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Addressing IoT Security Challenges through AI Solutions

by Md Shihab Uddin
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 45
Year of Publication: 2024
Authors: Md Shihab Uddin
10.5120/ijca2024924107

Md Shihab Uddin . Addressing IoT Security Challenges through AI Solutions. International Journal of Computer Applications. 186, 45 ( Oct 2024), 50-55. DOI=10.5120/ijca2024924107

@article{ 10.5120/ijca2024924107,
author = { Md Shihab Uddin },
title = { Addressing IoT Security Challenges through AI Solutions },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2024 },
volume = { 186 },
number = { 45 },
month = { Oct },
year = { 2024 },
issn = { 0975-8887 },
pages = { 50-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number45/addressing-iot-security-challenges-through-ai-solutions/ },
doi = { 10.5120/ijca2024924107 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-10-26T00:55:48.968717+05:30
%A Md Shihab Uddin
%T Addressing IoT Security Challenges through AI Solutions
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 45
%P 50-55
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Internet of Things (IoT) is a widely recognized technology that profoundly influences various sectors, including connectivity, work, healthcare, and the economy. IoT holds the potential to enhance daily life across different environments, from smart cities to educational settings, by automating processes, boosting efficiency, and reducing stress. However, the rise of cyberattacks and threats poses significant challenges to the security of intelligent IoT applications. Traditional methods for securing IoT are becoming increasingly ineffective due to emerging threats and vulnerabilities. To maintain robust security protocols, future IoT systems will require the integration of AI-powered machine learning and deep learning techniques. Leveraging the capabilities of artificial intelligence, particularly through machine learning and deep learning, is essential for equipping next-generation IoT systems with dynamic and adaptive security mechanisms. This paper explores IoT security intelligence from various perspectives, proposing an innovative approach that utilizes machine learning and deep learning to extract insights from raw data, thereby protecting IoT devices against a wide range of cyberattacks. It also discusses how these technologies can be employed to detect attack patterns in unstructured data and enhance the security of IoT devices.

