{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T05:29:24Z","timestamp":1732685364015,"version":"3.28.2"},"reference-count":31,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,10,4]]},"abstract":"Wireless sensor nodes (WSN) combine sensing and communication capabilities in the smallest sensor network component. Sensor nodes have basic networking capabilities, such as wireless connection with other nodes, data storage, and a microcontroller to do basic processing. The intrusion detection problem is well analyzed and there exist numerous techniques to solve this issue but suffer will poor intrusion detection accuracy and a higher false alarm ratio. To overcome this challenge, a novel Intrusion Detection via Salp Swarm Optimization based Deep Learning Algorithm (ID-SODA) has been proposed which classifies intrusion node and non-intrusion node. The proposed ID-SODA technique uses the k-means clustering algorithm to perform clustering. The Salp Swarm Optimization (SSO) technique takes into residual energy, distance, and cost while choosing the cluster head selection (CHS). The CHS is given the input to a multi-head convolutional neural network (MHCNN), which will classify into intrusion node and non-intrusion node. The performance analysis of the suggested ID-SODA is evaluated based on the parameters like accuracy, precision, F1 score, detection rate, recall, false alarm rate, and false negative rate. The suggested ID-SODA achieves an accuracy range of 98.95%. The result shows that the suggested ID-SODA improves the overall accuracy better than 6.56%, 2.94%, and 2.95% in SMOTE, SLGBM, and GWOSVM-IDS respectively.<\/jats:p>","DOI":"10.3233\/jifs-231756","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T15:19:31Z","timestamp":1688138371000},"page":"6897-6909","source":"Crossref","is-referenced-by-count":0,"title":["Blocking intrusion logic using optimized multi-head convolution in wireless sensor network"],"prefix":"10.1177","volume":"45","author":[{"given":"S.","family":"Prabhu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, S.A. Engineering College, Chennai, Tamil Nadu, India"}]},{"given":"E.A.","family":"Mary Anita","sequence":"additional","affiliation":[{"name":"Department of Computer Science Engineering, Christ University, Bengaluru, Karnataka, India"}]},{"given":"D.","family":"Mohanageetha","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India"}]}],"member":"179","reference":[{"issue":"3","key":"10.3233\/JIFS-231756_ref1","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.3390\/s22031070","article-title":"LT-FS-ID: Log-Transformed Feature Learning and Feature-Scaling-Based Machine Learning Algorithms to Predict the k-Barriers for Intrusion Detection Using Wireless Sensor Network","volume":"22","author":"Singh","year":"2022","journal-title":"Sensors"},{"issue":"2","key":"10.3233\/JIFS-231756_ref2","first-page":"873","article-title":"A deep learning-based frechet and dirichlet model for intrusion detection in IWSN","volume":"42","author":"Alzubi","year":"2022","journal-title":"Fuzzy Systems & Fuzzy Systems"},{"issue":"1","key":"10.3233\/JIFS-231756_ref3","doi-asserted-by":"crossref","first-page":"516","DOI":"10.11591\/ijeecs.v21.i1.pp516-523","article-title":"Data mining approach to analyzing intrusion detection of wireless sensor network","volume":"21","author":"Rezvi","year":"2021","journal-title":"Indonesian J Electric Eng Comput Sci"},{"key":"10.3233\/JIFS-231756_ref4","doi-asserted-by":"crossref","first-page":"114603","DOI":"10.1016\/j.eswa.2021.114603","article-title":"A Gaussian process regression approach to predict the k-barrier coverage probability for intrusion detection in wireless sensor networks","volume":"172","author":"Singh","year":"2021","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"10.3233\/JIFS-231756_ref5","first-page":"45","article-title":"Intrusion detection with wireless sensor network (WSN) internet of things","volume":"13","author":"Hendrawan","year":"2021","journal-title":"Journal of Telecommunication, Electronic and Computer Engineering (JTEC)"},{"issue":"3","key":"10.3233\/JIFS-231756_ref6","doi-asserted-by":"crossref","first-page":"244","DOI":"10.3103\/S1060992X20030029","article-title":"Supervised machine learning classification algorithmic approach for finding anomaly type of intrusion detection in wireless sensor network","volume":"29","author":"Abhale","year":"2020","journal-title":"Optical Memory and Neural Networks"},{"issue":"2","key":"10.3233\/JIFS-231756_ref7","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1007\/s11277-021-08288-4","article-title":"an environmental intrusion detection technology based on WiFi","volume":"119","author":"Zhu","year":"2021","journal-title":"Wireless Personal Communications"},{"key":"10.3233\/JIFS-231756_ref9","first-page":"102448","article-title":"An effective genetic algorithm-based feature selection method for intrusion detection systems","volume":"110","author":"Halim","year":"2021","journal-title":"Security & Security"},{"doi-asserted-by":"crossref","unstructured":"Alruhaily N.M. and Ibrahim D.M. , A multi-layer machine learning-based intrusion detection system for wireless sensor networks. 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