Improving IoT Security using Lightweight Based Deep Learning Protection Model
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Abstract
The Internet of Things (IoT) has recently become an essential ingredient of human life. The main critical challenges that confront IoT are security and protection. Several methods have been developed to protect the IoT; among these methods is Intrusion Detection System (IDS) Deep Learning-based. On the other hand, these types of IDS have a complex operation that takes a long time when applied on IoT devices and is inconvenient for a massive system that includes many connected devices. Thus, this paper suggested a Lightweight Intrusion Detection System (LIDS) IoT model that depends on deep learning using a Multi-Layer Perceptron (MLP) network. LIDS has the following characteristics lightweight, high accuracy, high speed in detection, and deals with a few features in MQTT protocol. The MQTTset dataset was used in training, validating, and testing the proposed model to investigate the performance of the proposed LIDS. The achieved performance ratios for the proposed LIDS, as measured by accuracy and F1-score. The experiment results showed that for the balanced MQTTset dataset, the number of obtained features was 15 with accuracy (95.06) and F1_score (95.31). Also, for the imbalanced MQTTset, the number of obtained features was 12 with accuracy (96.97) and F1-score (98.24). The obtained results have shown the deep learning efficiency role in improving the accuracy of an intrusion detection model by approximately 3.5% compared to other methods in the literature. In addition, the proposed methods reduced the number of features by around 50% of the total number of features, leading to a LIDS operating in a constrained environment.
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References
Green J. (2014, March). The internet of things reference model. In Internet of Things World Forum (pp. 1-12). San Jose, CA, USA: CISCO.
Ghannadrad A. (2021). Machine Learning-Based DoS Attacks Detection for MQTT Sensor Networks.
Razzaq MA, Gill SH, Qureshi MA, Ullah S. Security Issues in the Internet of Things (IoT): A Comprehensive Study. International Journal of Advanced Computer Science and Applications 2017; 8(6): 383-388. DOI: https://doi.org/10.14569/IJACSA.2017.080650
Galán CO, Lasheras FS, de Cos Juez, FJ, Sánchez AB. Missing Data Imputation of Questionnaires by Means of Genetic Algorithms with Different Fitness Functions. Journal of Computational and Applied Mathematics 2017; 311: 704-717. DOI: https://doi.org/10.1016/j.cam.2016.08.012
Kolias C, Kambourakis G, Stavrou A, Voas J. DDoS in the IoT: Mirai and other botnets. Computer 2017; 50(7): 80-84. DOI: https://doi.org/10.1109/MC.2017.201
Moustafa N, Slay J. (2015, November). UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In 2015 military communications and information systems conference (MilCIS) (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/MilCIS.2015.7348942
Tavallaee M, Bagheri E, Lu W, Ghorbani AA. (2009, July). A detailed analysis of the KDD CUP 99 data set. In 2009 IEEE symposium on computational intelligence for security and defense applications (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/CISDA.2009.5356528
Oluranti J, Omoregbe N, Misra S. Effect of feature selection on performance of internet traffic classification on NIMS multi-class dataset. In Journal of Physics: Conference Series IOP Publishing 2019; 1299(1): 012035. DOI: https://doi.org/10.1088/1742-6596/1299/1/012035
