A semi-supervised deep auto-encoder based intrusion detection for iot

Samir Fenanir, Fouzi Semchedine, Saad Harous, Abderrahmane Baadache

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

The main problem facing the Internet of Things (IoT) today is the identification of attacks due to the constrained nature of IoT devices. To address this problem, we present a lightweight intrusion detection system (IDS) which acts as a second line of defense allowing the reinforcement of the access control mechanism. The proposed method is based on a Deep Auto-Encoder (DAE), which learns the pattern of a normal process using only the features of the user’s normal behavior. Whatever deviation from the expected behavior is considered an anomaly. We validate our approach using two well-known network datasets, namely, the NSL-KDD and CIDDS-001. The experimental results demonstrate that our approach provides promising results in terms of accuracy, detection rate and false alarm rate.

Original languageEnglish
Pages (from-to)569-577
Number of pages9
JournalIngenierie des Systemes d'Information
Volume25
Issue number5
DOIs
Publication statusPublished - Nov 2020

Keywords

  • Access control
  • Anomaly detection
  • Autoencoder
  • Intrusion detection system
  • Machine learning

ASJC Scopus subject areas

  • Information Systems

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