Exploring Artificial Intelligence and Data Science-Based Security and its Scope in IoT Use Cases
DOI:
https://doi.org/10.22399/ijcesen.869Keywords:
Internet of Things (IoT) security, Intrusion detection systems (IDS), Multi-Layer Perceptron (MLP), Deep Learning Framework, CybersecurityAbstract
The fast growth of IO networks has resulted in a security crisis besides the development of decentralized-based innovations, and such decentralized bases or technologies also made challenges in terms of speed, performance, and scalability. Traditional machine learning-based intrusion detection systems (IDS) are unable to manage the intricate and non-linear correlations seen in massive amounts of IoT data. They produce relatively low detection rates, especially in multi-class classification, where many attack types must be addressed. Overcoming these hurdles calls for frameworks: innovative enough to accommodate the challenge whilst using the wealth of data produced by IoT devices. Abstract In this paper, we introduce a unique MLP-based deep learning architecture for intrusion detection in IoT settings. This framework includes a preprocessing pipeline that optimally normalizes and applies one-hot-encoding to the data to prepare it optimally for classification. We tested the algorithms on the UNSW-NB15 dataset, commonly used for IDS. Mere quantitative results show that MLP surpasses classical models like Logistic Regression, SVM, and Random Forests, giving a precision of 97.53%, recall of 97.23%, and accuracy of 97.73% on the multi-class classification task. This framework is undoubtedly scalable and provides a sufficient security mechanism for the whole IoT ecosystem; hence, it can be used in various actual use cases. This performance shows that it could solve the new threats developing in IoT environments.
References
Ahmed, Ejaz; Yaqoob, Ibrar; Hashem, Ibrahim Abaker Targio; Khan, Imran; Ahmed, Abdelmuttlib Ibrahim Abdalla; Imran, Muhammad and Vasilakos, Athanasios V. (2017). The role of big data analytics in the Internet of Things. Computer Networks. doi:10.1016/j.comnet.2017.06.013
Banerjee, Amit. (2020). Emerging trends in IoT and big data analytics for biomedical and health care technologies. Handbook of Data Science Approaches for Biomedical Engineering. 121–152. doi:10.1016/B978-0-12-818318-2.00005-2 DOI: https://doi.org/10.1016/B978-0-12-818318-2.00005-2
Adi, Erwin; Anwar, Adnan; Baig, Zubair; Zeadally, Sherali. (2020). Machine learning and data analytics for the IoT. Neural Computing and Applications. pp.14A3RE. doi:10.1007/s00521-020-04874-y DOI: https://doi.org/10.1007/s00521-020-04874-y
Tien, James M. (2017). Internet of Things, Real-Time Decision Making, and Artificial Intelligence. Annals of Data Science. 4(2):149–178. doi:10.1007/s40745-017-0112-5 DOI: https://doi.org/10.1007/s40745-017-0112-5
Ur Rehman, Muhammad Habib; Yaqoob, Ibrar; Salah, Khaled; Imran, Muhammad; Jayaraman, Prem Prakash; Perera, Charith. (2019). The role of big data analytics in industrial Internet of Things. Future Generation Computer Systems. 1-40. doi:10.1016/j.future.2019.04.020 DOI: https://doi.org/10.1016/j.future.2019.04.020
Ahmed, Ejaz; Yaqoob, Ibrar; Hashem, Ibrahim Abaker Targio; Khan, Imran; Ahmed, Abdelmuttlib Ibrahim Abdalla; Imran, Muhammad; Vasilakos, Athanasios V. (2017). The role of big data analytics in Internet of Things. Computer Networks. 1-22. 12doi:10.1016/j.comnet.2017.06.013 DOI: https://doi.org/10.1016/j.comnet.2017.06.013
Ghosh, Ashish; Chakraborty, Debasrita; Law, Anwesha. (2018). Artificial Intelligence in Internet of Things. CAAI Transactions on Intelligence Technology. 1-11. doi:10.1049/trit.2018.1008 DOI: https://doi.org/10.1049/trit.2018.1008
