Energy Analysis-Based Cyber Attack Detection by IoT with Artificial Intelligence in a Sustainable Smart City

D. Prabakar, M. Sundarrajan, R. Manikandan, N. Z. Jhanjhi (), Mehedi Masud and Abdulmajeed Alqhatani
Additional contact information
D. Prabakar: Department of Data Science and Business Systems, School of Computing, SRM Institute of Science and Technology, Kattankulathur Campus, Chennai 603203, India
M. Sundarrajan: Department of CSE, SRM Institute of Science and Technology, Ramapuram Campus, Chennai 600089, India
R. Manikandan: School of Computing, SASTRA Deemed University, Thanjavur 613401, India
N. Z. Jhanjhi: School of Computer Science, SCS, Taylor’s University, Subang Jaya 47500, Malaysia
Mehedi Masud: Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia
Abdulmajeed Alqhatani: Department of Information Systems, College of Computer Science & Information Systems, Najran University, Najran 61441, Saudi Arabia

Sustainability, 2023, vol. 15, issue 7, 1-14

Abstract: Cybersecurity continues to be a major issue for all industries engaged in digital activity given the cyclical surge in security incidents. Since more Internet of Things (IoT) devices are being used in homes, offices, transportation, healthcare, and other venues, malicious attacks are happening more frequently. Since distance between IoT as well as fog devices is closer than distance between IoT devices as well as the cloud, attacks can be quickly detected by integrating fog computing into IoT. Due to the vast amount of data produced by IoT devices, ML is commonly employed for attack detection. This research proposes novel technique in cybersecurity-based network traffic analysis and malicious attack detection using IoT artificial intelligence techniques for a sustainable smart city. A traffic analysis has been carried out using a kernel quadratic vector discriminant machine which enhances the data transmission by reducing network traffic. This enhances energy efficiency with reduced traffic. Then, the malicious attack detection is carried out using adversarial Bayesian belief networks. The experimental analysis has been carried out in terms of throughput, data traffic analysis, end-end delay, packet delivery ratio, energy efficiency, and QoS. The proposed technique attained a throughput of 98%, data traffic analysis of 74%, end-end delay of 45%, packet delivery ratio of 92%, energy efficiency of 92%, and QoS of 79%.

Keywords: cyber-attack; security; IoT devices; network traffic analysis; malicious attack detection; artificial intelligence; sustainable smart city (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/7/6031/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/7/6031/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:7:p:6031-:d:1112210

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().