (IoT). IEEE Access 8:94880–94911. https://doi.org/10.1109/ACCESS.2020.2993363
Stolojescu-crisan C, Crisan C, Butunoi B (2021) An IoT-based smart home automation system. 1–23
Safaei B, Monazzah AMH, Bafroei MB, Ejlali A (2017) Reliability side-effects in internet of things application layer protocols. 2017 2nd Int Conf Syst Reliab Saf 207–212
Soni D, Makwana A (2017) A Survey on Mqtt: a protocol of internet of things (IoT). Int Conf Telecommun Power Anal Comput Tech (Ictpact–2017) 0–5
Hunkeler U, Truong HL, Stanford-clark A MQTT-S–A publish/subscribe protocol for wireless sensor networks
Niruntasukrat A, Issariyapat C, Pongpaibool P, Meesublak K, Aiumsupucgul P, Panya A (2016) Authorization mechanism for MQTT-based internet of things. 2016 IEEE Int Conf Commun Work 290–295
Dorsemaine B, Gaulier J-P, Wary J-P, Kheir N, Urien P (2016) A new approach to investigate IoT threats based on a four layer model. In: Proceedings of the 2016 13th international conference on new technologies for distributed systems (NOTERE), pp 1–6
Mahdavinejad MS, Rezvan M, Barekatain M, Adibi P, Barnaghi P, Sheth AP (2018) Machine learning for internet of things data analysis: a survey. Digit Commun Networks 4:161–175. https://doi.org/10.1016/j.dcan.2017.10.002
da Costa KAP, Papa JP, Lisboa CO, Munoz R, de Albuquerque VHC (2019) Internet of things: a survey on machine learning-based intrusion detection approaches. Comput Networks 151:147–157. https://doi.org/10.1016/j.comnet.2019.01.023
Vaccari I, Cambiaso E, Aiello M (2019) Evaluating security of low-power internet of things networks. Univ Bahrain Sci J 2210–142X
Vaccari I, Aiello M, Cambiaso E (2020) SlowITe, a novel denial of service attack affecting MQTT. Sensors 20. https://doi.org/10.3390/s20102932
Vaccari I, Cambiaso E, Aiello M (2017) Remotely exploiting AT command attacks on ZigBee networks. Secur Commun Networks 2017:1723658. https://doi.org/10.1155/2017/1723658
Vaccari I, Aiello M, Cambiaso E (2020) Innovative protection system against remote AT command attacks on ZigBee networks. Comput Sci 2:2–8
Makhija J, Shetty AA, Bangera A (2022) Classification of attacks on MQTT-based IoT system using machine learning techniques. In: Proceedings, international conference innovation computer communication, pp 217–224
Khan MA, Khan MA, Jan SU, Ahmad J, Jamal SS, Shah AA, Pitropakis N, Buchanan WJ (2021) A deep learning-based intrusion detection system for Mqtt enabled Iot. Sensors 21:1–25. https://doi.org/10.3390/s21217016
Dissanayake MB (2022) Feature engineering for cyber-attack detection in Internet of Things. https://doi.org/10.5815/ijwmt.2021.06.05
Haripriya AP, Kulothungan K (2019) Secure-MQTT: an efficient fuzzy logic-based approach to detect DoS attack in MQTT protocol for internet of things. EURASIP J Wireless Commun Netw 2019(90)
Casteur G, Aubert A, Blondeau B, Clouet V, Quemat A, Pical V, Zitouni R (2020) Fuzzing attacks for vulnerability discovery within MQTT protocol. In: Proceedings of the 2020 international wireless communications and mobile computing (IWCMC), pp 420–425
Hwang HC, Park J, Shon JG (2016) Design and implementation of a reliable message transmission system based on MQTT protocol in IoT. Wirel Pers Commun 91:1765–1777. https://doi.org/10.1007/s11277-016-3398-2
Mishra B, Kertesz A (2020) The use of MQTT in M2M and IoT systems: a survey. IEEE Access 8:201071–201086. https://doi.org/10.1109/ACCESS.2020.3035849
Dinculeană D, Cheng X (2019) Vulnerabilities and limitations of MQTT protocol used between IoT devices. Appl Sci 9. https://doi.org/10.3390/app9050848
Ismail S, Khoei TT, Marsh R, Kaabouch N (2021) A comparative study of machine learning models for cyber-attacks detection in wireless sensor networks. In: Proceedings of the 2021 IEEE 12th annual ubiquitous computing, electronics mobile communication conference (UEMCON), pp 313–318
Vaccari I, Chiola G, Aiello M, Mongelli M, Cambiaso E (2020) MQTTset, a new dataset for machine learning techniques on MQTT. Sensors 20. https://doi.org/10.3390/s20226578
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Vijayan, P.M., Sundar, S. (2023). An Efficient CatBoost Classifier Approach to Detect Intrusions in MQTT Protocol for Internet of Things. In: Chaki, N., Devarakonda, N., Cortesi, A. (eds) Proceedings of International Conference on Computational Intelligence and Data Engineering. ICCIDE 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 163. Springer, Singapore. https://doi.org/10.1007/978-981-99-0609-3_18
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