Atitallah, Safa Ben; Driss, Maha; Boulila, Wadii; Ghaczala, Henda Ben. (2020). Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions. Computer Science Review. 38:1–29. doi:10.1016/j.cosrev.2020.100303 DOI: https://doi.org/10.1016/j.cosrev.2020.100303
Sarker, Iqbal H.; Hoque, Mohammed Moshiul; Uddin, Md. Kafil; Alsanoosy, Tawfeeq. (2020). Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions. Mobile Networks and Applications. 1–19. doi:10.1007/s11036-020-01650-z DOI: https://doi.org/10.1007/s11036-020-01650-z
Efpraxia D. Zamani, Conn Smyth, Samrat Gupta, Denis Dennehy. (2023). Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review. Annals of Operations Research. 327:605–632. https://doi.org/10.1007/s10479-022-04983-y DOI: https://doi.org/10.1007/s10479-022-04983-y
Gupta, Rajesh; Tanwar, Sudeep; Tyagi, Sudhanshu; Kumar, Neeraj. (2020). Machine Learning Models for Secure Data Analytics: A taxonomy and threat model. Computer Communications. 1–36. doi:10.1016/j.comcom.2020.02.008 DOI: https://doi.org/10.1016/j.comcom.2020.02.008
Iqbal, Rahat; Doctor, Faiyaz; More, Brian; Mahmud, Shahid; Yousuf, Usman. (2018). Big data analytics: Computational intelligence techniques and application areas. Technological Forecasting and Social Change. 1–13. doi:10.1016/j.techfore.2018.03.024 DOI: https://doi.org/10.1016/j.techfore.2018.03.024
Elijah, Olakunle; Rahman, Tharek Abdul; Orikumhi, Igbafe; Leow, Chee Yen; Hindia, MHD Nour. (2018). An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges. IEEE Internet of Things Journal. 1–17. doi:10.1109/JIOT.2018.2844296 DOI: https://doi.org/10.1109/JIOT.2018.2844296
Valentin Kuleto, Milena Ili´c, Mihail Dumangiu, Marko Rankovi´c, O. (2021). Exploring Opportunities and Challenges of Artificial Intelligence and Machine Learning in Higher Education Institutions. MDPI. 13(18):1-16. https://doi.org/10.3390/su131810424 DOI: https://doi.org/10.3390/su131810424
Amira Bourechak, Ouarda Zedadra, Mohamed Nadjib Kouahla and Antonio Guer. (2023). At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives. MDPI. 1-49. DOI: https://doi.org/10.3390/s23031639
Jie Chen; L. Ramanathan; Mamoun Alazab. (2021). Holistic big data integrated artificial intelligent modeling to improve privacy and security in data management of smart cities. Microprocessors and Microsystems. 1–17. doi:10.1016/j.micpro.2020.103722 DOI: https://doi.org/10.1016/j.micpro.2020.103722
Alrowaily, Mohammed; Lu, Zhuo. (2018). Secure Edge Computing in IoT Systems: Review and Case Studies. IEEE. 440–444. doi:10.1109/SEC.2018.00060 DOI: https://doi.org/10.1109/SEC.2018.00060
Saumyaranjan Sahoo. (2021). Big data analytics in manufacturing: a bibliometric analysis of research in the field of business management. International Journal of Production Research. 1–30. doi:10.1080/00207543.2021.1919333 DOI: https://doi.org/10.1080/00207543.2021.1919333
Gill, Sukhpal Singh; Tuli, Shreshth; Xu, Minxian; Singh, Inderpreet; Singh, Karan Vijay; Lindsay, Dominic; Tuli, Shikhar; Smirnova, Daria; Singh, Manmeet; Jain, Udit; Pervaiz, Haris; Sehgal, Bhanu; Kaila, Sukhwinder Singh; Mishra, Sanjay; Aslanpour, Mohammad Sadegh; Mehta, Harshit; Stankovski, Vlado; Garraghan, Peter. (2019). Transformative Effects of IoT, Blockchain and Artificial Intelligence on Cloud Computing: Evolution, Vision, Trends and Open Challenges. Internet of Things. 1-–33. doi:10.1016/j.iot.2019.100118 DOI: https://doi.org/10.1016/j.iot.2019.100118
