{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T18:00:32Z","timestamp":1774720832401,"version":"3.50.1"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T00:00:00Z","timestamp":1714435200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T00:00:00Z","timestamp":1714435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Inf. Secur."],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Embedded systems, including the Internet of things (IoT), play a crucial role in the functioning of critical infrastructure. However, these devices face significant challenges such as memory footprint, technical challenges, privacy concerns, performance trade-offs and vulnerability to cyber-attacks. One approach to address these concerns is minimising computational overhead and adopting lightweight intrusion detection techniques. In this study, we propose a highly efficient model called optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in IoT environments. The proposed OCFSDA model incorporates feature selection, data compression, pruning, and deparameterization. We deployed the model on a Raspberry Pi4 using the TFLite interpreter by leveraging optimisation and inferencing with semi-supervised learning. Using the MQTT-IoT-IDS2020 and CIC-IDS2017 datasets, our experimental results demonstrate a remarkable reduction in the computation cost in terms of time and memory use. Notably, the model achieved an overall average accuracies of 99% and 97%, along with comparable performance on other important metrics such as precision, recall, and F1-score. Moreover, the model accomplished the classification tasks within 0.30 and 0.12\u00a0s using only 2KB of memory.<\/jats:p>","DOI":"10.1007\/s10207-024-00855-7","type":"journal-article","created":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T05:01:38Z","timestamp":1714453298000},"page":"2559-2581","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things"],"prefix":"10.1007","volume":"23","author":[{"given":"Uneneibotejit","family":"Otokwala","sequence":"first","affiliation":[]},{"given":"Andrei","family":"Petrovski","sequence":"additional","affiliation":[]},{"given":"Harsha","family":"Kalutarage","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,30]]},"reference":[{"issue":"15","key":"855_CR1","doi-asserted-by":"publisher","first-page":"12251","DOI":"10.1109\/JIOT.2021.3060878","volume":"8","author":"M Abdel-Basset","year":"2021","unstructured":"Abdel-Basset, M., Hawash, H., Chakrabortty, R.K., Ryan, M.J.: Semi-supervised spatiotemporal deep learning for intrusions detection in IoT networks. IEEE Internet Things J. 8(15), 12251\u201312265 (2021)","journal-title":"IEEE Internet Things J."},{"issue":"2","key":"855_CR2","first-page":"48","volume":"16","author":"M Abdullah","year":"2018","unstructured":"Abdullah, M., Alshannaq, A., Balamash, A., Almabdy, S.: Enhanced intrusion detection system using feature selection method and ensemble learning algorithms. Int. J. Comput. Sci. Inf. Secur. (IJCSIS) 16(2), 48\u201355 (2018)","journal-title":"Int. J. Comput. Sci. Inf. Secur. (IJCSIS)"},{"key":"855_CR3","doi-asserted-by":"crossref","unstructured":"Aghakhani, H., Meng, D., Wang, Y.X., Kruegel, C., Vigna, G.: Bullseye polytope: a scalable clean-label poisoning attack with improved transferability. In: 2021 IEEE European Symposium on Security and Privacy (EuroS &P). IEEE, pp. 159\u2013178 (2021)","DOI":"10.1109\/EuroSP51992.2021.00021"},{"issue":"1","key":"855_CR4","first-page":"41","volume":"20","author":"KKR Amrita","year":"2018","unstructured":"Amrita, K.K.R.: A hybrid intrusion detection system: integrating hybrid feature selection approach with heterogeneous ensemble of intelligent classifiers. Int. J. Netw. Secur. 20(1), 41\u201355 (2018)","journal-title":"Int. J. Netw. Secur."