@inproceedings{a94484ec2c794ec5b14aea220bdd0f64,
title = "Optimized Edge-cCCN Based Model for the Detection of DDoS Attack in IoT Environment",
abstract = "In the context of the Internet of Things (IoT), safeguarding against Distributed Denial of Service (DDoS) attacks is critical. This paper introduces an Optimized Edge-cCNN (Convolutional Neural Network) Model designed for robust DDoS detection in IoT environments. The model employs two specialized CNN layers to identify distinct DDoS attack types. To enhance its performance, we utilize the Cuckoo Search algorithm to fine-tune hyperparameters effectively. Our approach demonstrates superior accuracy compared to existing methods while remaining lightweight, making it suitable for resource-constrained edge devices. Through rigorous evaluation, our model exhibits its effectiveness in real-time DDoS threat mitigation. The Optimized Edge-cCNN Model presents an innovative solution for enhancing IoT security, combining deep learning and optimization techniques to combat evolving DDoS attacks effectively.",
keywords = "CNN, Cuckoo Search, DDoS, Edge Computing, IoT",
author = "Gupta, {Brij B.} and Akshat Gaurav and Chui, {Kwok Tai} and Varsha Arya",
note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 7th International Conference on Edge Computing, EDGE 2023 ; Conference date: 17-12-2023 Through 18-12-2023",
year = "2024",
doi = "10.1007/978-3-031-51826-3_2",
language = "English",
isbn = "9783031518256",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "14--23",
editor = "Jun Feng and Frank Jiang and Min Luo and Liang-Jie Zhang",
booktitle = "Edge Computing – EDGE 2023 - 7th International Conference, Held as Part of the Services Conference Federation, SCF 2023, Proceedings",
}