{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T17:00:31Z","timestamp":1769706031630,"version":"3.49.0"},"reference-count":32,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,10,4]]},"abstract":"<jats:p>A severe problem that regularly affects cloud systems are intrusions. Ignore how the expansion of Internet of Things (IoT) devices will result in enormous intrusions. To distinguish intrusions from authorized network activity, detection is a crucial procedure. An Enhanced Lion Optimization Algorithm (ELOA) is utilized in this research, IoT intrusion detection system. Intrusions are classified using the Deep Belief Network (DBN) and an SDN controller technique. The proposed ELOA-based Intrusion Detection System uses the optimal weight in DBN to train the neurons to categorize the data in a network as normal and attacked during the training phase. In the testing step that follows training, data from nodes are examined, and by contrasting the training results, they are categorized as normal and attacked data. By using the proposed ELOA and DBN algorithms, our intrusion detection system can successfully identify intrusions. Based on the creation of blacklists for detecting IoT intrusions, the (SDN) Software Defined Networking controller can effectively prohibit harmful devices. In order to demonstrate that the proposed ELOA finds network intrusions more successfully, its performance is compared to that of other existing techniques. The node sizes of the algorithms are run and evaluated for 1000, 2000, 3000, 4000, and 5000 respectively. At highest node 5000, the Proposed ELOA and DPN have precision, recall, f-score and accuracy becomes as 97.8, 96.22, 97.5 and 98.67 respectively.<\/jats:p>","DOI":"10.3233\/jifs-232532","type":"journal-article","created":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T11:01:41Z","timestamp":1690887701000},"page":"6605-6615","source":"Crossref","is-referenced-by-count":4,"title":["Enhanced Lion Optimization Algorithm and deep belief network for intrusion detection with SDN enabled IoT networks"],"prefix":"10.1177","volume":"45","author":[{"given":"D.","family":"Suresh Babu","sequence":"first","affiliation":[{"name":"Department of Information and Communication Engineering, Anna University, Chennai, Tamilnadu, India"}]},{"given":"M.","family":"Ramakrishnan","sequence":"additional","affiliation":[{"name":"Department of Computer Applications, School of Information Technology, Madurai Kamaraj University, Madurai, Tamilnadu, India"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-232532_ref1","doi-asserted-by":"crossref","unstructured":"el mourabit Yousef et al., Intrusion Detection Techniques in Wireless Sensor Network using Data Mining Algorithms: Comparative Evaluation Based on Attacks Detection, International Journal of Advanced Computer Science and Applications 6(9) (2015).","DOI":"10.14569\/IJACSA.2015.060922"},{"key":"10.3233\/JIFS-232532_ref2","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.procs.2016.06.016","article-title":"Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection","volume":"89","author":"Belavagi","year":"2016","journal-title":"Procedia Computer Science Elsevier"},{"key":"10.3233\/JIFS-232532_ref3","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.3390\/sym12061046","article-title":"A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms","volume":"2","author":"Almoman","year":"2020","journal-title":"Symmetry"},{"key":"10.3233\/JIFS-232532_ref4","doi-asserted-by":"crossref","unstructured":"Almasoudy Faezah Hamad , Al-Yaseen Wathiq Laftah and Idrees Ali Kadhum , Differential EVolution WraPPer Feature Selection for Intrusion Detection system, International Conference on Computational Intelligence and Data Science (ICCIDS, Procedia Computer Science 167 (2020), 1230\u20131239.","DOI":"10.1016\/j.procs.2020.03.438"},{"key":"10.3233\/JIFS-232532_ref5","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.cose.2017.06.005","article-title":"A wrapper approach for feature selection in network intrusion detection","volume":"70","author":"Khammassi","year":"2017","journal-title":"Computers & Security"},{"key":"10.3233\/JIFS-232532_ref6","doi-asserted-by":"publisher","DOI":"10.1109\/eStream.2017.7950325"},{"key":"10.3233\/JIFS-232532_ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2871719"},{"key":"10.3233\/JIFS-232532_ref8","doi-asserted-by":"crossref","unstructured":"Divyatmika , and Sreekesh Manasa , Two-tier Network based mostly Intrusion Detection System design mistreatment Machine Learning Approach, In Proceedings of International Conference on Electrical, physics, and improvement Techniques (ICEEOT), LNCS 7755404, (2016), pp. 42\u201347.","DOI":"10.1109\/ICEEOT.2016.7755404"},{"issue":"3","key":"10.3233\/JIFS-232532_ref9","first-page":"1601","article-title":"Reducing False Positive in Intrusion Detection System: A Survey","volume":"7","author":"Gupta","year":"2016","journal-title":"Proc. of International Journal of computing and data Technologies (IJCSIT)"},{"issue":"08","key":"10.3233\/JIFS-232532_ref10","first-page":"786","article-title":"Network intrusion detection system supported changed random forest classifiers for kdd cup-99 and NSL-KDD dataset","volume":"04","author":"Chandra","year":"2017","journal-title":"Proc of. 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