New Hybrid Distributed Attack Detection System for IoT

Abstract

IoT is expressed as a network of physical objects with applications and various technologies that provide data connection and sharing with various devices and systems over the Internet. Security vulnerabilities in IoT devices are one of the biggest security issues in connecting devices to the internet and collecting and processing user data. These vulnerabilities can lead to increased attacks on IoT devices and malicious use of user data. In this article, we discuss these security problems that arise in IoT systems in detail in distributed systems technology. Distributed systems are increasingly used in the modern computing world. These systems are a structure where multiple independent computers communicate with each other for a common purpose. Distributed system technologies have become more common with the development of internet and cloud computing systems. However, the use of distributed systems has brought with it important security challenges such as security vulnerabilities, access controls and data integrity issues. Therefore, the security of distributed system technologies has been an important focus of work in this area. In this study, information about distributed system technologies and security for IoT is given. The all attack types were classified using ANN, developed RF and hybrid model. In RF, all feature vectors created from all datasets (bank and two financial datasets) were also analyzed separately and the classification performance was examined. In addition, a new RF algorithm based on weight values using the Gini algorithm has been proposed. With this algorithm, the traditional RF algorithm has been developed and the success rates have been increased. In addition, a hybrid method was created by classifying the datasets obtained by RF with ANN. With the hybrid method ANN and the enhanced RF method, its accuracy in detecting normal behaviors and attack types was calculated and the success of the methods was presented comparatively. In addition, the working times of the methods were determined.

Keywords

• Random Forest
• Artificial Neural Network
• Security
• IoT
• Distributed System.

References

1. [1] M. Wang, and Q. Zhang, “Optimized data storage algorithm of IoT based on cloud computing in distributed system,” Computer Communications, vol. 157, pp.124-131, 2020.
2. [2] G. Eleftherakis, D. Pappas, T. Lagkas, and K. Rousis, “Architecting the IoT paradigm: a middleware for autonomous distributed sensor networks,” International Journal of Distributed Sensor Networks, vol. 11, no.12, pp.139735-139735, 2015.
3. [3] X. Yu, J. Chu, and K. Yu, “Energy-efficiency optimization for IoT-distributed antenna systems with SWIPT over composite fading channels,” IEEE Internet of Things Journal, vol. 7, no.1, pp. 197-207, 2019 .
4. [4] A. R. Sfar, E. Natalizio, Y. Challal, and Z. Chtourou, “A roadmap for security challenges in the Internet of Things,” Digital Communications and Networks, vol. 4, no.2, pp. 118-137, 2018.
5. [5] S. Keoh, S. Kumar, and H. Tschofenig, “Securing the internet of things: A standardization perspective,” IEEE Internet of things Journal, vol. 1, no.3, pp. 265-275, 2014.
6. [6] K. Jaswal, T. Choudhury, and R. Chhokar, “Securing the Internet of Things: A proposed framework,” In 2017 International Conference on Computing, Communication and Automation (ICCCA), 2017, pp. 1277-1281.
7. [7] P. Sivaraman, C. Sharmeela, P. Sanjeevikumar, “Health Monitoring of a Transformer in a Smart Distribution System using IoT,” In IoT, Machine Learning and Blockchain Technologies for Renewable Energy and Modern Hybrid Power Systems River Publishers, pp. 79-91, 2023.
8. [8] G. Bhandari, A. Lyth, and A. Shalaginov, “Distributed Deep Neural-Network-Based Middleware for Cyber-Attacks Detection in Smart IoT Ecosystem: A Novel Framework and Performance Evaluation Approach,” Electronics, vol. 12, no. 2, pp. 298-298, 2023.