Berk Kaan Kuguoglu, Haiko van der Voort and Marijn Janssen. (2021). The Giant Leap for Smart Cities: Scaling Up Smart City Artificial Intelligence of Things (AIoT) Initiatives. MDPI. 13(21):1-16. https://doi.org/10.3390/su132112295 DOI: https://doi.org/10.3390/su132112295
Abderahman Rejeb, Karim Rejeb, Horst Treiblmaier, Andrea Appolloni. (2023). The Internet of Things (IoT) in healthcare: Taking stock and moving forward. Internet of Things. 22:1-23. https://doi.org/10.1016/j.iot.2023.100721 DOI: https://doi.org/10.1016/j.iot.2023.100721
Jitendra Bhatia, Kiran Italiya, Kuldeepsinh Jadeja, Malaram Kumhar. (2023). An Overview of Fog Data Analytics for IoT Applications. MDPI. 23(1):1-31. https://doi.org/10.3390/s23010199 DOI: https://doi.org/10.3390/s23010199
Supriya M. and Vijay Kumar Chattu. (2021). A Review of Artificial Intelligence, Big Data, and Blockchain Technology Applications in Medicine and Global Health. MDPI. 5(3):1-20. https://doi.org/10.3390/bdcc5030041 DOI: https://doi.org/10.3390/bdcc5030041
Mishra, Sushruta (2020). Analysis of the role and scope of big data analytics with IoT in health care domain. Handbook of Data Science Approaches for Biomedical Engineering. 1–23. doi:10.1016/B978-0-12-818318-2.00001-5 DOI: https://doi.org/10.1016/B978-0-12-818318-2.00001-5
Tanwar, Sudeep; Bhatia, Qasim; Patel, Pruthvi; Kumari, Aparna; Singh, Pradeep Kumar; Hong, Wei-Chiang. (2020). Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward. IEEE Access. 8:474–488. doi:10.1109/access.2019.2961372 DOI: https://doi.org/10.1109/ACCESS.2019.2961372
Zhang, J. Z., Srivastava, P. R., Sharma, D., and Eachempati, P. (2021). Big data analytics and machine learning: A retrospective overview and bibliometric analysis. Expert Systems with Applications. 184:115561. doi:10.1016/j.eswa.2021.115561 DOI: https://doi.org/10.1016/j.eswa.2021.115561
Antonio João Gonçalves de Azambuja, Christian Plesker, Klaus Schützer. (2023). Artificial Intelligence-Based Cyber Security in the Context of Industry 4.0—A Survey. MDPI. 12(8):1-18. https://doi.org/10.3390/electronics12081920 DOI: https://doi.org/10.3390/electronics12081920
Mariani, Marcello M.; Fosso Wamba, Samuel (2020). Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies. Journal of Business Research. 121:338–352. doi:10.1016/j.jbusres.2020.09.012 DOI: https://doi.org/10.1016/j.jbusres.2020.09.012
Frederick J. Riggins and Samuel Fosso Wamba. (2015). Research Directions on the Adoption, Usage, and Impact of the Internet of Things through the Use of Big Data Analytics. Hawaii International Conference on System Sciences. 1-10. DOI: 10.1109/HICSS.2015.186 DOI: https://doi.org/10.1109/HICSS.2015.186
Shah, Syed Attique; Seker, Dursun Zafer; Hameed, Sufian; Draheim, Dirk. (2019). The Rising Role of Big Data Analytics and IoT in Disaster Management: Recent Advances, Taxonomy and Prospects. IEEE Access. 1–1. doi:10.1109/ACCESS.2019.2913340 DOI: https://doi.org/10.1109/ACCESS.2019.2913340
Misra, N. N.; Dixit, Yash; Al-Mallahi, Ahmad; Bhullar, Manreet Singh; Upadhyay, Rohit; Martynenko, Alex. (2020). IoT, big data and artificial intelligence in agriculture and food industry. IEEE Internet of Things Journal. 1–19. doi:10.1109/JIOT.2020.2998584 DOI: https://doi.org/10.1109/JIOT.2020.2998584
Bag, Surajit; Pretorius, Jan Ham Christiaan; Gupta, Shivam; Dwivedi, Yogesh K. (2020). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change. 1–14. doi:10.1016/j.techfore.2020.120420 DOI: https://doi.org/10.1016/j.techfore.2020.120420
