[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127334-en":3,"doc-seo-127334-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127334,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Detecting Network Security Incidents in Wireless Sensor Networks Using Machine Learning - Predictive Anomaly Detection Framework","The study advances cybersecurity for wireless sensor networks by integrating artificial intelligence and machine learning techniques to understand network behavior and enable predictive anomaly detection. Exploratory data analysis extracts security-relevant insights, while machine learning models based on decision trees and random forests demonstrate strong effectiveness. Performance is assessed using accuracy, precision, recall, and F1 scores, supporting reliable identification of potential security threats. The proposed methodology can be deployed across other WSN and IoT applications to strengthen incident detection and future cybersecurity initiatives.","Detecting network security incidents in wireless sensor networks using machine learning  \nTamara Zhukabayeva1,2, Atdhe Buja1,3, Melinda Pacolli4, Yerik Mardenov1,5  \n1International Science Complex “Astana”, Astana, Kazakhstan  \n2Eurasian National University, Astana, Kazakhstan  \n3ICT Academy Research, Prishtina, Kosovo  \n4ECPD, Prishtina, Kosovo  \n5Department of Information Technology and Engineering, Astana International University, Astana , Kazakhstan  \nArticle history:  \nReceived Apr 24, 2024 Revised Sep 9, 2024 Accepted Oct 7, 2024  \nKeywords:  \nAnomaly detection Artificial intelligence Cybersecurity  \nInternet of things Wireless sensor networks  \nCorresponding Author:  \nThis study enhances the domain of cybersecurity within wireless sensor networks (WSNs) through the integration of sophisticated artificial intelligence (AI) and machine learning (ML) techniques. By conducting an exploratory data analysis (EDA), this research reveals critical insights into network behavior, facilitating the development of predictive models for anomaly detection. The application of ML algorithms decision trees (DT) and random forest (RF) demonstrated dominant performance in identifying potential security threats, as evidenced by metrics accuracy, precision, recall, and F1 scores. This work not only enhances the security framework for WSNs but also contributes to the extensive field of network security, offering a robust analytical and predictive methodology for future cybersecurity initiatives. The advanced model can be deployed in other WSN and internet of things (IoT) based applications.  \nThis is an open access article under the CC BY-SA license.  \nAtdhe Buja  \nInternational Science Complex “Astana”Astana, Kazakhstan  \nEmail: [atdhe.buja@academyict.net](atdhe.buja@academyict.net)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nWireless sensor networks (WSNs) have appeared as a key technology in varied domains, from environmental monitoring to industrial automation, outstanding to their ability to collect data from remote and tough environments. However, the broad deployment of WSNs has introduced new challenges, in terms of security, due to the vulnerabilities of wireless communication and resource-constrained IoT sensor devices. The utilization of machine learning (ML) and the internet of things (IoT) has engaged the focus of researchers following it can process vast amounts of data capable of unraveling various industry challenges [1] . Outcome, assuring the integrity, confidentiality, and availability of data transmitted within WSNs has become a concern for researchers and cybersecurity practitioners as well [2] .  \nSeveral works have focused on approaches that have been proposed to detect and mitigate security threats in WSNs, ranging from cryptographic protocols to anomaly detection techniques. Through these, MLbased anomaly detection has gathered significant attention due to its capability to suit evolving threats and detect previously invisible attacks. Therefore, there is a need for a breakthrough force of AI-based models to mitigate cyber threats within IoT systems [3] . For instance, research work has investigated a secured framework to detect and stop data integrity attacks in WSNs in microgrids [4] . The breakout of the Mirai botnet uses IoT vulnerabilities and ruins numerous websites and domain name systems highlighting the need for a robust cybersecurity mechanism [5] .  \nML has been applied for anomaly detection systems in IoT systems and has been better [6] . Even IDS-based solutions present limitations in false positives [7] . Such research work provides a Hadoop-based framework to identify the malicious IoT traffic using a modified Tomek-link under-sampling integrated with automated Hyper-parameter tuning of machine learning classifiers [8] . So anomaly detection systems depend on a centralized management method to collect and process data generated by IoT devices, and a federated learning (FL) -base exhibits better accuracy","cbCaidCjbAaMVEDZ","https://ap.wps.com/l/cbCaidCjbAaMVEDZ","pdf",764609,1,11,"English","en",105,"# Introduction\n## Motivation and challenges in WSN security\n## Related work on ML and anomaly detection\n# Materials and Methods\n## Data preprocessing\n## Feature engineering\n## Machine learning models\n# Experiments and Results\n## Performance metrics for DT and RF\n## Comparative analysis\n# Discussion and Future Work","[{\"question\":\"What is the main goal of the proposed research?\",\"answer\":\"The research develops and evaluates an ML-based framework to detect network security incidents in wireless sensor networks and to determine suitable ML methods for identifying attacks accurately.\"},{\"question\":\"Which machine learning models are used for anomaly detection?\",\"answer\":\"The study applies decision tree (DT) and random forest (RF) models for identifying potential security threats through predictive anomaly detection.\"},{\"question\":\"How is the framework performance evaluated?\",\"answer\":\"Effectiveness is measured using accuracy, precision, recall, and F1 scores to quantify how well the models detect network security incidents.\"}]","Detecting Network Security Incidents in Wireless Sensor Networks Using Machine Learning - 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