},{"issue":"2","key":"855_CR5","first-page":"18","volume":"2","author":"B Azhagusundari","year":"2013","unstructured":"Azhagusundari, B., Thanamani, A.S., et al.: Feature selection based on information gain. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 2(2), 18\u201321 (2013)","journal-title":"Int. J. Innov. Technol. Explor. Eng. (IJITEE)"},{"key":"855_CR6","unstructured":"Baldi, P.: Autoencoders, unsupervised learning, and deep architectures. In: Proceedings of ICML Workshop on Unsupervised and Transfer Learning. JMLR Workshop and Conference Proceedings, pp. 37\u201349 (2012)"},{"key":"855_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105805","volume":"119","author":"TK Boppana","year":"2023","unstructured":"Boppana, T.K., Bagade, P.: GAN-AE: an unsupervised intrusion detection system for MQTT networks. Eng. Appl. Artif. Intell. 119, 105805 (2023). https:\/\/doi.org\/10.1016\/j.engappai.2022.105805","journal-title":"Eng. Appl. Artif. Intell."},{"key":"855_CR8","unstructured":"Borgohain, T., Kumar, U., Sanyal, S.: Survey of security and privacy issues of internet of things. arXiv preprint arXiv:1501.02211 (2015)"},{"issue":"2","key":"855_CR9","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/s12583-021-1402-6","volume":"32","author":"Y Chen","year":"2021","unstructured":"Chen, Y., Wang, S., Zhao, Q., Sun, G.: Detection of multivariate geochemical anomalies using the bat-optimized isolation forest and bat-optimized elliptic envelope models. J. Earth Sci. 32(2), 415\u2013426 (2021)","journal-title":"J. Earth Sci."},{"key":"855_CR10","doi-asserted-by":"crossref","unstructured":"Choi, S.K., Yang, C.H., Kwak, J.: System hardening and security monitoring for IoT devices to mitigate IoT security vulnerabilities and threats. KSII Trans. Internet Inf. Syst. 12(2) (2018)","DOI":"10.3837\/tiis.2018.02.022"},{"key":"855_CR11","doi-asserted-by":"publisher","unstructured":"Ciklabakkal, E., Donmez, A., Erdemir, M., Suren, E., Yilmaz, M.K., Angin, P.: ARTEMIS: An intrusion detection system for MQTT attacks in internet of things. In: 2019 38th Symposium on Reliable Distributed Systems (SRDS). IEEE (2019). https:\/\/doi.org\/10.1109\/srds47363.2019.00053","DOI":"10.1109\/srds47363.2019.00053"},{"key":"855_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102448","volume":"110","author":"Z Halim","year":"2021","unstructured":"Halim, Z., Yousaf, M.N., Waqas, M., Sulaiman, M., Abbas, G., Hussain, M., Ahmad, I., Hanif, M.: An effective genetic algorithm-based feature selection method for intrusion detection systems. Comput. Secur. 110, 102448 (2021)","journal-title":"Comput. Secur."},{"key":"855_CR13","doi-asserted-by":"crossref","unstructured":"Hanafi, A.V., Ghaffari, A., Rezaei, H., Valipour, A., arasteh, B.: Intrusion detection in internet of things using improved binary golden jackal optimization algorithm and LSTM. Cluster Comput. 1\u201318 (2023)","DOI":"10.1007\/s10586-023-04102-x"},{"key":"855_CR14","unstructured":"Hindy, H., Tachtatzis, C., Atkinson, R., Bayne, E., Bellekens, X.: Mqtt-iot-ids2020: Mqtt internet of things intrusion detection dataset. IEEE Dataport (2020)"},{"issue":"2","key":"855_CR15","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/s40747-017-0060-x","volume":"4","author":"N Hoque","year":"2018","unstructured":"Hoque, N., Singh, M., Bhattacharyya, D.K.: EFS-MI: an ensemble feature selection method for classification. Complex Intell. Syst. 4(2), 105\u2013118 (2018)","journal-title":"Complex Intell. Syst."},{"issue":"1","key":"855_CR16","doi-asserted-by":"publisher","first-page":"209","DOI":"10.12785\/ijcds\/110117","volume":"11","author":"I Idrissi","year":"2022","unstructured":"Idrissi, I., Moussaoui, O., Azizi, M.: A lightweight optimized deep learning-based host-intrusion detection system deployed on the edge for IoT. Int. J. Comput. Digital Syst. 11(1), 209\u2013216 (2022). https:\/\/doi.org\/10.12785\/ijcds\/110117","journal-title":"Int. J. Comput. Digital Syst."