TY - JOUR
T1 - Enhancing internet of things intrusion detection using artificial intelligence
AU - Bar, Shachar
AU - Prasad, P. W.C.
AU - Sayeed, Md Shohel
N1 - Publisher Copyright:
© 2024 The Authors. Published by Tech Science Press.
PY - 2024
Y1 - 2024
N2 - Escalating cyber security threats and the increased use of Internet of Things (IoT) devices require utilisation of the latest technologies available to supply adequate protection. The aim of Intrusion Detection Systems (IDS) is to prevent malicious attacks that corrupt operations and interrupt data flow, which might have significant impact on critical industries and infrastructure. This research examines existing IDS, based on Artificial Intelligence (AI) for IoT devices, methods, and techniques. The contribution of this study consists of identification of the most effective IDS systems in terms of accuracy, precision, recall and F1-score; this research also considers training time. Results demonstrate that Graph Neural Networks (GNN) have several benefits over other traditional AI frameworks through their ability to achieve in excess of 99% accuracy in a relatively short training time, while also capable of learning from network traffic the inherent characteristics of different cyber-attacks. These findings identify the GNN (a Deep Learning AI method) as the most efficient IDS system. The novelty of this research lies also in the linking between high yielding AI-based IDS algorithms and the AI-based learning approach for data privacy protection. This research recommends Federated Learning (FL) as the AI training model, which increases data privacy protection and reduces network data flow, resulting in a more secure and efficient IDS solution.
AB - Escalating cyber security threats and the increased use of Internet of Things (IoT) devices require utilisation of the latest technologies available to supply adequate protection. The aim of Intrusion Detection Systems (IDS) is to prevent malicious attacks that corrupt operations and interrupt data flow, which might have significant impact on critical industries and infrastructure. This research examines existing IDS, based on Artificial Intelligence (AI) for IoT devices, methods, and techniques. The contribution of this study consists of identification of the most effective IDS systems in terms of accuracy, precision, recall and F1-score; this research also considers training time. Results demonstrate that Graph Neural Networks (GNN) have several benefits over other traditional AI frameworks through their ability to achieve in excess of 99% accuracy in a relatively short training time, while also capable of learning from network traffic the inherent characteristics of different cyber-attacks. These findings identify the GNN (a Deep Learning AI method) as the most efficient IDS system. The novelty of this research lies also in the linking between high yielding AI-based IDS algorithms and the AI-based learning approach for data privacy protection. This research recommends Federated Learning (FL) as the AI training model, which increases data privacy protection and reduces network data flow, resulting in a more secure and efficient IDS solution.
KW - Anomaly detection
KW - artificial intelligence
KW - cyber security
KW - data privacy
KW - deep learning
KW - federated learning
KW - industrial internet of things
KW - internet of things
KW - intrusion detection system
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85206676468&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85206676468&partnerID=8YFLogxK
U2 - 10.32604/cmc.2024.053861
DO - 10.32604/cmc.2024.053861
M3 - Review article
AN - SCOPUS:85206676468
SN - 1546-2218
VL - 81
SP - 1
EP - 23
JO - Computers, Materials and Continua
JF - Computers, Materials and Continua
IS - 1
ER -