9. [9] A. Ukil, J. Sen, and S. Koilakonda, “Embedded security for Internet of Things,” In 2011 2nd National Conference on Emerging Trends and Applications in Computer Science, 2011, pp. 1-6.
10. [10] M. H. Ali, M. Jaber, and S. Abd, “Threat analysis and distributed denial of service (DDoS) attack recognition in the internet of things (IoT),” Electronics, vol. 11, no.3, pp. 494-494, 2022.
11. [11] H. Moudoud, Z. Mlika, L. Khoukhi, and S. Cherkaoui, “Detection and prediction of fdi attacks in iot systems via hidden markov model,” IEEE Transactions on Network Science and Engineering, vol. 9, no.5, pp. 2978-2990, 2022.
12. [12] Y. Labiod, Y, A. Korba, and N. Ghoualmi, “Fog computing-based intrusion detection architecture to protect iot networks,” Wireless Personal Communications, vol. 125, no.1, pp. 231-259, 2022.
13. [13] M. Habiba, M.R. Islam, S.M. Muyeen, and A.S. Ali, “Edge intelligence for network intrusion prevention in IoT ecosystem,” Computers and Electrical Engineering, vol.108, pp.108727-108727, 2023.
14. [14] Y. Alotaibi, and M. Ilyas, “Ensemble-Learning Framework for Intrusion Detection to Enhance Internet of Things’ Devices Security,” Sensors, vol. 23, no. 12, pp. 5568-5568, 2023.
15. [15] W. A. Mahmoud, M. Fathi, H. El-Badawy, and R. Sadek, R, “Performance Analysis of IDS_MDL Algorithm to Predict Intrusion Detection for IoT Applications”, In 2023 40th National Radio Science Conference (NRSC), vol. 1, 2023, pp. 139-149.
16. [16] C. Sun, D. J. Cardenas, A. Hahn, and C. Liu, “Intrusion detection for cybersecurity of smart meters”, IEEE Transactions on Smart Grid, vol. 12, no. 1, pp. 612-622, 2020.
17. [17] R. A. Elsayed, and R.A.Hamada, “Securing IoT and SDN systems using deep-learning based automatic intrusion detection”, Ain Shams Engineering Journal, vol. 14, no. 10, pp.102211- 102211, 2023.
18. [18] K. Sasikala, and S. Vasuhi, “Anomaly Based Intrusion Detection on IOT Devices using Logistic Regression”, In 2023 International Conference on Networking and Communications (ICNWC), 2023, pp. 1-5.
19. [19] A. F. J. Jasim, and S. Kurnaz, “New automatic (IDS) in IoTs with artificial intelligence technique”, Optik, vol. 273, pp.170417-170417, 2023.
20. [20] M. Almiani, A. AbuGhazleh, and A. Razaque, “Deep recurrent neural network for IoT intrusion detection system”, Simulation Modelling Practice and Theory, 101, 102031, 2020.
21. [21] S. S. Kareem, R. R Mostafa, F.A. Hashim, and H. M. El-Bakry, “An effective feature selection model using hybrid metaheuristic algorithms for iot intrusion detection”, Sensors, vol. 22, no. 4, pp. 1396- 1396, 2022.
22. [22] M. K. Pehlivanoğlu, A.Kuyucu, K.A. Recep, “IoT Veri Kümelerinde Makine Öğrenmesi Tekniklerine Dayalı Saldırı Tespiti”, Avrupa Bilim ve Teknoloji Dergisi, vol. 52, pp. 19-26, 2023.
23. [23] R. Kozik, M. Pawlicki, and M. Choraś, “A new method of hybrid time window embedding with transformer-based traffic data classification in IoT-networked environment”, Pattern Analysis and Applications, vol. 24, no. 4, pp. 1441-1449, 2021.
24. [24] A. Gökdemir, and A. Calhan, “Deep learning and machine learning based anomaly detection in internet of things environments”, Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 37, no. 4, pp. 1945-1956, 2022.
25. [25] E.G. Ölmez, and İ. Kenan, “IoT Botnet Verisetlerinin Karşılaştırmalı Analizi”, Computer Science, 151-164, 2022.