},{"issue":"3","key":"855_CR17","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/0893-6080(91)90075-G","volume":"4","author":"Y Ito","year":"1991","unstructured":"Ito, Y.: Representation of functions by superpositions of a step or sigmoid function and their applications to neural network theory. Neural Netw. 4(3), 385\u2013394 (1991)","journal-title":"Neural Netw."},{"key":"855_CR18","doi-asserted-by":"publisher","unstructured":"Jaafar, F., Malik, Y., Serre, J., Wang, H., Wang, T.: Lightweight intrusion detection in MQTT based sensor network. In: 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE (2022). https:\/\/doi.org\/10.1109\/iceccme55909.2022.9988354","DOI":"10.1109\/iceccme55909.2022.9988354"},{"key":"855_CR19","doi-asserted-by":"publisher","first-page":"42450","DOI":"10.1109\/ACCESS.2019.2907965","volume":"7","author":"SU Jan","year":"2019","unstructured":"Jan, S.U., Ahmed, S., Shakhov, V., Koo, I.: Toward a lightweight intrusion detection system for the internet of things. IEEE Access 7, 42450\u201342471 (2019)","journal-title":"IEEE Access"},{"issue":"10","key":"855_CR20","doi-asserted-by":"publisher","first-page":"1764","DOI":"10.3390\/sym13101764","volume":"13","author":"E Jaw","year":"2021","unstructured":"Jaw, E., Wang, X.: Feature selection and ensemble-based intrusion detection system: an efficient and comprehensive approach. Symmetry 13(10), 1764 (2021)","journal-title":"Symmetry"},{"issue":"6","key":"855_CR21","doi-asserted-by":"publisher","first-page":"1022","DOI":"10.3390\/electronics9061022","volume":"9","author":"S Kim","year":"2020","unstructured":"Kim, S., Hwang, C., Lee, T.: Anomaly based unknown intrusion detection in endpoint environments. Electronics 9(6), 1022 (2020)","journal-title":"Electronics"},{"key":"855_CR22","doi-asserted-by":"publisher","first-page":"8434","DOI":"10.1109\/ACCESS.2022.3144208","volume":"10","author":"B Lahasan","year":"2022","unstructured":"Lahasan, B., Samma, H.: Optimized deep autoencoder model for internet of things intruder detection. IEEE Access 10, 8434\u20138448 (2022)","journal-title":"IEEE Access"},{"key":"855_CR23","first-page":"1","volume":"20","author":"QV Le","year":"2015","unstructured":"Le, Q.V., et al.: A tutorial on deep learning part 2: Autoencoders, convolutional neural networks and recurrent neural networks. Google Brain 20, 1\u201320 (2015)","journal-title":"Google Brain"},{"issue":"3","key":"855_CR24","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1145\/45072.45074","volume":"19","author":"DA Lelewer","year":"1987","unstructured":"Lelewer, D.A., Hirschberg, D.S.: Data compression. ACM Comput. Surv. (CSUR) 19(3), 261\u2013296 (1987)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"855_CR25","unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710 (2016)"},{"key":"855_CR26","doi-asserted-by":"crossref","unstructured":"Li, J.: Research on intrusion detect system of internet of things based on deep learning. In: 2022 International Conference on Machine Learning and Knowledge Engineering (MLKE), pp. 55\u201358. IEEE (2022)","DOI":"10.1109\/MLKE55170.2022.00016"},{"key":"855_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2020.101851","volume":"95","author":"X Li","year":"2020","unstructured":"Li, X., Chen, W., Zhang, Q., Wu, L.: Building auto-encoder intrusion detection system based on random forest feature selection. Comput. Secur. 95, 101851 (2020)","journal-title":"Comput. Secur."},{"key":"855_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102842","volume":"121","author":"Y Li","year":"2022","unstructured":"Li, Y., Qin, T., Huang, Y., Lan, J., Liang, Z., Geng, T.: HDFEF: a hierarchical and dynamic feature extraction framework for intrusion detection systems. Comput. Secur. 121, 102842 (2022)","journal-title":"Comput. Secur."