Edge-HetIoT defense against DDoS attack using learning techniques

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

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

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. The proposed DL-based hybrid model successfully detects and classifies MSSQL, NetBIOS, TFTP, NTP, Syn, and Portmap attacks with 100% precision, recall, and f1-score. The overall weighted average precision, recall, and f1-score for the proposed DL-based hybrid model are 92%, 89%, and 90%, respectively.

Original languageEnglish
Article number103347
JournalComputers and Security
Volume132
DOIs
StatePublished - Sep 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd

Keywords

  • CNN and LSTM
  • DDoS attack
  • Deep learning (DL)
  • Edge computing
  • Heterogeneous Internet-of-Things
  • Hybrid
  • Machine learning (ML)

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