| 
 Proceedings of International Conference on Applied Innovation in IT
 2025/07/26, Volume 13, Issue 3, pp.149-156
 
 An Efficient Intrusion Detection System for IoT Using Feature Engineering and Machine Learning
 Basima kzar Hassan, Khawla Rashige Hassen and Sakna Jahiya FarajAbstract: IoT devices have limited computing and security capabilities, making them vulnerable to cyberattacks. This rapid expansion of IoT has introduced unprecedented connectivity but also heightened security vulnerabilities. The security of IoT environments, therefore, depends on efficient and lightweight intrusion detection systems (IDS). Using advanced feature engineering and machine learning algorithms, this study develops high-performance IDS designed for IoT networks. Data imbalance is addressed with preprocessing techniques, feature extraction, and synthetic minority oversampling techniques (SMOTE). Multi-dataset training and testing included K-nearest neighbour models, sequential minimalism optimization models, random forest models, and stacking ensembles. A transfer learning model such as VGG-16 and DenseNet was also incorporated to improve classification accuracy. It has been demonstrated that the proposed models, particularly the ensemble and RF-based approaches, are highly accurate, precise, and recallable. IoT environments with limited resources can benefit from the proposed IDS framework because it effectively identifies malicious traffic while maintaining computational efficiency.
 
 Keywords: Intrusion Detection System (IDS), Internet of Things (IoT), Machine Learning, Feature Engineering, SMOTE.
 