Zaidan, A. A.; Zaidan, B. B. (2018). A review on intelligent process for smart home applications based on IoT: coherent taxonomy, motivation, open challenges, and recommendations. Artificial Intelligence Review. 1–24. doi:10.1007/s10462-018-9648-9 DOI: https://doi.org/10.1007/s10462-018-9648-9
Anawar, Muhammad Rizwan; Wang, Shangguang; Azam Zia, Muhammad; Jadoon, Ahmer Khan; Akram, Umair; Raza, Salman. (2018). Fog Computing: An Overview of Big IoT Data Analytics. Wireless Communications and Mobile Computing. 1–22. doi:10.1155/2018/7157192 DOI: https://doi.org/10.1155/2018/7157192
Singh, Saurabh; Sharma, Pradip Kumar; Yoon, Byungun; Shojafar, Mohammad; Cho, Gi Hwan; Ra, In-Ho. (2020). Convergence of Blockchain and Artificial Intelligence in IoT Network for the Sustainable Smart City. Sustainable Cities and Society. 1–23. doi:10.1016/j.scs.2020.102364 DOI: https://doi.org/10.1016/j.scs.2020.102364
Chhabra, Gurpal Singh; Singh, Varinder Pal; Singh, Maninder. (2018). Cyber forensics framework for big data analytics in IoT environment using machine learning. Multimedia Tools and Applications. 1–20. doi:10.1007/s11042-018-6338-1 DOI: https://doi.org/10.1007/s11042-018-6338-1
Winter, Jenifer; Ono, Ryota. (2015). The Future Internet Algorithmic Discrimination: Big Data Analytics and the Future of the Internet. 10.1007/978-3-319-22994-2 (Chapter 8):125–140. doi:10.1007/978-3-319-22994-2_8 DOI: https://doi.org/10.1007/978-3-319-22994-2_8
Juan M. Górriz, et. al. (2020). Artificial intelligence within the interplay between natural and artificial Computation: advances in data science, trends and applications. Neurocomputing. 410:237-270. doi:10.1016/j.neucom.2020.05.078 DOI: https://doi.org/10.1016/j.neucom.2020.05.078
M. Bublitz, Frederico; Oetomo, Arlene; S. Sahu, Kirti; Kuang, Amethyst; X. Fadrique, Laura; E. Velmovitsky, Pedro; M. Nobrega, Raphael; P. Morita, Plinio. (2019). Disruptive Technologies for Environment and Health Research: An Overview of Artificial Intelligence, Blockchain, and Internet of Things. International Journal of Environmental Research and Public Health. 16(20):1–24. doi:10.3390/ijerph16203847 DOI: https://doi.org/10.3390/ijerph16203847
Ramalingam, Hariharan; Venkatesan, V.Prasanna. (2019). Conceptual analysis of Internet of Things use cases in Banking domain. Conference: TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). 34–2039. doi:10.1109/TENCON.2019.8929473 DOI: https://doi.org/10.1109/TENCON.2019.8929473
Smail Benzidia;Naouel Makaoui;Omar Bentahar; (2021). The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technological Forecasting and Social Change. 1–13. doi:10.1016/j.techfore.2020.120557 DOI: https://doi.org/10.1016/j.techfore.2020.120557
Iqbal H. Sarker. (2022). AI Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems. SN Computer Science. 3(158):1-20. https://doi.org/10.1007/s42979-022-01043-x DOI: https://doi.org/10.1007/s42979-022-01043-x
Zakria Qadira, Khoa N. Lea , Nasir Saeedb , Hafiz Suliman Munawar. (2023). Towards 6G Internet of Things: Recent advances, use cases, and open challenges. ICT Express. 9(2):296-312. DOI:10.1016/j.icte.2022.06.006 DOI: https://doi.org/10.1016/j.icte.2022.06.006
Yuxin Li, Jizheng Yi1, Huanyu Chen1 and Duanxiang Peng. (2021). Theory and application of artificial intelligence in financial industry. Data Science in Finance and Economics.1(2):96-116. DOI:10.3934/DSFE.2021006 DOI: https://doi.org/10.3934/DSFE.2021006
Paul, Anand; Ahmad, Awais; Rathore, M. Mazhar; Jabbar, Sohail. (2016). Smartbuddy: defining human behaviors using big data analytics in social internet of things. IEEE Wireless Communications. 23(5):68–74. doi:10.1109/MWC.2016.7721744 DOI: https://doi.org/10.1109/MWC.2016.7721744