References
  1. Q. Zhou and J. Zhang, "Research prospect of Internet of Things geography", Proceedings of the 19th International Conference on Geoinformatics, pp. 1-5, 2011.
  2. Y. Yu, J. Wang and G. Zhou, "The exploration in the education of professionals in applied Internet of Things engineering", Proceedings of the 4th International Conference on Distance Learning and Education (ICDLE), pp. 74-77, 2010.
  3. J. Li, Z. Huang and X. Wang, "Countermeasure research about developing Internet of Things economy: A case of hangzhou city", Proceedings of the International Conference on E-Business and E-Government (ICEE), 2011
  4. M. M. Hossain, M. Fotouhi and R. Hasan, "Towards an Analysis of Security Issues, Challenges, and Open Problems in the Internet of Things," 2015 IEEE World Congress on Services, New York, NY, USA, 2015, pp. 21-28, doi: 10.1109/SERVICES.2015.12.
  5. Podder, P., Mondal, M., Bharati, S., & Paul, P. K. (2021). Review on the security threats of internet of things. arXiv preprint arXiv:2101.05614.
  6. Bharati, S., Podder, P., Mondal, M. R. H., & Paul, P. K. (2021). Applications and challenges of cloud integrated IoMT. Cognitive Internet of Medical Things for Smart Healthcare: Services and Applications, 67-85.
  7. Mondal, M. R. H., Bharati, S., Podder, P., & Kamruzzaman, J. (2023). Deep Learning and Federated Learning for Screening COVID-19: A Review. BioMedInformatics, 3(3), 691-713.
  8. Hoque, K., Hossain, M. B., Sami, A., Das, D., Kadir, A., & Rahman, M. A. (2024). Technological trends in 5G networks for IoT-enabled smart healthcare: A review. International Journal of Science and Research Archive, 12(2), 1399-1410.
  9. Bazgir, Ehsan, et al. "Security aspects in IoT based cloud computing." World Journal of Advanced Research and Reviews 20.3 (2023): 540-551.
  10. Mazhar, T., Talpur, D.B., Shloul, T.A., Ghadi, Y.Y., Haq, I., Ullah, I., Ouahada, K. and Hamam, H., 2023. Analysis of IoT security challenges and its solutions using artificial intelligence. Brain Sciences, 13(4), p.683.
  11. Rawat, D.B.; Doku, R.; Garuba, M. Cybersecurity in Big Data Era: From Securing Big Data to Data-Driven Security. IEEE Trans. Serv. Comput. 2019, 14, 2055–2072.
  12. Farrokhi, A.; Farahbakhsh, R.; Rezazadeh, J.; Minerva, R. Application of Internet of Things and artificial intelligence for smart fitness: A survey. Comput. Netw. 2021, 189, 107859.
  13. Yahya, F.; Zaki, A.F.A.; Moung, E.G.; Sallehudin, H.; Bakar, N.A.A.; Utomo, R.G. An IoT-based Coastal Recreational Suitability System using Effective Messaging Protocol. Int. J. Adv. Comput. Sci. Appl. 2021, 12, 8.
  14. Routray, S.K.; Gopal, D.; Javali, A.; Sahoo, A. Narrowband IoT (NBIoT) Assisted Smart Grids. In Proceedings of the 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), Coimbatore, India, 25–27March 2021; pp. 1454–1458.
  15. Sangra, P.; Rana, B.; Singh, Y. Energy efficiency in IoT-based smart healthcare. In Proceedings of Third International Conference on Computing, Communications, and Cyber-Security; Springer: Singapore, 2023; pp. 503–515.
  16. Alshamrani, M. IoT and artificial intelligence implementations for remote healthcare monitoring systems: A survey. J. King Saud Univ. Comput. Inf. Sci. 2021, 34, 4687–4701.
  17. S. Bharati, M. R. H. Mondal and P. Podder, "A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?," in IEEE Transactions on Artificial Intelligence, vol. 5, no. 4, pp. 1429-1442, April 2024, doi: 10.1109/TAI.2023.3266418.
  18. Bharati, S., Mondal, M. R. H., Podder, P., & Kose, U. (2023). Explainable artificial intelligence (XAI) with IoHT for smart healthcare: A review. Interpretable Cognitive Internet of Things for Healthcare, 1-24.
  19. Bharati, S., & Podder, P. (2022). Machine and deep learning for iot security and privacy: applications, challenges, and future directions. Security and communication networks, 2022(1), 8951961.
  20. Khandoker Hoque, Md Boktiar Hossain, Denesh Das, Partha Protim Roy . Integration of IoT in Energy Sector. International Journal of Computer Applications. 186, 36 ( Aug 2024), 32-40. DOI=10.5120/ijca2024923981.
  21. Sarker, B., Sharif, N. B., Rahman, M. A., & Parvez, A. S. (2023). AI, IoMT and Blockchain in Healthcare. Journal of Trends in Computer Science and Smart Technology, 5(1), 30-50.
  22. Kaur, J.; Jaskaran; Sindhwani, N.; Anand, R.; Pandey, D. Implementation of IoT in Various Domains, in IoT Based Smart Applications; Springer: Berlin/Heidelberg, Germany, 2023; pp. 165–178.