 DOI: Under Indexing
 
 Download: PDF
 
 
 References:
A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, "Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications," IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2347–2376, 2015, [Online]. Available: https://doi.org/10.1109/COMST.2015.2444095.
B. Bhola et al., "Quality-enabled decentralized dynamic IoT platform with scalable resources integration," IET Communications, 2022.
W. Zhou, Y. Jia, A. Peng, Y. Zhang, and P. Liu, "The Effect of IoT New Features on Security and Privacy: New Threats, Existing Solutions, and Challenges Yet to Be Solved," IEEE Internet of Things Journal, vol. 6, no. 2, pp. 1606–1616, Apr. 2019, [Online]. Available: https://doi.org/10.1109/JIOT.2018.2847733.
N. Chaabouni, M. Mosbah, A. Zemmari, C. Sauvignac, and P. Faruki, "Network Intrusion Detection for IoT Security Based on Learning Techniques," IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2671–2701, 2019, [Online]. Available: https://doi.org/10.1109/COMST.2019.2896380.
P. Mishra, V. Varadharajan, U. Tupakula, and E. S. Pilli, "A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection," IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 686–728, 2019, [Online]. Available: https://doi.org/10.1109/COMST.2018.2847722.
P. Rani and R. Sharma, "Intelligent transportation system for internet of vehicles based vehicular networks for smart cities," Computers & Electrical Engineering, vol. 105, p. 108543, 2023.
B. A. Tama, M. Comuzzi, and K.-H. Rhee, "TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System," IEEE Access, vol. 7, pp. 94497–94507, 2019, [Online]. Available: https://doi.org/10.1109/ACCESS.2019.2928048.
M. A. Hall, "Correlation-based feature selection for machine learning," Ph.D. dissertation, The University of Waikato, 1999, [Online]. Available: https://researchcommons.waikato.ac.nz/bitstream/10289/15043/1/thesis.pdf. [Accessed: Apr. 25, 2025].
B. Yan and G. Han, "Effective Feature Extraction via Stacked Sparse Autoencoder to Improve Intrusion Detection System," IEEE Access, vol. 6, pp. 41238–41248, 2018, [Online]. Available: https://doi.org/10.1109/ACCESS.2018.2858277.
N. Hussain, P. Rani, H. Chouhan, and U. S. Gaur, "Cyber security and privacy of connected and automated vehicles (CAVs)-based federated learning: challenges, opportunities, and open issues," Federated Learning in IoT Applications, pp. 169–183, 2022.
P. Rani and R. Sharma, "Intelligent Transportation System Performance Analysis of Indoor and Outdoor Internet of Vehicle (IoV) Applications Towards 5G," Tsinghua Science and Technology, vol. 29, no. 6, pp. 1785–1795, Dec. 2024, [Online]. Available: https://doi.org/10.26599/TST.2023.9010119.
A. M. Mhlaba and M. Masinde, "An Integrated Internet of Things Based System for Tracking and Monitoring Assets–the case of the Central University of Technology," in IST-Africa 2015 Conference Proceedings, 2015, [Online]. Available: https://www.academia.edu/download/66891365/An_Integrated_Internet_of_Things_Based_S20210504-29331-1occ0bs.pdf. [Accessed: Apr. 25, 2025].
F. T. First and I. B. Blocks, "How the Internet of Things Is Revolutionizing Healthcare," [Online]. Available: http://euro.ecom.cmu.edu/resources/elibrary/ubi/IOTREVHEALCARWP.pdf. [Accessed: Apr. 25, 2025].
M. Masinde, "IoT applications that work for the African continent: Innovation or adoption?," in 2014 12th IEEE International Conference on Industrial Informatics (INDIN), IEEE, 2014, pp. 633–638, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/6945587/. [Accessed: Apr. 25, 2025].
R. Mahmoud, T. Yousuf, F. Aloul, and I. Zualkernan, "Internet of things (IoT) security: Current status, challenges and prospective measures," in 2015 10th International Conference for Internet Technology and Secured Transactions (ICITST), IEEE, 2015, pp. 336–341, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7412116/. [Accessed: Apr. 25, 2025].
N. Hussain, P. Rani, N. Kumar, and M. G. Chaudhary, "A deep comprehensive research architecture, characteristics, challenges, issues, and benefits of routing protocol for vehicular ad-hoc networks," International Journal of Distributed Systems and Technologies (IJDST), vol. 13, no. 8, pp. 1–23, 2022.
X. Xingmei, Z. Jing, and W. He, "Research on the basic characteristics, the key technologies, the network architecture and security problems of the internet of things," in Proceedings of 2013 3rd International Conference on Computer Science and Network Technology, IEEE, 2013, pp. 825–828, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/6967233/. [Accessed: Apr. 25, 2025].
A. Andalib and V. Tabataba Vakili, "A Novel Dimension Reduction Scheme for Intrusion Detection Systems in IoT Environments," arXiv e-Prints, p. arXiv-2007, 2020.
P. Rani et al., "Federated Learning-Based Misbehavior Detection for the 5G-Enabled Internet of Vehicles," IEEE Transactions on Consumer Electronics, vol. 70, no. 2, pp. 4656–4664, May 2024, [Online]. Available: https://doi.org/10.1109/TCE.2023.3328020.
R. Sommer and V. Paxson, "Outside the closed world: On using machine learning for network intrusion detection," in 2010 IEEE Symposium on Security and Privacy, IEEE, 2010, pp. 305–316, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/5504793/. [Accessed: Apr. 25, 2025].
A.-H. Qureshi, H. Larijani, J. Ahmad, and N. Mtetwa, "A Heuristic Intrusion Detection System for Internet-of-Things (IoT)," in K. Arai et al. (eds), Intelligent Computing, in Advances in Intelligent Systems and Computing, vol. 997, Cham: Springer International Publishing, 2019, pp. 86–98, [Online]. Available: https://doi.org/10.1007/978-3-030-22871-2_7.
K. Jiang, W. Wang, A. Wang, and H. Wu, "Network intrusion detection combined hybrid sampling with deep hierarchical network," IEEE Access, vol. 8, pp. 32464–32476, 2020.
A. Dushimimana, T. Tao, R. Kindong, and A. Nishyirimbere, "Bi-directional recurrent neural network for intrusion detection system (IDS) in the internet of things (IoT)," International Journal of Advanced Engineering Research and Science, vol. 7, no. 3, 2020, [Online]. Available: https://www.academia.edu/download/113495300/68IJAERS-03202047-Bi-directional.pdf. [Accessed: Apr. 25, 2025].
G. Ansari, P. Rani, and V. Kumar, "A novel technique of mixed gas identification based on the group method of data handling (GMDH) on time-dependent MOX gas sensor data," in Proceedings of International Conference on Recent Trends in Computing: ICRTC 2022, Springer, 2023, pp. 641–654.
F. Farhin, M. S. Kaiser, and M. Mahmud, "Secured Smart Healthcare System: Blockchain and Bayesian Inference Based Approach," in M. S. Kaiser et al. (eds), Proceedings of International Conference on Trends in Computational and Cognitive Engineering, in Advances in Intelligent Systems and Computing, vol. 1309, Singapore: Springer Singapore, 2021, pp. 455–465, [Online]. Available: https://doi.org/10.1007/978-981-33-4673-4_36.
N. Islam, I. Sultana, and M. S. Rahman, "HKMS-AMI: A Hybrid Key Management Scheme for AMI Secure Communication," in M. S. Kaiser et al. (eds), Proceedings of International Conference on Trends in Computational and Cognitive Engineering, in Advances in Intelligent Systems and Computing, vol. 1309, Singapore: Springer Singapore, 2021, pp. 383–392, [Online]. Available: https://doi.org/10.1007/978-981-33-4673-4_30.
K. He, X. Zhang, S. Ren, and J. Sun, "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification," in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 1026–1034, [Online]. Available: http://openaccess.thecvf.com/content_iccv_2015/html/He_Delving_Deep_into_ICCV_2015_paper.html. [Accessed: Apr. 19, 2025].
C. Szegedy et al., "Going deeper with convolutions," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9, [Online]. Available: https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Szegedy_Going_Deeper_With_2015_CVPR_paper.html. [Accessed: Apr. 19, 2025].
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778, [Online]. Available: http://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html. [Accessed: Apr. 19, 2025].
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4700–4708, [Online]. Available: http://openaccess.thecvf.com/content_cvpr_2017/html/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.html. [Accessed: Apr. 19, 2025].
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, "Mobilenetv2: Inverted residuals and linear bottlenecks," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 4510–4520, [Online]. Available: http://openaccess.thecvf.com/content_cvpr_2018/html/Sandler_MobileNetV2_Inverted_Residuals_CVPR_2018_paper.html. [Accessed: Apr. 19, 2025].
M. Tan et al., "Mnasnet: Platform-aware neural architecture search for mobile," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 2820–2828, [Online]. Available: http://openaccess.thecvf.com/content_CVPR_2019/html/Tan_MnasNet_Platform-Aware_Neural_Architecture_Search_for_Mobile_CVPR_2019_paper. [Accessed: Apr. 19, 2025].
S. Pal, "Transfer learning and fine tuning for cross domain image classification with Keras," GitHub: Transfer Learning Fine Tuning Cross Domain Image Classification with Keras, 2016.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2009, pp. 248–255, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/5206848/. [Accessed: Apr. 20, 2025].
K. Gopalakrishnan, S. K. Khaitan, A. Choudhary, and A. Agrawal, "Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection," Construction and Building Materials, vol. 157, pp. 322–330, 2017.
N. Subramanian, O. Elharrouss, S. Al-Maadeed, and M. Chowdhury, "A review of deep learning-based detection methods for COVID-19," Computers in Biology and Medicine, vol. 143, p. 105233, 2022.
D. M. W. Powers, “Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation,” J. Mach. Learn. Technol., vol. 2, pp. 37–63, 2011, doi: 10.9735/2229-3981.
 |  
	