},{"issue":"6","key":"855_CR29","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1016\/j.cose.2009.01.001","volume":"28","author":"Y Li","year":"2009","unstructured":"Li, Y., Wang, J.L., Tian, Z.H., Lu, T.B., Young, C.: Building lightweight intrusion detection system using wrapper-based feature selection mechanisms. Comput. Secur. 28(6), 466\u2013475 (2009)","journal-title":"Comput. Secur."},{"key":"855_CR30","doi-asserted-by":"crossref","unstructured":"Liang, Y.: Efficient temporal compression in wireless sensor networks. In: 2011 IEEE 36th Conference on Local Computer Networks, pp. 466\u2013474. IEEE (2011)","DOI":"10.1109\/LCN.2011.6115508"},{"key":"855_CR31","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s11280-015-0381-x","volume":"20","author":"AS Manek","year":"2017","unstructured":"Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web 20, 135\u2013154 (2017)","journal-title":"World Wide Web"},{"issue":"5","key":"855_CR32","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12917","volume":"39","author":"RV Mendonca","year":"2022","unstructured":"Mendonca, R.V., Silva, J.C., Rosa, R.L., Saadi, M., Rodriguez, D.Z., Farouk, A.: A lightweight intelligent intrusion detection system for industrial internet of things using deep learning algorithms. Expert. Syst. 39(5), e12917 (2022)","journal-title":"Expert. Syst."},{"issue":"5","key":"855_CR33","first-page":"129","volume":"4","author":"M Moukhafi","year":"2018","unstructured":"Moukhafi, M., El Yassini, K., Bri, S.: A novel hybrid GA and SVM with PSO feature selection for intrusion detection system. Int. J. Adv. Sci. Res. Eng. 4(5), 129\u2013134 (2018)","journal-title":"Int. J. Adv. Sci. Res. Eng."},{"key":"855_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.108768","volume":"121","author":"E Mushtaq","year":"2022","unstructured":"Mushtaq, E., Zameer, A., Umer, M., Abbasi, A.A.: A two-stage intrusion detection system with auto-encoder and LSTMs. Appl. Soft Comput. 121, 108768 (2022)","journal-title":"Appl. Soft Comput."},{"key":"855_CR35","doi-asserted-by":"crossref","unstructured":"Neisse, R., Baldini, G., Steri, G., Ahmad, A., Fourneret, E., Legeard, B.: Improving internet of things device certification with policy-based management. In: 2017 Global Internet of Things Summit (GIoTS), pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/GIOTS.2017.8016273"},{"issue":"1","key":"855_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13040-016-0114-4","volume":"9","author":"U Neumann","year":"2016","unstructured":"Neumann, U., Riemenschneider, M., Sowa, J.P., Baars, T., K\u00e4lsch, J., Canbay, A., Heider, D.: Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach. BioData Mining 9(1), 1\u201314 (2016)","journal-title":"BioData Mining"},{"key":"855_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2020.100663","volume":"54","author":"BH Nguyen","year":"2020","unstructured":"Nguyen, B.H., Xue, B., Zhang, M.: A survey on swarm intelligence approaches to feature selection in data mining. Swarm Evol. Comput. 54, 100663 (2020)","journal-title":"Swarm Evol. Comput."},{"issue":"2","key":"855_CR38","doi-asserted-by":"publisher","first-page":"432","DOI":"10.3390\/s22020432","volume":"22","author":"XH Nguyen","year":"2022","unstructured":"Nguyen, X.H., Nguyen, X.D., Huynh, H.H., Le, K.H.: Realguard: a lightweight network intrusion detection system for IoT gateways. Sensors 22(2), 432 (2022)","journal-title":"Sensors"},{"key":"855_CR39","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1109\/ACCESS.2022.3233775","volume":"11","author":"OD Okey","year":"2023","unstructured":"Okey, O.D., Melgarejo, D.C., Saadi, M., Rosa, R.L., Kleinschmidt, J.H., Rodr\u00edguez, D.Z.: Transfer learning approach to ids on cloud IoT devices using optimized CNN. IEEE Access 11, 1023\u20131038 (2023)","journal-title":"IEEE Access"},{"issue":"2","key":"855_CR40","doi-asserted-by":"publisher","first-page":"315","DOI":"10.2298\/FUEE1902315O","volume":"32","author":"O Osanaiye","year":"2019","unstructured":"Osanaiye, O., Ogundile, O., Aina, F., Periola, A.: Feature selection for intrusion detection system in a cluster-based heterogeneous wireless sensor network. Facta Universitatis Ser. Electron. Energet. 