  23. Albalawi, A.M.; Almaiah, M.A. Assessing and reviewing of cyber-security threats, attacks, mitigation techniques in IoT environment. J. Theor. Appl. Inf. Technol. 2022, 100, 2988–3011.
  24. Deep, S.; Zheng, X.; Jolfaei, A.; Yu, D.; Ostovari, P.; Bashir, A.K. A survey of security and privacy issues in the Internet of Things from the layered context. Trans. Emerg. Telecommun. Technol. 2020, 33, e3935.
  25. Navya, P.; Rama, G.S.; Kumar, T.P.; Pasha, S.N.; Mahender, K. IoT technology: Architecture, stack, security risks, privacy risks and its applications. In Proceedings of AIP Conference Proceedings; AIP Publishing LLC.: College Park, MD, USA, 2022; p. 020062.
  26. Chatterjee, U.; Ray, S. Security Issues on IoT Communication and Evolving Solutions. In Soft Computing in Interdisciplinary Sciences; Springer: Berlin/Heidelberg, Germany, 2022; pp. 183–204.
  27. Haque, A.K.M.B.; Bhushan, B.; Dhiman, G. Conceptualizing smart city applications: Requirements, architecture, security issues, and emerging trends. Expert Syst. 2021, 39, e12753.
  28. Jangjou, M.; Sohrabi, M.K. A Comprehensive Survey on Security Challenges in Different Network Layers in Cloud Computing. Arch. Comput. Methods Eng. 2022, 29, 3587–3608.
  29. Zahran, S.; Elkadi, H.; Helm,W. Fog of Things Framework to Handle Data Streaming Heterogeneity on Internet of Things. In Proceedings of International Conference on Advanced Intelligent Systems and Informatics; Springer International Publishing: Cham, Switzerland, 2022; pp. 653–667.
  30. Rasheed, M.A.; Saleem, J.; Murtaza, H.; Tanweer, H.A.; Rasheed, M.A.; Ahmed, M. A Survey on Fog computing in IoT. VFAST Trans. Softw. Eng. 2022, 9, 4.
  31. Yassein, M.B.; Shatnawi, M.Q. Application layer protocols for the Internet of Things: A survey. In Proceedings of the 2016 International Conference on Engineering & MIS (ICEMIS), Agadir, Morocco, 22–24 September 2016; pp. 1–4.
  32. Donta, P.K.; Srirama, S.N.; Amgoth, T.; Annavarapu, C.S.R. Survey on recent advances in IoT application layer protocols and machine learning scope for research directions. Digit. Commun. Netw. 2021, 8, 727–744.
  33. Kakkar, L.; Gupta, D.; Saxena, S.; Tanwar, S. IoT architectures and its security: A review. In Proceedings of the Second International Conference on Information Management and Machine Intelligence; Springer: Singapore, 2021; pp. 87–94.
  34. Noura, M.; Atiquzzaman, M.; Gaedke, M. Interoperability in Internet of Things: Taxonomies and Open Challenges. Mob. Netw. Appl. 2018, 24, 796–809
  35. Nawalagatti, A. IoT: A Boon for Advancement of Technology. Int. J. Res. Appl. Sci. Eng. Technol. 2022, 10, 652–655
  36. Ja, S.; Dhasb, J.T.M.; Angelc, T.S. Proposed Novel Methodology for Automatic Drainage Block Identification in Smart Cities Using Internet of Things. In Advances in Parallel Computing Algorithms, Tools and Paradigms; IOS Press: Amsterdam, The Netherlands, 2022.
  37. Cˇ olakovic´, A.; Salihovic´, N.; Dželihodžic´, A. Adaptive Traffic Management Systems Based on the Internet of Things (IoT). In Proceedings of Advanced Technologies, Systems, and Applications VII: Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (IAT) 2022; Springer International Publishing: Cham, Switzerland, 2022; pp. 426–437.
  38. Podder, P., Zawodniok, M., & Madria, S. (2024, June). Deep Learning for UAV Detection and Classification via Radio Frequency Signal Analysis. In 2024 25th IEEE International Conference on Mobile Data Management (MDM) (pp. 165-174). IEEE.
  39. Al-lQubaydhi, Nader, et al. "Deep learning for unmanned aerial vehicles detection: A review." Computer Science Review 51 (2024): 100614.
  40. Amit Deb Nath, Rahmanul Hoque, Md. Masum Billah, Numair Bin Sharif, Mahmudul Hoque . Distributed Parallel and Cloud Computing: A Review. International Journal of Computer Applications. 186, 16 ( Apr 2024), 25-32. DOI=10.5120/ijca2024923547
  41. Abusitta, A., de Carvalho, G.H., Wahab, O.A., Halabi, T., Fung, B.C. and Al Mamoori, S., 2023. Deep learning-enabled anomaly detection for IoT systems. Internet of Things, 21, p.100656.
  42. Mekala, S. H., Baig, Z., Anwar, A., & Zeadally, S. (2023). Cybersecurity for Industrial IoT (IIoT): Threats, countermeasures, challenges and future directions. Computer Communications.
  43. Huang, B., Chaki, D., Bouguettaya, A. and Lam, K.Y., 2023. A survey on conflict detection in iot-based smart homes. ACM Computing Surveys, 56(5), pp.1-40.