		|  
  
			    HOME 
			- Conference - Journal
 - Paper Submission to Journal
 - Paper Submission to Conference
 - For Authors
 - For Reviewers
 - Important Dates
 - Conference Committee
 - Editorial Board
 - Reviewers
 - Last Proceedings
 
			-
            Volume 13, Issue 3 (ICAIIT 2025) PROCEEDINGS
 -
            Volume 13, Issue 2 (ICAIIT 2025)
 -
            Volume 13, Issue 1 (ICAIIT 2025)
 -
            Volume 12, Issue 2 (ICAIIT 2024)
 -
            Volume 12, Issue 1 (ICAIIT 2024)
 -
            Volume 11, Issue 2 (ICAIIT 2023)
 -
            Volume 11, Issue 1 (ICAIIT 2023)
 -
            Volume 10, Issue 1 (ICAIIT 2022)
 -
            Volume 9, Issue 1 (ICAIIT 2021)
 -
            Volume 8, Issue 1 (ICAIIT 2020)
 -
            Volume 7, Issue 1 (ICAIIT 2019)
 -
            Volume 7, Issue 2 (ICAIIT 2019)
 -
            Volume 6, Issue 1 (ICAIIT 2018)
 -
            Volume 5, Issue 1 (ICAIIT 2017)
 -
            Volume 4, Issue 1 (ICAIIT 2016)
 -
			Volume 3, Issue 1 (ICAIIT 2015)
 -
			Volume 2, Issue 1 (ICAIIT 2014)
 -
			Volume 1, Issue 1 (ICAIIT 2013)
 
			ICAIIT 2025 PAST CONFERENCES
 -
			Photos
 -
			Reports
 
 ICAIIT 2024
 -
			Photos
 -
			Reports
 
 ICAIIT 2023
 -
			Photos
 -
			Reports
 
 ICAIIT 2021
 -
			Photos
 -
			Reports
 
 ICAIIT 2020
 -
			Photos
 -
			Reports
 
 ICAIIT 2019
 -
			Photos
 -
			Reports
 
 ICAIIT 2018
 -
			Photos
 -
			Reports
   
			    ETHICS IN PUBLICATIONS
 ACCOMODATION
   
			    CONTACT US   |  |