International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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Enhancing Internet of Things Security with Random Forest-Based Anomaly Detection

  • Ahmed Al Shihimi (Military Technological College) ;
  • Muhammad R Ahmed (Military Technological College) ;
  • Thirein Myo (Military Technological College) ;
  • Badar Al Baroomi (Military Technological College)
  • 투고 : 2024.06.05
  • 발행 : 2024.06.30
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초록

The Internet of Things (IoT) has revolutionized communication and device operation, but it has also brought significant security challenges. IoT networks are structured into four levels: devices, networks, applications, and services, each with specific security considerations. Personal Area Networks (PANs), Local Area Networks (LANs), and Wide Area Networks (WANs) are the three types of IoT networks, each with unique security requirements. Communication protocols such as Wi-Fi and Bluetooth, commonly used in IoT networks, are susceptible to vulnerabilities and require additional security measures. Apart from physical security, authentication, encryption, software vulnerabilities, DoS attacks, data privacy, and supply chain security pose significant challenges. Ensuring the security of IoT devices and the data they exchange is crucial. This paper utilizes the Random Forest Algorithm from machine learning to detect anomalous data in IoT devices. The dataset consists of environmental data (temperature and humidity) collected from IoT sensors in Oman. The Random Forest Algorithm is implemented and trained using Python, and the accuracy and results of the model are discussed, demonstrating the effectiveness of Random Forest for detecting IoT device data anomalies.

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참고문헌

  1. K. O. M. Salih, T. A. Rashid, D. Radovanovic, and N. Bacanin, "A Comprehensive Survey on the Internet of Things with the Industrial Marketplace," Sensors, vol. 22, no. 3, Art. no. 3, Jan. 2022, doi: 10.3390/s22030730.
  2. M. Ayaz, M. Ammad-Uddin, Z. Sharif, A. Mansour, and E.-H. M. Aggoune, "Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk," IEEE Access, vol. 7, pp. 129551-129583, 2019, doi: 10.1109/ACCESS.2019.2932609.
  3. C. Maple, "Security and privacy in the internet of things," Journal of Cyber Policy, vol. 2, no. 2, pp. 155-184, May 2017, doi: 10.1080/23738871.2017.1366536.
  4. M. Wu, T.-J. Lu, F.-Y. Ling, J. Sun, and H.-Y. Du, "Research on the architecture of Internet of Things," in 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE), Aug. 2010, pp. V5-484-V5-487. doi: 10.1109/ICACTE.2010.5579493.
  5. M. Saqlain, M. Piao, Y. Shim, and J. Y. Lee, "Framework of an IoT-based Industrial Data Management for Smart Manufacturing," Journal of Sensor and Actuator Networks, vol. 8, no. 2, Art. no. 2, Jun. 2019, doi: 10.3390/jsan8020025.
  6. G. Pau, C. Chaudet, D. Zhao, and M. Collotta, "Next Generation Wireless Technologies for Internet of Things," Sensors, vol. 18, no. 1, Art. no. 1, Jan. 2018, doi: 10.3390/s18010221.
  7. R. C. Braley, I. C. Gifford, and R. F. Heile, "Wireless personal area networks: an overview of the IEEE P802.15 working group," SIGMOBILE Mob. Comput. Commun. Rev., vol. 4, no. 1, pp. 26-33, Jan. 2000, doi: 10.1145/360449.360465.
  8. S. Kraijak and P. Tuwanut, "A survey on IoT architectures, protocols, applications, security, privacy, real-world implementation and future trends," in 11th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2015), Sep. 2015, pp. 1-6. doi: 10.1049/cp.2015.0714.
  9. M. R. Ahmed, A. Al Shihimi, T. Myo, B. Al Baroomi, and M. A. Aseeri, "Internet of Things Network Architecture and Security Challenges," presented at the Second International Conference on Advances in Software Engineering and Information Technology, Mumbai: Hinweis, Jun. 2023.
