Biomedical Engineering Department, College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq and Computer Science Department, College of Computer Science and Information Technology, University of Kerbala, Karbala, Iraq.
The Internet of Things (IoT) has proliferated, transitioning from modest home automation to encompass sectors like healthcare, agriculture, transportation, and manufacturing. This evolution is characterized by devices' ability to autonomously gather, disseminate, and analyze data, leading to improved real-time decision-making, predictive insights, and customized user experiences. The ubiquity of IoT, while promising, introduces significant data security concerns. The vast number of interlinked devices and diverse and often insufficient security features make them vulnerable to cyber threats, emphasizing the need for robust security mechanisms. Intrusion Detection Systems (IDS) have traditionally acted as vital guards against such threats; however, with the ever-increasing data in the IoT, traditional IDS models, such as Naive Bayes, face processing speed and accuracy challenges. This paper introduces a novel model, "FE+NB," which merges advanced Feature Extraction (FE) with the Naive Bayes (NB) classifier. Central to this model is the "Temporal-Structural Synthesis" technique tailored for IoT traffic data, focusing on data compression, temporal and structural analyses, and Feature Selection (FS) using mutual information. Consequently, the model enhances efficiency and accuracy in Intrusion Detection (ID) in complex IoT networks.
Keywords
Intrusion Detection, Feature Extraction, Naïve Bayes, Internet of Things, Accuracy.
M. K. Thota, F. H. Shajin, and P. Rajesh, “Survey on software defect prediction techniques,” International Journal of Applied Science and Engineering, vol. 17, no. 4, pp. 331–344, 2020.
Y. V. R. N. Pawan, K. B. Prakash, S. Chowdhury, and Y.-C. Hu, “Particle swarm optimization performance improvement using deep learning techniques,” Multimedia Tools and Applications, vol. 81, no. 19, pp. 27949–27968, Mar. 2022, doi: 10.1007/s11042-022-12966-1.
A. Appathurai, R. Sundarasekar, C. Raja, E. J. Alex, C. A. Palagan, and A. Nithya, “An Efficient Optimal Neural Network-Based Moving Vehicle Detection in Traffic Video Surveillance System,” Circuits, Systems, and Signal Processing, vol. 39, no. 2, pp. 734–756, Aug. 2019, doi: 10.1007/s00034-019-01224-9.
N. Yuvaraj, T. Karthikeyan, and K. Praghash, “An Improved Task Allocation Scheme in Serverless Computing Using Gray Wolf Optimization (GWO) Based Reinforcement Learning (RIL) Approach,” Wireless Personal Communications, vol. 117, no. 3, pp. 2403–2421, Nov. 2020, doi: 10.1007/s11277-020-07981-0.
S. Deshmukh, K. Thirupathi Rao, and M. Shabaz, “Collaborative Learning Based Straggler Prevention in Large-Scale Distributed Computing Framework,” Security and Communication Networks, vol. 2021, pp. 1–9, May 2021, doi: 10.1155/2021/8340925.
E. Rajesh Kumar, K. V. S. N. Rama Rao, S. R. Nayak, and R. Chandra, “Suicidal ideation prediction in twitter data using machine learning techniques,” Journal of Interdisciplinary Mathematics, vol. 23, no. 1, pp. 117–125, Jan. 2020, doi: 10.1080/09720502.2020.1721674.
S. Stalin et al., “A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach,” Mathematical Problems in Engineering, vol. 2021, pp. 1–11, Oct. 2021, doi: 10.1155/2021/2942808.
R. K et al., “A Robust And Accurate Video Watermarking System Based On SVD Hybridation For Performance Assessment,” International Journal of Engineering Trends and Technology, vol. 68, no. 7, pp. 19–24, Jul. 2020, doi: 10.14445/22315381/ijett-v68i7p204s.
R. Janarthanan, R. U. Maheshwari, P. K. Shukla, P. K. Shukla, S. Mirjalili, and M. Kumar, “Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems,” Energies, vol. 14, no. 20, p. 6584, Oct. 2021, doi: 10.3390/en14206584.
S. Sengan, P. Vidya Sagar, R. Ramesh, O. I. Khalaf, and R. Dhanapal, “The optimization of reconfigured real-time datasets for improving classification performance of machine learning algorithms,” Mathematics in Engineering, Science and Aerospace, vol. 12, no. 1, pp. 43–54, 2021.
D. Balamurugan, S. S. Aravinth, P. C. S. Reddy, A. Rupani, and A. Manikandan, “Multiview Objects Recognition Using Deep Learning-Based Wrap-CNN with Voting Scheme,” Neural Processing Letters, vol. 54, no. 3, pp. 1495–1521, Apr. 2022, doi: 10.1007/s11063-021-10679-4.
