articleInternet of ThingsSep 12, 2023HYBRID OA

Enhancing IoT network security through deep learning-powered Intrusion Detection System

SAShahid Allah BakhshMAMuhammad Almas KhanFAFawad AhmedMSMohammed S. AlshehriHAHisham Ali

National University of Sciences and Technology · National University of Computer and Emerging Sciences · +2 more institutions

Indexed incrossref

Abstract

The rapid growth of the Internet of Things (IoT) has brought about a global concern for the security of interconnected devices and networks. This necessitates the use of efficient Intrusion Detection System (IDS) to mitigate cyber threats. Deep learning (DL) techniques provides a promising approach to effectively detect irregularities in network traffic, enhancing IoT network security and reducing cyber threats. In this paper, DL-based IDS is proposed using Feed Forward Neural Networks (FFNN), Long Short-Term Memory (LSTM), and Random Neural Networks (RandNN) to protect IoT networks from cyberattacks. Each DL model has its potential benefit as reported in this paper. For example, the FFNN can handle complex…

Citation impact

198
total citations
FWCI
37.98
Percentile
100%
References
68
Citations per year

Authors

6

Topics & keywords

Topics
Keywords
  • Intrusion detection system
  • Internet of Things
  • Computer science
  • Computer security
  • Intrusion prevention system
  • Deep learning
  • Network security
  • Artificial intelligence
No related works found for this paper.

Funding