26. [26] O. Yaman, and R. Tekin, “Akıllı Ev Sistemleri için XGBoost Tabanlı Saldırı Tespit Yöntemi”, Journal of Intelligent Systems: Theory & Applications, vol. 6, no. 2, 2023.
27. [27] Ş. Okul, and M. A. Aydın, “Security attacks on IoT”, In 2017 International Conference on Computer Science and Engineering (UBMK), 2017, pp. 1-5.
28. [28] A. A. Ismael, and A.Varol, “IoT Sistemini Güvenliği: Yeni Bir Model”, 5th National Informatics Congress, 2018.
29. [29] K. İlhan, and Ş. Abdülkadir, Ş. “IoT Ağ Güvenliği için 802.1 x, DMZ ve SSL-VPN Birleştirme Tabanlı Etkili bir Güvenlik Yöntemi”. Acta Infologica, vol. 4, no. 2, pp. 65-76, 2020.
30. [30] J. Azimjonov, and T. Kim, “Designing accurate lightweight intrusion detection systems for IoT networks using fine-tuned linear SVM and feature selectors”, Computers & Security, vol. 137, no. 103598, 2024.
31. [31] P. Vijayan, and S. Sundar, “Original Research Article IoT intrusion detection system using ensemble classifier and hyperparameter optimization using tuna search algorithm”, Journal of Autonomous Intelligence, vol. 7, no.2, pp. 1-10, 2024.
32. [32] A. Biju, and S.W. Franklin, “Evaluated bird swarm optimization based on deep belief network (EBSO-DBN) classification technique for IOT network intrusion detection”, Automatika, vol. 65, no. 1, pp. 108-116, 2024.
33. [33] S. Shen, C. Cai, and S. Yu, “Deep Q-network-based heuristic intrusion detection against edge-based SIoT zero-day attacks”, Applied Soft Computing, vol. 150, no. 111080, 2024.
34. [34] M. Abomhara, and G. M. Køien, “Security and privacy in the Internet of Things: Current status and open issues,” In 2014 international conference on privacy and security in mobile systems (PRISMS), 2014, pp. 1-8.
35. [35] J. Park, and Y. S. Jeong, “Dynamic analysis for IoT malware detection with convolution neural network model,” IEEE Access, vol. 8, pp. 96899-96911, 2020.
36. [36] S. Smys, H. Wang, and A. Basar, “5G network simulation in smart cities using neural network algorithm,” Journal of Artificial Intelligence, vol. 3, no. 1, pp. 43-52, 2021.
37. [37] D. K. Reddy, H. S. Behera, and J. Nayak, “Deep neural network based anomaly detection in Internet of Things network traffic tracking for the applications of future smart cities,” Transactions on Emerging Telecommunications Technologies, vol. 32, no. 7, 2021.
38. [38] W. Pannakkong, K. Thiwa-Anont, K. Singthong, K., and J. Buddhakulsomsiri, “Hyperparameter tuning of machine learning algorithms using response surface methodology: a case study of ANN, SVM, and DBN”, Mathematical problems in engineering, 2022, 1-17, 2022.
39. [39] S. S. Roy, S. Dey, and S. Chatterjee, “Autocorrelation aided random forest classifier-based bearing fault detection framework”, IEEE Sensors Journal, vol. 20, no. 18, pp. 10792-10800, 2020.
40. [40] Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
41. [41] J. Wang, C. Rao, and X. Xiao, “Risk assessment of coronary heart disease based on cloud-random forest”, Artificial Intelligence Review, vol. 56, no. 1, pp. 203-232, 2023.
42. [42] H. Parmar, S. Bhanderi, S., and G. Shah, “Sentiment mining of movie reviews using Random Forest with Tuned Hyperparameters”, In International Conference on Information Science, Kerela, 2014, pp. 1-6.
43. [43] F. James, “IoT cybersecurity based smart home intrusion prevention system”, In 2019 3rd Cyber Security in Networking Conference (CSNet), 2019, pp. 107-113.