  10. M. A. Jabraeil Jamali, B. Bahrami, A. Heidari, P. Allahverdizadeh, and F. Norouzi, "IoT Architecture," in Towards the Internet of Things: Architectures, Security, and Applications, M. A. Jabraeil Jamali, B. Bahrami, A. Heidari, P. Allahverdizadeh, and F. Norouzi, Eds., in EAI/Springer Innovations in Communication and Computing. Cham: Springer International Publishing, 2020, pp. 9-31. doi: 10.1007/978-3-030-18468-1_2.
  11. C.-L. Zhong, Z. Zhu, and R.-G. Huang, "Study on the IOT Architecture and Gateway Technology," in 2015 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), Aug. 2015, pp. 196-199. doi: 10.1109/DCABES.2015.56.
  12. S. Kumar, P. Tiwari, and M. Zymbler, "Internet of Things is a revolutionary approach for future technology enhancement: a review," J Big Data, vol. 6, no. 1, p. 111, Dec. 2019, doi: 10.1186/s40537-019-0268-2.
  13. S. Oza et al., "IoT: The Future for Quality of Services," in ICCCE 2019, A. Kumar and S. Mozar, Eds., in Lecture Notes in Electrical Engineering. Singapore: Springer, 2020, pp. 291-301. doi: 10.1007/978-981-13-8715-9_35.
  14. D. Sehrawat and N. S. Gill, "Smart Sensors: Analysis of Different Types of IoT Sensors," in 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Apr. 2019, pp. 523-528. doi: 10.1109/ICOEI.2019.8862778.
  15. S. Al-Sarawi, M. Anbar, K. Alieyan, and M. Alzubaidi, "Internet of Things (IoT) communication protocols: Review," in 2017 8th International Conference on Information Technology (ICIT), May 2017, pp. 685-690. doi: 10.1109/ICITECH.2017.8079928.
  16. I. Hedi, I. Speh, and A. Sarabok, "IoT network protocols comparison for the purpose of IoT constrained networks," in 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), May 2017, pp. 501-505. doi: 10.23919/MIPRO.2017.7973477.
  17. K. Shaukat, T. M. Alam, I. A. Hameed, W. A. Khan, N. Abbas, and S. Luo, "A Review on Security Challenges in Internet of Things (IoT)," in 2021 26th International Conference on Automation and Computing (ICAC), Sep. 2021, pp. 1-6. doi: 10.23919/ICAC50006.2021.9594183.
  18. K. Kimani, V. Oduol, and K. Langat, "Cyber security challenges for IoT-based smart grid networks," International Journal of Critical Infrastructure Protection, vol. 25, pp. 36-49, Jun. 2019, doi: 10.1016/j.ijcip.2019.01.001.
  19. H. Lin and N. W. Bergmann, "IoT Privacy and Security Challenges for Smart Home Environments," Information, vol. 7, no. 3, Art. no. 3, Sep. 2016, doi: 10.3390/info7030044.
  20. N. Waheed, X. He, M. Ikram, M. Usman, S. S. Hashmi, and M. Usman, "Security and Privacy in IoT Using Machine Learning and Blockchain: Threats and Countermeasures," ACM Comput. Surv., vol. 53, no. 6, p. 122:1-122:37, Dec. 2020, doi: 10.1145/3417987.
  21. B. K. Mohanta, D. Jena, S. Ramasubbareddy, M. Daneshmand, and A. H. Gandomi, "Addressing Security and Privacy Issues of IoT Using Blockchain Technology," IEEE Internet of Things Journal, vol. 8, no. 2, pp. 881-888, Jan. 2021, doi: 10.1109/JIOT.2020.3008906.
  22. F. Hussain, R. Hussain, S. A. Hassan, and E. Hossain, "Machine Learning in IoT Security: Current Solutions and Future Challenges," IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 1686-1721, 2020, doi: 10.1109/COMST.2020.2986444.