Ben-Asher N, Gonzalez C. Effects of Cyber Security Knowledge on Attack Detection. Computers in Human Behavior 2015; 48: 51- 61. DOI: https://doi.org/10.1016/j.chb.2015.01.039
Prabha K, Sree SS. A Survey on IPS Methods and Techniques. International Journal of Computer Science Issues (IJCSI) 2016; 13(2): 38. DOI: https://doi.org/10.20943/01201602.3843
Hamed T, Ernst JB, Kremer SC. A Survey and Taxonomy of Classifiers of Intrusion Detection Systems. Computer and Network Security Essentials 2018; 21-39. DOI: https://doi.org/10.1007/978-3-319-58424-9_2
AP H. Secure-MQTT: An Efficient Fuzzy Logic-Based Approach to Detect Dos Attack in MQTT Protocol for Internet of Things. EURASIP Journal on Wireless Communications and Networking 2019; (1): 1-15. DOI: https://doi.org/10.1186/s13638-019-1402-8
Fenanir S, Semchedine F, Baadache A. A Machine Learning-Based Lightweight Intrusion Detection System for the Internet of Things. Revue d'Intelligence Artificielle 2019; 33(3): 203-211. DOI: https://doi.org/10.18280/ria.330306
Alsamiri J, Alsubhi K. Internet of Things Cyber Attacks Detection Using Machine Learning. International Journal of Advanced Computer Science and Applications 2019; 10(12). DOI: https://doi.org/10.14569/IJACSA.2019.0101280
Lashkari, A. H., Draper-Gil, G., Mamun, M. S. I., & Ghorbani, A. A. (2017, February). Characterization of tor traffic using time based features. In ICISSp (pp. 253-262). DOI: https://doi.org/10.5220/0005740704070414
Vaccari I, Chiola G, Aiello M, Mongelli M, Cambiaso E. MQTTset, A New Dataset for Machine Learning Techniques on MQTT. Sensors 2020; 20(22): 6578(1-17). DOI: https://doi.org/10.3390/s20226578
Susilo B, Sari RF. Intrusion Detection in IoT Networks using Deep Learning Algorithm. Information 2020; 11(5): 279(1-11). DOI: https://doi.org/10.3390/info11050279
Dissanayake MB. Feature Engineering for Cyber-attack detection in Internet of Things. (2021). DOI: https://doi.org/10.5815/ijwmt.2021.06.05
Azizan AH, Mostafa SA, Mustapha A, Foozy CFM, Wahab MHA, Mohammed M A, Khalaf BA. A Machine Learning Approach for Improving the Performance of Network Intrusion Detection Systems. Annals of Emerging Technologies in Computing (AETiC) 2021; 5(5): 201-208. DOI: https://doi.org/10.33166/AETiC.2021.05.025
Alfoudi AS, Aziz MR, Alyasseri ZAA, Alsaeedi AH, Nuiaa RR, Mohammed MA, ... & Jaber MM. (2022). Hyper clustering model for dynamic network intrusion detection. IET Communications. DOI: https://doi.org/10.1049/cmu2.12523
Soni D, Makwana A. A Survey on MQTT: A Protocol of Internet of Things (IoT). In International conference on telecommunication, power analysis and computing techniques (ICTPACT-2017) 2017; 20: 173-177.
R. K. Kodali and S. Soratkal, "MQTT Based Home Automation System using ESP8266," 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Agra, India, 2016, pp. 1-5. DOI: https://doi.org/10.1109/R10-HTC.2016.7906845
Banks A. (2014). MQTT Version 3.1. 1. Edited by Andrew Banks and Rahul Gupta. OASIS Standard.
Bandyopadhyay S, Bhattacharyya A. (2013, January). Lightweight Internet protocols for web enablement of sensors using constrained gateway devices. In 2013 International Conference on Computing, Networking and Communications (ICNC) (pp. 334-340). IEEE. DOI: https://doi.org/10.1109/ICCNC.2013.6504105
Mishra B, Kertesz A. "The Use of MQTT in M2M and IoT Systems: A Survey," in IEEE Access, vol. 8, pp. 201071-201086, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3035849
Fehrenbach, P. (2018). Messaging Queues in the IoT under pressure. Computational Science and Its Applications, ICCSA, 1-9.
Egli, P. R. (2015). An introduction to MQTT, a protocol for M2M and IoT applications. Indigoo. com.