32(2), 315\u2013330 (2019)","journal-title":"Facta Universitatis Ser. Electron. Energet."},{"key":"855_CR41","doi-asserted-by":"crossref","unstructured":"Otokwala, U.J., Petrovski, A., Kotenko, I.V.: Enhancing intrusion detection through data perturbation augmentation strategy, Unpublished (2024)","DOI":"10.1109\/USBEREIT61901.2024.10584007"},{"key":"855_CR42","doi-asserted-by":"crossref","unstructured":"Paudice, A., Mu\u00f1oz-Gonz\u00e1lez, L., Lupu, E.C.: Label sanitization against label flipping poisoning attacks. In: ECML PKDD 2018 Workshops: Nemesis 2018, UrbReas 2018, SoGood 2018, IWAISe 2018, and Green Data Mining 2018, Dublin, Ireland, September 10\u201314, 2018, Proceedings 18, pp. 5\u201315. Springer, Berlin (2019)","DOI":"10.1007\/978-3-030-13453-2_1"},{"key":"855_CR43","doi-asserted-by":"crossref","unstructured":"Peri, N., Gupta, N., Huang, W.R., Fowl, L., Zhu, C., Feizi, S., Goldstein, T., Dickerson, J.P.: Deep k-nn defense against clean-label data poisoning attacks. In: Computer Vision\u2013ECCV 2020 Workshops: Glasgow, UK, August 23\u201328, 2020, Proceedings, Part I 16, pp. 55\u201370. Springer, Berlin (2020)","DOI":"10.1007\/978-3-030-66415-2_4"},{"issue":"8","key":"855_CR44","doi-asserted-by":"publisher","first-page":"436","DOI":"10.3390\/systems11080436","volume":"11","author":"G Perumal","year":"2023","unstructured":"Perumal, G., Subburayalu, G., Abbas, Q., Naqi, S.M., Qureshi, I.: VBQ-Net: a novel vectorization-based boost quantized network model for maximizing the security level of IoT system to prevent intrusions. Systems 11(8), 436 (2023)","journal-title":"Systems"},{"key":"855_CR45","doi-asserted-by":"crossref","unstructured":"Rachburee, N., Punlumjeak, W.: A comparison of feature selection approach between greedy, IG-ratio, chi-square, and MRMR in educational mining. In: 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE), pp. 420\u2013424. IEEE (2015)","DOI":"10.1109\/ICITEED.2015.7408983"},{"key":"855_CR46","doi-asserted-by":"crossref","unstructured":"Rizvi, S., Scanlon, M., McGibney, J., Sheppard, J.: Deep learning based network intrusion detection system for resource-constrained environments. In: International Conference on Digital Forensics and Cyber Crime, pp. 355\u2013367. Springer, Berlin (2022)","DOI":"10.1007\/978-3-031-36574-4_21"},{"key":"855_CR47","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez, D., Ruiz, R., Cuadrado-Gallego, J., Aguilar-Ruiz, J.: Detecting fault modules applying feature selection to classifiers. In: 2007 IEEE International Conference on Information Reuse and Integration, pp. 667\u2013672. IEEE (2007)","DOI":"10.1109\/IRI.2007.4296696"},{"key":"855_CR48","unstructured":"Roesch, M., et\u00a0al.: Snort: Lightweight intrusion detection for networks. In: Lisa, vol.\u00a099, pp. 229\u2013238 (1999)"},{"key":"855_CR49","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.future.2021.09.027","volume":"127","author":"S Roy","year":"2022","unstructured":"Roy, S., Li, J., Choi, B.J., Bai, Y.: A lightweight supervised intrusion detection mechanism for IoT networks. Futur. Gener. Comput. Syst. 127, 276\u2013285 (2022)","journal-title":"Futur. Gener. Comput. Syst."},{"issue":"3","key":"855_CR50","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1198\/106186008X344522","volume":"17","author":"M Sandri","year":"2008","unstructured":"Sandri, M., Zuccolotto, P.: A bias correction algorithm for the gini variable importance measure in classification trees. J. Comput. Graph. Stat. 17(3), 611\u2013628 (2008)","journal-title":"J. Comput. Graph. Stat."