DDoS attack detection in IoT environment using optimized Elman recurrent neural networks based on chaotic bacterial colony optimization
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Software
Link
https://link.springer.com/content/pdf/10.1007/s10586-023-04187-4.pdf
Reference58 articles.
1. Sinthuja, M., Suthendran, K.: DDoS attack detection using enhanced long-short term memory with hybrid machine learning algorithms. In: 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), pp. 1213–1218, IEEE (2022)
2. Wani, A., Revathi, S.: DDoS detection and alleviation in IoT using SDN (SDIoT-DDoS-DA). J. Inst. Eng. 101(2), 117–128 (2020)
3. Roopak, M., Tian, G.Y., Chambers, J.: Multi-objective-based feature selection for DDoS attack detection in IoT networks. IET Netw. 9(3), 120–127 (2020)
4. Vishwakarma, R., Jain, A.K.: A survey of DDoS attacking techniques and defence mechanisms in the IoT network. Telecommun. Syst. 73(1), 3–25 (2020)
5. Jia, Y., Zhong, F., Alrawais, A., Gong, B., Cheng, X.: Flowguard: An intelligent edge defense mechanism against IoT DDoS attacks. IEEE Internet Things J. 7(10), 9552–9562 (2020)
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