ISSN (Online) : 2456 - 0774

Email : ijasret@gmail.com

ISSN (Online) 2456 - 0774

An Effective Framework for Packet Behavior ClassificationinHigh-Speed Connectionless Networks

Abstract

The surge in connectionless data transmissions, driven by IoT devices and streaming services, has intensifiedchallengessuchas packet loss, jitter, and delay. Conventional monitoring systems often fail to adapt to these dynamic conditions, highlightingtheneedfor intelligent Machine Learning (ML)-based approaches. This paper presents a comprehensive ML framework for evaluatingandpredicting packet behavior in connectionless networks using synthetically generated datasets. Two models—RandomForest andSupportVector Machine (SVM)—were developed and compared. The Random Forest classifier demonstrated superior performance, achieving98.33% precision, recall, and F1-score across all classes, while the SVM model yielded a slightly lower overall accuracyof 97%. Featureimportance analysis revealed packet loss rate and jitter as critical predictors influencing classification. Performance validationthroughconfusion matrices, ROC-AUC scores, and precision-recall curves confirmed the high reliability of both models, witheachattaininganAUC of 1.00. The findings establish Random Forest as the more robust and accurate choice for anomaly detection inunstablenetworkenvironments. Leveraging such ML techniques enables proactive monitoring, prediction, and optimization of networkperformance,ultimately enhancing the reliability and efficiency of connectionless communication systems. Keywords: Anomaly Detection, Connectionless Networks, Jitter, Machine Learning, Packet Loss, Precision-Recall, Reliability,ROCCurve, Streaming Services, Wireless Networks

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Paper Submission Open For August 2025
UGC indexed in (Old UGC) 2017
Last date for paper submission 31 August 2025
Deadline Submit the paper anytime.
Publication of Paper Within 15-30 Days after completing all the formalities
Publication Fees  Rs.5000 (UG student)
Publication Fees  Rs.6000 (PG student)