Elkhadir, Z., Chougdali, K., & Benattou, M. (2017, November). An effective cyber attack detection system based on an improved OMPCA. In 2017 International Conference on Wireless Networks and Mobile Communications (WINCOM) (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/WINCOM.2017.8238162
Mohammadi S, Mirvaziri H, Ghazizadeh-Ahsaee M, Karimipour H. Cyber Intrusion Detection by Combined Feature Selection Algorithm. Journal of Information Security and Applications 2019; 44: 80-88. DOI: https://doi.org/10.1016/j.jisa.2018.11.007
Moustafa N, Turnbull B, Choo KKR. An Ensemble Intrusion Detection Technique Based on Proposed Statistical Flow Features for Protecting Network Traffic of Internet of Things. IEEE Internet of Things Journal 2018; 6(3): 4815-4830. DOI: https://doi.org/10.1109/JIOT.2018.2871719
Shalaginov, A., Semeniuta, O., & Alazab, M. (2019, December). MEML: Resource-aware MQTT-based machine learning for network attacks detection on IoT edge devices. In Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion (pp. 123-128). DOI: https://doi.org/10.1145/3368235.3368876
Meidan Y, Bohadana M, Mathov Y, Mirsky Y, Shabtai A, Breitenbacher D, Elovici Y. N-Baiot—Network-Based Detection of Iot Botnet Attacks using Deep Autoencoders. IEEE Pervasive Computing 2018; 17(3): 12-22. DOI: https://doi.org/10.1109/MPRV.2018.03367731
Li J, Zhao Z, Li R, Zhang H. Ai-Based Two-Stage Intrusion Detection for Software Defined IoT Networks. IEEE internet of Things Journal 2018; 6(2): 2093-2102. DOI: https://doi.org/10.1109/JIOT.2018.2883344
Guerra-Manzanares, A., Medina-Galindo, J., Bahsi, H., & Nõmm, S. (2020, February). MedBIoT: Generation of an IoT Botnet Dataset in a Medium-sized IoT Network. In ICISSP (pp. 207-218). DOI: https://doi.org/10.5220/0009187802070218
Moustafa, N. (2019, October). New generations of internet of things datasets for cybersecurity applications based machine learning: TON_IoT datasets. In Proceedings of the eResearch Australasia Conference, Brisbane, Australia (pp. 21-25).
Ciklabakkal, E., Donmez, A., Erdemir, M., Suren, E., Yilmaz, M. K., & Angin, P. (2019, October). ARTEMIS: An intrusion detection system for MQTT attacks in Internet of Things. In 2019 38th Symposium on Reliable Distributed Systems (SRDS) (pp. 369-3692). IEEE. DOI: https://doi.org/10.1109/SRDS47363.2019.00053
Ghazanfar, S., Hussain, F., Rehman, A. U., Fayyaz, U. U., Shahzad, F., & Shah, G. A. (2020, March). Iot-flock: An open-source framework for iot traffic generation. In 2020 International Conference on Emerging Trends in Smart Technologies (ICETST) (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/ICETST49965.2020.9080732
Wood AD, Stankovic JA. Denial of Service in Sensor Networks. Computer 2002; 35(10): 54-62. DOI: https://doi.org/10.1109/MC.2002.1039518
Vaccari I, Aiello M, Cambiaso E. SlowITe, a Novel Denial of Service Attack Affecting MQTT. Sensors 2020; 20(10):2932. DOI: https://doi.org/10.3390/s20102932
Cambiaso E, Papaleo G, Chiola G, Aiello M. Slow DoS Attacks: Definition and Categorisation. International Journal of Trust Management in Computing and Communications 2013; 1(3-4): 300-319. DOI: https://doi.org/10.1504/IJTMCC.2013.056440
Cambiaso, E., Papaleo, G., Chiola, G., & Aiello, M. (2015, June). Designing and modeling the slow next DoS attack. In Computational intelligence in security for information systems conference (pp. 249-259). Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-19713-5_22
Cambiaso, E., Papaleo, G., & Aiello, M. (2012, October). Taxonomy of slow DoS attacks to web applications. In International Conference on Security in Computer Networks and Distributed Systems (pp. 195-204). Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/978-3-642-34135-9_20
Bhagat Patil, A. R., & Thakur, N. V. (2019). Mitigation Against Denial-of-Service Flooding and Malformed Packet Attacks. In Third International Congress on Information and Communication Technology (pp. 335-342). Springer, Singapore. DOI: https://doi.org/10.1007/978-981-13-1165-9_30
Stiawan, D., Idris, M., Malik, R. F., Nurmaini, S., Alsharif, N., & Budiarto, R. (2019). Investigating brute force attack patterns in IoT network. Journal of Electrical and Computer Engineering, 2019. DOI: https://doi.org/10.1155/2019/4568368
Chawla, S. (2017). Deep learning based intrusion detection system for Internet of Things.thesis, University of Washington.
Ketkar, N., & Santana, E. (2017). Deep learning with Python (Vol. 1). Berkeley: Apress. DOI: https://doi.org/10.1007/978-1-4842-2766-4_1