Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Jomon, Ashvin Antony; Poonia, Ramesh Chandra Title A name given to the resource Real-Time Network Monitoring: Integrating Machine Learning and Custom Packet Sniffer Using Python Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1579 LNNS;pp.1-15 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-95-0626-2_1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-95-0626-2_1</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028357876?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028357876?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jomon A.A., Christ (Deemed to Be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; Poonia R.C., Christ (Deemed to Be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India Description An account of the resource The growth in network traffic and the increasing complexity of cyber threats necessitate robust systems for detecting anomalies that indicate security breaches. This research presents a methodology for finding anomalies in packets sent when the connection is established. It uses a machine learning model and a packet sniffer. It captures Transmission Control Protocol (TCP), User Datagram Protocol (UDP), IPv4, and Internet Control Message Protocol (ICMP) segments to predict if any anomalies are present (Sanders in Practical packet analysis: using wireshark to solve real-world network problems, No Starch Press, San Francisco, 2017). An unsupervised learning model is utilized. The presence of unlabeled data to enhance the real-time prediction using isolation forest model. The data collected by packet sniffer undergoes avoiding null values and encoding addresses, and thus an isolation forest is used so that it predicts if anomalies are present using binary trees. The performance is evaluated on the basis of metrics like accuracy, precision, and F1-score (Goutte and Gaussier in European conference on information retrieval, Springer, New York, 2005). The result illustrates the model is accurate in predicting whether anomalies are present. Future work is focused on enhancing the models capabilities with more protocols and an active defense mechanism. The study addresses real-world deployment challenges especially in heterogeneous environments like IoT-based networks. While isolation forest is getting high accuracy, future research could explore hybrid approaches combining traditional statistical methods with deep learning techniques for enhanced industry applications (Ahmed et al. in J Netw Comput Appl 60:1931, 2016). The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Isolation forest classification; Machine learning; Network anomaly detection; Packet sniffer; Real-time processing; Unsupervised learning Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981950625-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online