A Novel Approach to Packet Dropping and Malicious Attack Detection using Ensemble Techniques
Cross Validation; Gradient Boosting; KNN; Packet Dropping Detection; Stack Classifier; SVM
Packet-dropping attacks interrupt data transfer while damaging security protocols, which create a threat to wireless Sensor Networks and Mobile Ad Hoc networks. This paper examines packet-dropping detection methods as well as security attack identification since these threats represent significant risks to networks such as Wireless sensor networks and Mobile Ad Hoc Networks. The research paper utilized a dataset from Kaggle for network traffic analysis, which classified packets through their behaviors as either abnormal or normal. The detection employed a stacking classifier with logistic regression as the meta-classifier and Support Vector Machine, Gradient Boosting, and K-Nearest Neighbour as its main constituents. The analysis model showed high detection rates for packet-dropping incidents, reaching 93.5%, and for malicious attacks, reaching 98.2%, based on the experimental test results. The obtained data shows that stacking models show stable reliability levels above traditional approaches. Ensemble learning proves effective for discovering cyber threats through results that reduce the number of incorrect detections. The stacking classifier functions as a dependable framework for developing security measures required to protect computer networks from modern-day threats. 2025 IEEE.
Martin, Joseph John; George, Jossy P; Alapatt, Bosco Paul
Proceedings of 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2025;pp.1458-1463
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICICV64824.2025.11085510 https://www.scopus.com/pages/publications/105013070858?origin=resultslist
Martin J.J., CHRIST (Deemed to Be University), India; George J.P., Christ University, India; Alapatt B.P., Christ University, India