  23. A. B. Feroz Khan and A. G, "A Multi-layer Security approach for DDoS detection in Internet of Things," International Journal of Intelligent Unmanned Systems, vol. 9, no. 3, pp. 178-191, Jan. 2020, doi: 10.1108/IJIUS-06-2019-0029.
  24. F. Nizzi, T. Pecorella, F. Esposito, L. Pierucci, and R. Fantacci, "IoT Security via Address Shuffling: The Easy Way," IEEE Internet of Things Journal, vol. 6, no. 2, pp. 3764-3774, Apr. 2019, doi: 10.1109/JIOT.2019.2892003.
  25. R. Ramya Devi and V. Vijaya Chamundeeswari, "Triple DES: Privacy Preserving in Big Data Healthcare," Int J Parallel Prog, vol. 48, no. 3, pp. 515-533, Jun. 2020, doi: 10.1007/s10766-018-0592-8.
  26. M. K. Abiodun, J. B. Awotunde, R. O. Ogundokun, E. A. Adeniyi, and M. O. Arowolo, "Security and Information Assurance for IoT-Based Big Data," in Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities, S. Misra and A. Kumar Tyagi, Eds., in Studies in Computational Intelligence. Cham: Springer International Publishing, 2021, pp. 189-211. doi: 10.1007/978-3-030-72236-4_8.
  27. A. Tewari and B. b. Gupta, "A lightweight mutual authentication protocol based on elliptic curve cryptography for IoT devices," International Journal of Advanced Intelligence Paradigms, vol. 9, no. 2-3, pp. 111-121, Jan. 2017, doi: 10.1504/IJAIP.2017.082962.
  28. H. D. Tiwari and J. H. Kim, "Novel Method for DNA-Based Elliptic Curve Cryptography for IoT Devices," ETRI Journal, vol. 40, no. 3, pp. 396-409, 2018, doi: 10.4218/etrij.2017-0220.
  29. P. Barman and B. Saha, "DNA Encoded Elliptic Curve Cryptography System for IoT Security." Rochester, NY, Mar. 19, 2019. Accessed: Jun. 18, 2023. [Online]. Available: https://papers.ssrn.com/abstract=3355530
  30. M. Aledhari, R. M. Parizi, A. Dehghantanha, and K.-K. R. Choo, "A Hybrid RSA Algorithm in Support of IoT Greenhouse Applications," in 2019 IEEE International Conference on Industrial Internet (ICII), Nov. 2019, pp. 233-240. doi: 10.1109/ICII.2019.00049.
  31. W. Sullivan, Decision Tree and Random Forest: Machine Learning and Algorithms: The Future Is Here! CreateSpace Independent Publishing Platform, 2018.
  32. A. Panesar, Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes. Apress, 2019.
  33. C. Strobl, Statistical Issues in Machine Learning: Towards Reliable Split Selection and Variable Importance Measures. Cuvillier Verlag, 2008.
  34. M. R. Ahmed, T. Myo, B. Al Baroomi, M. H. Marhaban, M. S. Kaiser, and M. Mahmud, "A Novel Framework to Detect Anomalous Nodes to Secure Wireless Sensor Networks," in Applied Intelligence and Informatics, M. Mahmud, C. Ieracitano, M. S. Kaiser, N. Mammone, and F. C. Morabito, Eds., in Communications in Computer and Information Science. Cham: Springer Nature Switzerland, 2022, pp. 499-510. doi: 10.1007/978-3-031-24801-6_35.
  35. S. Wan and H. Yang, "Comparison among Methods of Ensemble Learning," in 2013 International Symposium on Biometrics and Security Technologies, Jul. 2013, pp. 286-290. doi: 10.1109/ISBAST.2013.50.
  36. M. W. Ahmad, M. Mourshed, and Y. Rezgui, "Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression," Energy, vol. 164, pp. 465-474, Dec. 2018, doi: 10.1016/j.energy.2018.08.207.