},{"key":"855_CR51","volume-title":"Introduction to Data Compression","author":"K Sayood","year":"2017","unstructured":"Sayood, K.: Introduction to Data Compression. Morgan Kaufmann, Burlington (2017)"},{"key":"855_CR52","first-page":"108","volume":"1","author":"I Sharafaldin","year":"2018","unstructured":"Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp 1, 108\u2013116 (2018)","journal-title":"ICISSp"},{"key":"855_CR53","doi-asserted-by":"crossref","unstructured":"Sharmila, B., Nagapadma, R.: QAE-IDS: DDoS anomaly detection in IoT devices using post-quantization training. Smart Sci. 1\u201316 (2023)","DOI":"10.1080\/23080477.2023.2260023"},{"issue":"1","key":"855_CR54","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1186\/s42400-023-00178-5","volume":"6","author":"B Sharmila","year":"2023","unstructured":"Sharmila, B., Nagapadma, R.: Quantized autoencoder (QAE) intrusion detection system for anomaly detection in resource-constrained IoT devices using rt-iot2022 dataset. Cybersecurity 6(1), 41 (2023)","journal-title":"Cybersecurity"},{"issue":"1","key":"855_CR55","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/TETCI.2017.2772792","volume":"2","author":"N Shone","year":"2018","unstructured":"Shone, N., Ngoc, T.N., Phai, V.D., Shi, Q.: A deep learning approach to network intrusion detection. IEEE Trans. Emerging Top. Comput. Intell. 2(1), 41\u201350 (2018)","journal-title":"IEEE Trans. Emerging Top. Comput. Intell."},{"key":"855_CR56","doi-asserted-by":"publisher","first-page":"33095","DOI":"10.1109\/ACCESS.2022.3161566","volume":"10","author":"H Siddharthan","year":"2022","unstructured":"Siddharthan, H., Deepa, T., Chandhar, P.: SENMQTT-set: an intelligent intrusion detection in IoT-MQTT networks using ensemble multi cascade features. IEEE Access 10, 33095\u201333110 (2022)","journal-title":"IEEE Access"},{"key":"855_CR57","doi-asserted-by":"crossref","unstructured":"Soe, Y.N., Feng, Y., Santosa, P.I., Hartanto, R., Sakurai, K.: Implementing lightweight IoT-IDS on raspberry PI using correlation-based feature selection and its performance evaluation. In: Advanced Information Networking and Applications: Proceedings of the 33rd International Conference on Advanced Information Networking and Applications (AINA-2019), vol 33, pp 458\u2013469. Springer, Berlin (2020)","DOI":"10.1007\/978-3-030-15032-7_39"},{"issue":"2","key":"855_CR58","doi-asserted-by":"publisher","first-page":"264","DOI":"10.23919\/JCN.2022.000002","volume":"24","author":"S Subbiah","year":"2022","unstructured":"Subbiah, S., Anbananthen, K.S.M., Thangaraj, S., Kannan, S., Chelliah, D.: Intrusion detection technique in wireless sensor network using grid search random forest with boruta feature selection algorithm. J. Commun. Netw. 24(2), 264\u2013273 (2022)","journal-title":"J. Commun. Netw."},{"key":"855_CR59","doi-asserted-by":"publisher","first-page":"13624","DOI":"10.1109\/ACCESS.2018.2810198","volume":"6","author":"P Tao","year":"2018","unstructured":"Tao, P., Sun, Z., Sun, Z.: An improved intrusion detection algorithm based on GA and SVM. IEEE Access 6, 13624\u201313631 (2018)","journal-title":"IEEE Access"},{"key":"855_CR60","unstructured":"Van Der Maaten, L., Postma, E., Van den Herik, J., et al.: Dimensionality reduction: a comparative. J. Mach. Learn. Res. 10(66\u201371), 13 (2009)"},{"issue":"2","key":"855_CR61","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1080\/21642583.2019.1620658","volume":"7","author":"J Wang","year":"2019","unstructured":"Wang, J., Xu, J., Zhao, C., Peng, Y., Wang, H.: An ensemble feature selection method for high-dimensional data based on sort aggregation. Syst. Sci. Control Eng. 7(2), 32\u201339 (2019)","journal-title":"Syst. Sci. Control Eng."},{"key":"855_CR62","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1569","volume":"9","author":"Z Wang","year":"2023","unstructured":"Wang, Z., Chen, H., Yang, S., Luo, X., Li, D., Wang, J.: A lightweight intrusion detection method for IoT based on deep learning and dynamic quantization. PeerJ Comput. Sci. 9, e1569 (2023)","journal-title":"PeerJ Comput. Sci."