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Title	Analysis Of Dos Attacks And Detection Techniques In Smart City Systems
ID_Doc	9148
Authors	Munnee R.; Armoogum V.; Armoogum S.
Year	2024
Published	4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024
DOI	http://dx.doi.org/10.1109/ICMNWC63764.2024.10872046
Abstract	In smart city systems, Denial of Service (DoS) attacks threaten critical infrastructure reliability. This paper analyzes DoS attack simulations and evaluates four detection models: Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN-LSTM). RF emerged as the most effective model, achieving over 99% accuracy, precision, recall, and F1-scores across all attack intensities, making it ideal for real-time scenarios due to its efficiency and balanced resource usage. While SVM performed well in low to medium-intensity attacks, its high CPU usage limits real-time applicability. CNN achieved 94 % accuracy in spatial anomaly detection but exhibited reduced recall under high-intensity attacks. The RNN-LSTM excelled in high-intensity scenarios, recording the highest recall (97%), but its extended training time and memory usage pose challenges. These findings underscore the need for hybrid approaches like CNN-LSTM to optimize accuracy and efficiency in smart city cybersecurity. © 2024 IEEE.
Author Keywords	CNN; DoS attack detection; random forest; RNN-LSTM; smart city; SVM

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