Kakatkar, Chinmay; Bilgram, Volker; Füller, Johann (2019). Innovation analytics: Leveraging artificial intelligence in the innovation process. Business Horizons. 1–11. doi:10.1016/j.bushor.2019.10.006 DOI: https://doi.org/10.2139/ssrn.3293533
Mahdavinejad, Mohammad Saeid; Rezvan, Mohammadreza; Barekatain, Mohammadamin; Adibi, Peyman; Barnaghi, Payam; Sheth, Amit P. (2017). Machine learning for Internet of Things data analysis: A survey. Digital Communications and Networks. 1–57. doi:10.1016/j.dcan.2017.10.002 DOI: https://doi.org/10.1016/j.dcan.2017.10.002
Ravesa Akhter;Shabir Ahmad Sofi. (2021). Precision agriculture using IoT data analytics and machine learning. Journal of King Saud University - Computer and Information Sciences. 1–39. doi:10.1016/j.jksuci.2021.05.013 DOI: https://doi.org/10.1016/j.jksuci.2021.05.013
lv, zhihan; Song, Houbing; Basanta-Val, Pablo; Steed, Anthony; Jo, Minho (2017). Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics. IEEE Transactions on Industrial Informatics. 1–9. doi:10.1109/TII.2017.2650204 DOI: https://doi.org/10.1109/TII.2017.2650204
Goel, Pankaj; Datta, Aniruddha; Mannan, M. Sam (2017). Application of big data analytics in process safety and risk management. 2017 IEEE International Conference on Big Data (Big Data). 1143–1152. doi:10.1109/BigData.2017.8258040 DOI: https://doi.org/10.1109/BigData.2017.8258040
Moustafa, M., & Slay, J. (2015). UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems (UNSW-NB15). Harvard Dataverse. https://doi.org/10.7910/DVN/OGDUHB DOI: https://doi.org/10.1109/MilCIS.2015.7348942
D, jayasutha. (2024). Remote Monitoring and Early Detection of Labor Progress Using IoT-Enabled Smart Health Systems for Rural Healthcare Accessibility. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.672 DOI: https://doi.org/10.22399/ijcesen.672
Ponugoti Kalpana, L. Smitha, Dasari Madhavi, Shaik Abdul Nabi, G. Kalpana, & Kodati , S. (2024). A Smart Irrigation System Using the IoT and Advanced Machine Learning Model: A Systematic Literature Review. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.526 DOI: https://doi.org/10.22399/ijcesen.526
J. Anandraj. (2024). Transforming Education with Industry 6.0: A Human-Centric Approach . International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.732 DOI: https://doi.org/10.22399/ijcesen.732
N. Vidhya, & C. Meenakshi. (2025). Blockchain-Enabled Secure Data Aggregation Routing (BSDAR) Protocol for IoT-Integrated Next-Generation Sensor Networks for Enhanced Security. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.722 DOI: https://doi.org/10.22399/ijcesen.722
Alkhatib, A., Albdor , L., Fayyad, S., & Ali, H. (2024). Blockchain-Enhanced Multi-Factor Authentication for Securing IoT Children’s Toys: Securing IoT Children’s Toys. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.417 DOI: https://doi.org/10.22399/ijcesen.417
P. Jagdish Kumar, & S. Neduncheliyan. (2024). A novel optimized deep learning based intrusion detection framework for an IoT networks. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.597 DOI: https://doi.org/10.22399/ijcesen.597
Vutukuru, S. R., & Srinivasa Chakravarthi Lade. (2025). CoralMatrix: A Scalable and Robust Secure Framework for Enhancing IoT Cybersecurity. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.825 DOI: https://doi.org/10.22399/ijcesen.825
Iqbal, A., Shaima Qureshi, & Mohammad Ahsan Chishti. (2025). Bringing Context into IoT: Vision and Research Challenges. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.760 DOI: https://doi.org/10.22399/ijcesen.760
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
