Optimizing Autoencoder-Based Feature Selection for Attack Detection in IoT Networks via Machine Learning Approaches
(1) Dinamika Bangsa University, Jambi
(2) Dinamika Bangsa University, Jambi
(3) Dinamika Bangsa University, Jambi
(*) Corresponding Author
Abstract
The Internet of Things (IoT) presents significant security challenges as the number of connected devices continues to grow. One critical approach in developing efficient attack detection systems is the selection of relevant features to reduce model complexity without compromising accuracy. This study evaluates the effectiveness of Autoencoders as a feature reduction method for IoT network intrusion detection systems. Three machine learning algorithms are employed for comparative analysis: K-Nearest Neighbors (KNN), Naive Bayes (NB), and Support Vector Machine (SVM). The dataset is evaluated both before and after feature reduction using an Autoencoder, with performance assessed based on accuracy, precision, recall, F1-score, training time, and the number of features. Experimental results demonstrate that the Autoencoder can reduce the number of features by up to 30% without significantly degrading performance. In fact, the NB and SVM models exhibit improvements in both accuracy and training efficiency. The KNN model shows a minimal performance decline, which remains within acceptable limits. Overall, the Autoencoder proves to be an effective method for feature reduction, maintaining or even enhancing detection efficiency and performance. These findings support the use of Autoencoders as an efficient feature selection technique in IoT-based attack detection systems.
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S. Budiyanto, L. M. Silalahi, A. R. Hakim, A. Hamid, and D. Hanafi, “Vulnerability analysis on internet of things (IoT) networks using raspberry pi and open web application security project (OWASP),” Proceedings - 2024 FORTEI-International Conference on Electrical Engineering: Empowering Innovations : Navigating The Future Of Semiconductor Industry, FORTEI-ICEE 2024, no. October, pp. 58–63, 2024, doi: 10.1109/FORTEI-ICEE64706.2024.10824490.
D. D. Malicious, J. Jan, J. Feb, and J. May, 2022 index IEEE internet of things journal vol. 9, vol. 9. 2023. doi: 10.1109/jiot.2022.3232257.
E. Gelenbe, B. C. Gül, and M. Nakıp, “DISFIDA: Distributed self-supervised federated intrusion detection algorithm with online learning for health internet of things and internet of vehicles,” Internet of Things (Netherlands), vol. 28, no. August, p. 101340, 2024, doi: 10.1016/j.iot.2024.101340.
K. Kurniabudi, E. A. Winanto, L. Y. Astri, and S. Sharipuddin, “Ensemble method for anomaly detection on the internet of things,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 18, no. 1, p. 25, 2024, doi: 10.22146/ijccs.85834.
Sharipuddin, E. A. Winanto, Z. Z. Mohtar, Kurniabudi, I. S. Wijaya, and D. Sandra, “Improvement detection system on complex network using hybrid deep belief network and selection features,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 31, no. 1, pp. 470–479, 2023, doi: 10.11591/ijeecs.v31.i1.pp470-479.
K. G. Maheswari, C. Siva, and G. Nalinipriya, “Optimal cluster based feature selection for intrusion detection system in web and cloud computing environment using hybrid teacher learning optimization enables deep recurrent neural network,” Computer Communications, vol. 202, no. September 2022, pp. 145–153, 2023, doi: 10.1016/j.comcom.2023.02.003.
]R. SaiSindhuTheja and G. K. Shyam, “An efficient metaheuristic algorithm based feature selection and recurrent neural network for DoS attack detection in cloud computing environment,” Applied Soft Computing, vol. 100, p. 106997, 2021, doi: 10.1016/j.asoc.2020.106997.
I. K. Thajeel, K. Samsudin, S. J. Hashim, and F. Hashim, “Dynamic feature selection model for adaptive cross site scripting attack detection using developed multi-agent deep Q learning model,” Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 6, p. 101490, 2023, doi: 10.1016/j.jksuci.2023.01.012.
K. Ren, Y. Zeng, Z. Cao, and Y. Zhang, “ID-RDRL: a deep reinforcement learning-based feature selection intrusion detection model,” Scientific Reports, vol. 12, no. 1, pp. 1–18, 2022, doi: 10.1038/s41598-022-19366-3.
S. Waskle, L. Parashar, and U. Singh, “Intrusion detection system using PCA with random forest approach,” in Proceedings of the international conference on electronics and sustainable communication systems, ICESC 2020, 2020, pp. 803–808. doi: 10.1109/ICESC48915.2020.9155656
Z. Liu, Y. Fang, C. Huang, and Y. Xu, “MFXSS: An effective XSS vulnerability detection method in JavaScript based on multi-feature model,” Computers & Security, vol. 124, 103015, 2023, doi: 10.1016/j.cose.2022.103015.
C. Wang, H. Zhou, Z. Hao, S. Hu, J. Li, X. Zhang, B. Jiang, and X. Chen, “Network traffic analysis over clustering-based collective anomaly detection,” Computer Networks, vol. 205, 108760, 2022, doi: 10.1016/j.comnet.2022.108760.
S. Vaishnodevi, D . V. Kumar, G. Murali, M. Azhagiri, C. A. Madhuvappan, and K. Sathishkumar, “Network traffic examination for network intrusion detection in IOV using autoencoder and decoder,” in 2nd international conference on intelligent cyber physical systems and internet of things, ICoICI 2024 - proceedings, IEEE, 2024, pp. 13–18. doi: 10.1109/ICoICI62503.2024.10696347.
