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Forensic analysis on false data injection attack on IoT environment

Sharul Nizam, Saiful Amin and Ibrahim, Zul-Azri and Abdul Rahim, Fiza and Fadzil, Hafizuddin Shahril and Mohd. Abdullah, Haris Iskandar and Mustaffa, Muhammad Zulhusni (2021) Forensic analysis on false data injection attack on IoT environment. International Journal of Advanced Computer Science and Applications, 12 (10). pp. 265-271. ISSN 2158-107X


Official URL: http://dx.doi.org/10.14569/IJACSA.2021.0121029


False Data Injection Attack (FDIA) is an attack that could compromise Advanced Metering Infrastructure (AMI) devices where an attacker may mislead real power consumption by falsifying meter usage from end-users smart meters. Due to the rapid development of the Internet, cyber attackers are keen on exploiting domains such as finance, metering system, defense, healthcare, governance, etc. Securing IoT networks such as the electric power grid or water supply systems has emerged as a national and global priority because of many vulnerabilities found in this area and the impact of the attack through the internet of things (IoT) components. In this modern era, it is a compulsion for better awareness and improved methods to counter such attacks in these domains. This paper aims to study the impact of FDIA in AMI by performing data analysis from network traffic logs to identify digital forensic traces. An AMI testbed was designed and developed to produce the FDIA logs. Experimental results show that forensic traces can be found from the evidence logs collected through forensic analysis are sufficient to confirm the attack. Moreover, this study has produced a table of attributes for evidence collection when performing forensic investigation on FDIA in the AMI environment.

Item Type:Article
Uncontrolled Keywords:forensic analysis, internet of things (IoT), man in the middle (MITM)
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General) > T58.5-58.64 Information technology
Divisions:Razak School of Engineering and Advanced Technology
ID Code:95737
Deposited By: Yanti Mohd Shah
Deposited On:31 May 2022 21:18
Last Modified:31 May 2022 21:18

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