},{"key":"855_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117671","volume":"206","author":"Z Wang","year":"2022","unstructured":"Wang, Z., Li, Z., He, D., Chan, S.: A lightweight approach for network intrusion detection in industrial cyber-physical systems based on knowledge distillation and deep metric learning. Expert Syst. Appl. 206, 117671 (2022)","journal-title":"Expert Syst. Appl."},{"issue":"9","key":"855_CR64","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1002\/fld.975","volume":"48","author":"F Xiao","year":"2005","unstructured":"Xiao, F., Honma, Y., Kono, T.: A simple algebraic interface capturing scheme using hyperbolic tangent function. Int. J. Numer. Methods Fluids 48(9), 1023\u20131040 (2005)","journal-title":"Int. J. Numer. Methods Fluids"},{"key":"855_CR65","doi-asserted-by":"crossref","unstructured":"Xu, Y., Tang, Y., Yang, Q.: Deep learning for IoT intrusion detection based on LSTMs-AE. In: Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture, pp 64\u201368 (2020)","DOI":"10.1145\/3421766.3421891"},{"key":"855_CR66","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103388","volume":"133","author":"I Zakariyya","year":"2023","unstructured":"Zakariyya, I., Kalutarage, H., Al-Kadri, M.O.: Towards a robust, effective and resource efficient machine learning technique for IoT security monitoring. Comput. Secur. 133, 103388 (2023)","journal-title":"Comput. Secur."},{"issue":"2","key":"855_CR67","doi-asserted-by":"publisher","first-page":"56","DOI":"10.38094\/jastt1224","volume":"1","author":"R Zebari","year":"2020","unstructured":"Zebari, R., Abdulazeez, A., Zeebaree, D., Zebari, D., Saeed, J.: A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction. J. Appl. Sci. Technol. Trends 1(2), 56\u201370 (2020)","journal-title":"J. Appl. Sci. Technol. Trends"},{"issue":"6","key":"855_CR68","doi-asserted-by":"publisher","first-page":"5026","DOI":"10.1109\/TCYB.2020.3026101","volume":"52","author":"D Zeng","year":"2020","unstructured":"Zeng, D., Wu, Z., Ding, C., Ren, Z., Yang, Q., Xie, S.: Labeled-robust regression: simultaneous data recovery and classification. IEEE Trans. Cybernet. 52(6), 5026\u20135039 (2020)","journal-title":"IEEE Trans. Cybernet."},{"issue":"12","key":"855_CR69","doi-asserted-by":"publisher","first-page":"9960","DOI":"10.1109\/JIOT.2021.3119055","volume":"9","author":"R Zhao","year":"2021","unstructured":"Zhao, R., Gui, G., Xue, Z., Yin, J., Ohtsuki, T., Adebisi, B., Gacanin, H.: A novel intrusion detection method based on lightweight neural network for internet of things. IEEE Internet Things J. 9(12), 9960\u20139972 (2021)","journal-title":"IEEE Internet Things J."}],"container-title":["International Journal of Information Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-024-00855-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10207-024-00855-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-024-00855-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T03:13:47Z","timestamp":1721013227000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10207-024-00855-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,30]]},"references-count":69,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["855"],"URL":"https:\/\/doi.org\/10.1007\/s10207-024-00855-7","relation":{},"ISSN":["1615-5262","1615-5270"],"issn-type":[{"value":"1615-5262","type":"print"},{"value":"1615-5270","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,30]]},"assertion":[{"value":"30 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"I declare that the authors have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The study did not involve the use of humans or animals.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animals rights"}}]}}