  44. Wang, X., Zhu, H., Ning, Z., Guo, L. and Zhang, Y., 2023. Blockchain intelligence for internet of vehicles: Challenges and solutions. IEEE Communications Surveys & Tutorials.
  45. E. Manavalan, K. Jayakrishna A review of internet of things (iot) embedded sustainable supply chain for industry 4.0 requirements Comput. Ind. Eng., 127 (2019), pp. 925-953
  46. Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P. and Fischl, M., 2021. Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, pp.502-517.
  47. Elghamrawy, S.M.; Lotfy, M.O.; Elawady, Y.H. An Intrusion Detection Model Based on Deep Learning and Multi-Layer Perceptron in the Internet of Things (IoT) Network. In International Conference on Advanced Machine Learning Technologies and Applications; Springer: Berlin/Heidelberg, Germany, 2022; pp. 34–46.
  48. Uhricek, D.; Hynek, K.; Cejka, T.; Kolar, D. BOTA: Explainable IoT Malware Detection in Large Networks; IEEE: New York, NY, USA, 2022; p. 1.
  49. Madhu, B.; Chari, M.V.G.; Vankdothu, R.; Silivery, A.K.; Aerranagula, V. Intrusion detection models for IOT networks via deep learning approaches. Meas. Sens. 2022, 100641.
  50. Al-Shareeda, M.A.; Manickam, S.; Saare, M.A. DDoS attacks detection using machine learning and deep learning techniques: Analysis and comparison. Bull. Electr. Eng. Inform. 2023, 12, 930–939.
  51. Gopal, S.B.; Poongodi, C.; Nanthiya, D.; Kirubakaran, T.; Logeshwar, D.; Saravanan, B.K. Autoencoder based Architecture for Mitigating Phishing URL attack in the Internet of Things (IoT) Using Deep Neural Networks. In 2022 6th International Conference on Devices, Circuits and Systems (ICDCS); IEEE: New York, NY, USA, 2022; pp. 427–431.
  52. Bhattacharya, S.; Ghorai, S.; Khan, A.K. Systematic Study of Detection Mechanism for Network Intrusion in Cloud, Fog, and Internet of Things Using Deep Learning. In Human-Centric Smart Computing; Springer: Berlin/Heidelberg, Germany, 2023; pp. 31–43.
  53. Saheed, Y.K.; Baba, U.A.; Orje-Ishegh, T.; Longe, O.B. An Efficient Machine Learning and Deep Belief Network Models for Wireless Intrusion Detection System. 2022.
  54. Hoque, K., Hossain, M. B., Sami, A., Das, D., Kadir, A., & Rahman, M. A. (2024). Technological trends in 5G networks for IoT-enabled smart healthcare: A review. International Journal of Science and Research Archive, 12(2), 1399-1410.
  55. Bharati, Subrato, et al. "Comparative performance analysis of different classification algorithm for the purpose of prediction of lung cancer." Intelligent Systems Design and Applications: 18th International Conference on Intelligent Systems Design and Applications (ISDA 2018) held in Vellore, India, December 6-8, 2018, Volume 2. Springer International Publishing, 2020.
  56. Javed Mehedi Shamrat, F. M., Tasnim, Z., Chowdhury, T. R., Shema, R., Uddin, M. S., & Sultana, Z. (2022). Multiple cascading algorithms to evaluate performance of face detection. In Pervasive Computing and Social Networking: Proceedings of ICPCSN 2021 (pp. 89-102). Springer Singapore.
  57. Javed Mehedi Shamrat, F. M., Ghosh, P., Tasnim, Z., Khan, A. A., Uddin, M. S., & Chowdhury, T. R. (2022). Human Face recognition using eigenface, SURF method. In Pervasive Computing and Social Networking: Proceedings of ICPCSN 2021 (pp. 73-88). Springer Singapore.
  58. Kowsher, M., Tahabilder, A., Sanjid, M. Z. I., Prottasha, N. J., Uddin, M. S., Hossain, M. A., & Jilani, M. A. K. (2021). LSTM-ANN & BiLSTM-ANN: Hybrid deep learning models for enhanced classification accuracy. Procedia Computer Science, 193, 131-140.
  59. Hoque, R., Maniruzzaman, M., Michael, D. L., & Hoque, M. (2024). Empowering blockchain with SmartNIC: Enhancing performance, security, and scalability. World Journal of Advanced Research and Reviews, 22(1), 151-162.
  60. Hoque, R., Billah, M., Debnath, A., Hossain, S. S., & Sharif, N. B. (2024). Heart Disease Prediction using SVM. International Journal of Science and Research Archive, 11(2), 412-420.
  61. Mohanta, B. K., Jena, D., Satapathy, U., & Patnaik, S. (2020). Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology. Internet of Things, 11, 100227.
  62. Chowdhury, I. K., & Yu, W. B. (2024). Information extraction from the reviews of AI applications using SAS text Mining Process. Issues in Information Systems, 25(4), 127-135.
Index Terms

Computer Science
Information Sciences

Keywords

Internet of things; cyberattacks; anomalies; deep learning; machine learning; security; data security; network security

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