DNS<i>guard</i>: A Raspberry Pi-Based DDoS Mitigation on DNS Server in IoT Networks

Datta, Suvrima; Kotha, Aditya; Manohar, Kota; Udutalapally, Venkanna

doi:10.1109/lnet.2022.3215561

Cited by 3 publications

(1 citation statement)

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“…Trejo et al [36] used a visual platform and K-nearest neighbor (KNN) classifcation algorithm to detect DNS food attacks. Datta et al [37] detected DNS food attacks by monitoring the DNS query per second in IoT networks. Bushart and Rossow [38] used an anomaly-based low-pass flter to detect DNS food attacks.…”

Section: Related Workmentioning

confidence: 99%

Application‐Layer DDoS Attack Detection Using Explicit Duration Recurrent Network‐Based Application‐Layer Protocol Communication Models

Xie

Wang

Wen

et al. 2023

International Journal of Intelligent Systems

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Existing application-layer distributed denial of service (AL-DDoS) attack detection methods are mainly targeted at specific attacks and cannot effectively detect other types of AL-DDoS attacks. This study presents an application-layer protocol communication model for AL-DDoS attack detection, based on the explicit duration recurrent network (EDRN). The proposed method includes model training and AL-DDoS attack detection. In the AL-DDoS attack detection phase, the output of each observation sequence is updated in real time. The observation sequences are based on application-layer protocol keywords and time intervals between adjacent protocol keywords. Protocol keywords are extracted based on their identification using regular expressions. Experiments are conducted using datasets collected from a real campus network and the CICDDoS2019 dataset. The results of the experiments show that EDRN is superior to several popular recurrent neural networks in accuracy, F1, recall, and loss values. The proposed model achieves an accuracy of 0.996, F1 of 0.992, recall of 0.993, and loss of 0.041 in detecting HTTP DDoS attacks on the CICDDoS2019 dataset. The results further show that our model can effectively detect multiple types of AL-DDoS attacks. In a comparison test, the proposed method outperforms several state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%