S. Ustebay, Z. Turgut, and M. A. Aydin, “Intrusion detection system with recursive feature elimination by using random forest and deep learning classifier,” International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism, IBIGDELFT 2018 - Proceedings, pp. 71–76, 2019, doi: 10.1109/IBIGDELFT.2018.8625318.
J. Lee, J. G. Pak, and M. Lee, “Network intrusion detection system using feature extraction based on deep sparse autoencoder,” International Conference on ICT Convergence, vol. 2020-Octob, pp. 1282–1287, 2020, doi: 10.1109/ICTC49870.2020.9289253.
S. Zavrak and M. Iskefiyeli, “Anomaly-based intrusion detection from network flow features using variational autoencoder,” IEEE Access, vol. 8, pp. 108346–108358, 2020, doi: 10.1109/ACCESS.2020.3001350.
A. Bhardwaj, V. Mangat, and R. Vig, “Hyperband tuned deep neural network with well posed stacked sparse AutoEncoder for detection of DDoS attacks in cloud,” IEEE Access, vol. 8, pp. 181916–181929, 2020, doi: 10.1109/access.2020.3028690.
E. C. P. Neto, S. Dadkhah, R. Ferreira, A. Zohourian, R. Lu, and A. A. Ghorbani, “CICIoT2023: a real-time dataset and benchmark for large-scale attacks in IoT environment,” Sensors, vol. 23, no. 13, 2023, doi: 10.3390/s23135941.
Y. Meidan, M. Bohadana, and D. Breitenbacher, “N-BaIoT — network-based detection of IoT botnet attacks using deep autoencoders,” IEEE Pervasive Computing, no. September, pp. 12–22, 2018.
A. Sarıkaya, B. G. Kılıç, and M. Demirci, “RAIDS: Robust autoencoder-based intrusion detection system model against adversarial attacks,” Computers and Security, vol. 135, no. March, p. 103483, 2023, doi: 10.1016/j.cose.2023.103483.
W. Wang, S. M. Sadjadi, and N. Rishe, “Curse of feature selection: a comparison experiment of DDoS detection using classification techniques,” Proceedings - 20th IEEE International Symposium on Parallel and Distributed Processing with Applications, 12th IEEE International Conference on Big Data and Cloud Computing, 12th IEEE International Conference on Sustainable Computing and Communications an, pp. 262–269, 2022, doi: 10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00040.
T. Wisanwanichthan and M. Thammawichai, “A double-layered hybrid approach for network intrusion detection system using combined naive bayes and SVM,” IEEE Access, vol. 9, pp. 138432–138450, 2021, doi: 10.1109/ACCESS.2021.3118573.
J. Gu and S. Lu, “An effective intrusion detection approach using SVM with naïve Bayes feature embedding,” Computers and Security, vol. 103, p. 102158, 2021, doi: 10.1016/j.cose.2020.102158.
B. Xu, S. Chen, H. Zhang, and T. Wu, “Incremental k-NN SVM method in intrusion detection,” Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS, vol. 2017-Novem, pp. 712–717, 2018, doi: 10.1109/ICSESS.2017.8343013
Y. Song, S. Hyun, and Y.-G. Cheong, “Analysis of autoencoders for network intrusion detection,” Sensors, vol. 21, no. 13, p. 4294, 2021, doi: 10.3390/s21134294.
F. S. Alrayes, M. Zakariah, S. U. Amin, Z. I. Khan, and M. Helal, “Intrusion detection in IoT systems using denoising autoencoder,” IEEE Access, vol. 10, pp. 135308–135318, 2022, doi: 10.1109/ACCESS.2022.3222774.
S. A. H. Ayubkhan, W.-S. Yap, E. Morris, and M. B. K. Rawthar, “A practical intrusion detection system based on denoising autoencoder and LightGBM classifier with improved detection performance,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, pp. 7427–7452, 2022, doi: 10.1007/s12652-022-03653-8.
M. A. Alsoufi, M. M. Siraj, and F. A. Sim, “An anomaly intrusion detection system in IoT based on autoencoder: a review,” in Advanced intelligent computing technologies and applications, Springer, 2024, pp. 331–343. doi: 10.1007/978-981-99-1299-6_27.
J.-f. Cui, H. Xia, R. Zhang, B.-x. Hu, and X.-g. Cheng, “Optimization scheme for intrusion detection scheme GBDT in edge computing center,” Computer Communications, vol. 168, pp. 136–145, 2021, doi: 10.1016/j.comcom.2020.12.007.
P. Mahadevappa, R. K. Murugesan, R. Al-amri, R. Thabit, A. H. Al-Ghushami, and G. Alkawsi, “A secure edge computing model using machine learning and IDS to detect and isolate intruders,” MethodsX, vol. 12, no. November 2023, p. 102597, 2024, doi: 10.1016/j.mex.2024.102597.
F. Ullah, S. Ullah, G. Srivastava, and J. C. W. Lin, “IDS-INT: Intrusion detection system using transformer-based transfer learning for imbalanced network traffic,” Digital Communications and Networks, vol. 10, no. 1, pp. 190–204, 2024, doi: 10.1016/j.dcan.2023.03.008.
M. R. Shahid, G. Blanc, Z. Zhang, and H. Debar, “Anomalous communications detection in IoT networks using sparse autoencoders,” 2019. [Online]. Available: https://arxiv.org/abs/1912.11831
DOI: http://dx.doi.org/10.61944/bids.v3i2.104
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