S. Kumar, A. Jain, S. Rani, H. Alshazly, S. Ahmed Idris, and S. Bourouis, “Deep Neural Network Based Vehicle Detection and Classification of Aerial Images,” Intelligent Automation & Soft Computing, vol. 34, no. 1, pp. 119–131, 2022, doi: 10.32604/iasc.2022.024812.
S. Rajasoundaran et al., “Secure routing with multi-watchdog construction using deep particle convolutional model for IoT based 5G wireless sensor networks,” Computer Communications, vol. 187, pp. 71–82, Apr. 2022, doi: 10.1016/j.comcom.2022.02.004.
N. Yuvaraj, K. Praghash, R. A. Raja, and T. Karthikeyan, “An Investigation of Garbage Disposal Electric Vehicles (GDEVs) Integrated with Deep Neural Networking (DNN) and Intelligent Transportation System (ITS) in Smart City Management System (SCMS),” Wireless Personal Communications, vol. 123, no. 2, pp. 1733–1752, Oct. 2021, doi: 10.1007/s11277-021-09210-8.
S. C. Dharmadhikari, V. Gampala, Ch. M. Rao, S. Khasim, S. Jain, and R. Bhaskaran, “A smart grid incorporated with ML and IoT for a secure management system,” Microprocessors and Microsystems, vol. 83, p. 103954, Jun. 2021, doi: 10.1016/j.micpro.2021.103954.
S. P. Jaiprakash, M. B. Desai, C. S. Prakash, V. H. Mistry, and K. L. Radadiya, “Low dimensional DCT and DWT feature based model for detection of image splicing and copy-move forgery,” Multimedia Tools and Applications, vol. 79, no. 39–40, pp. 29977–30005, Aug. 2020, doi: 10.1007/s11042-020-09415-2.
J. R. K. K. Dabbakuti, A. Jacob, V. R. Veeravalli, and R. K. Kallakunta, “Implementation of IoT analytics ionospheric forecasting system based on machine learning and ThingSpeak,” IET Radar, Sonar & Navigation, vol. 14, no. 2, pp. 341–347, Jan. 2020, doi: 10.1049/iet-rsn.2019.0394.
T. Chakravorti and P. Satyanarayana, “Non linear system identification using kernel based exponentially extended random vector functional link network,” Applied Soft Computing, vol. 89, p. 106117, Apr. 2020, doi: 10.1016/j.asoc.2020.106117.
A. Paul and S. P. Maity, “Machine Learning for Spectrum Information and Routing in Multihop Green Cognitive Radio Networks,” IEEE Transactions on Green Communications and Networking, vol. 6, no. 2, pp. 825–835, Jun. 2022, doi: 10.1109/tgcn.2021.3127308.
M. Sathya et al., “A Novel, Efficient, and Secure Anomaly Detection Technique Using DWU-ODBN for IoT-Enabled Multimedia Communication Systems,” Wireless Communications and Mobile Computing, vol. 2021, pp. 1–12, Dec. 2021, doi: 10.1155/2021/4989410.
S. D. M. Achanta, T. Karthikeyan, and R. V. Kanna, “Wearable sensor based acoustic gait analysis using phase transition-based optimization algorithm on IoT,” International Journal of Speech Technology, Sep. 2021, doi: 10.1007/s10772-021-09893-1.
Dr. B. Singh, P. Kavitha, R. Regin, Dr. K. Praghash, S. Sujatha, and Dr. S. S. Rajest, “Optimized Node Clustering based on Received Signal Strength with Particle Ordered-filter Routing Used in VANET,” Webology, vol. 17, no. 2, pp. 262–277, Dec. 2020, doi: 10.14704/web/v17i2/web17029.
G. Rekha, S. Malik, A. K. Tyagi, and M. M. Nair, “Intrusion Detection in Cyber Security: Role of Machine Learning and Data Mining in Cyber Security,” Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 72–81, 2020, doi: 10.25046/aj050310.
K. N. Reddy and P. Bojja, “A new hybrid optimization method combining moth–flame optimization and teaching–learning-based optimization algorithms for visual tracking,” Soft Computing, vol. 24, no. 24, pp. 18321–18347, May 2020, doi: 10.1007/s00500-020-05032-1.
C. Banchhor and N. Srinivasu, “FCNB: Fuzzy Correlative Naive Bayes Classifier with MapReduce Framework for Big Data Classification,” Journal of Intelligent Systems, vol. 29, no. 1, pp. 994–1006, Oct. 2018, doi: 10.1515/jisys-2018-0020.
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Sivaprakash S
Sivaprakash S
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
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Cite this article
Juan Carlos Juarez Vargas, Hayder M. A. Ghanimi, Sivaprakash S, Amarendra M, Rajendiran M and Sheylla L Cotrado Lupo, “Intrusion Detection in Internet of Things Systems: A Feature Extraction with Naive Bayes Classifier Approach”, Journal of Machine and Computing, pp. 021-030, January 2024. doi: 10.53759/7669/jmc202404003.
