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Edge-HetIoT defense against DDoS attack using learning techniques by Shalaka S. Mahadik; Pranav M. Pawar; Raja Muthalagu is a Computer Science article available to read on EtoBox.

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The heterogeneous nature of the internet-of-thing (IoT) is gaining popularity and, simultaneously, faces rising security issues. The distributed denial of service (DDoS) attack is the most significant security threat addressed in the research. The research proposes edge-heterogeneous IoT (HetIoT) centric defense IDS that aids the HetIoT infrastructure in detecting and blocking victim traffic near the network edge. The Edge-HetIoT defense IDS helps to address significant issues such as performance and security due to proximity to the local network. The research focuses on six learning techniques, including five machine learning (ML) classifiers, namely, ID3, NB, RF, LR, and AdaBoost, and the proposed deep learning (DL)-based hybrid model (i.e., CNN+LSTM). These learning techniques are trained and tested using the real-time benchmark-dataset CICDDoS2019 and consider binary and multiclass (14 classes) classification. The performance is analyzed and evaluated against six classifiers to determine which classification model performs best in detecting and classifying various DDoS attacks. The proposed DL-based hybrid model outperforms when compared against ID3, NB, RF, LR, and AdaBoost. T

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Author

Shalaka S. Mahadik; Pranav M. Pawar; Raja Muthalagu

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)