Machine learning-based intrusion detection system in advanced metering infrastructure

Smart meters are quickly being introduced to households all over the world. Consumers will benefit significantly from Advanced Metering Infrastructure (AMI). This high-tech equipment, however, is vulnerable to serious threats. To detect attacks, many authors recommend using Intrusion Detection Systems (IDSs). An IDS use a classifier to distinguish between known and unknown threats. It is challenging to choose the proper IDS category, however, as no single category can accurately identify all types of attacks. A smart grid's autonomous meter reading system is built on Advanced Metering Infrastructure (AMI). It blends traditional utility operations and asset management methods with wide-ranging elements of machinery, such as (a) automated metering; (b) communication networks; and (c) data management systems, to enhance the grid's interface with customers and utility providers. AMI enables smart meters and utilities to communicate in a two-way mode, regarding data such as power usage, price, upgraded firmware, remote disconnection, issue or outage detection, and exclusion notifications. One of the most significant obstacles to AMI's global acceptance is its security. We investigated the topic of detecting malicious assaults in AMI in this thesis. By the time the national Smart meter goes live, cybersecurity experts predict that four major categories of attacks will be prevalent. Data attacks affect the smart meter to cause erroneous decisions/actions, by attempting to adversely inject, change, or remove data or control commands in the networking ow. By examining energy use statistics, a privacy attack attempts to learn or infer customers' confidential information. Smart meters record power to use data numerous times each hour, and the precise data may quickly disclose consumers' private, physical activity. A network availability attack can take the form of a denial of service (DoS) attack, which will cause a delay or loss in data connection. This thesis emphasis on attacks that threaten the availability and integrity of the entire system. We describe a hierarchical, distributed approach for Smart meters, which merges the feature engineering of preprocessing with machine learning classifiers, and ne tunes the hyper-parameters, using voting classifiers to improve detection accuracy and processing time. Different types of classifiers, decision trees (DT), random forests (RF), and Naive Bayes (NB) classifiers tend to be used in this thesis. AWS Sage-Maker, AWS Sage-Maker Autopilot, and Google Colab were used to test the overall performance of the system. This thesis contributes to existing research by demonstrating how much AMI attack detection enhancing strategies, and different types of IDS techniques in AMI networks. According to analyze the output, three combined classifiers outperformed single classifiers in terms of accuracy and processing time.

Name thesis:degree_name

Master of Applied Science (MASc)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Engineering - Electronic Systems

Grantor dc:publisher

Faculty of Graduate Studies and Research, University of Regina

Year dc:date.issued

2022

Author dc:creator

• Rahman, MD Syeedur

Advisor dc:contributor.advisor

• Al-Anbagi, Irfan

Committee member dc:contributor.committeemember

• Wagner, Doug

Language dc:language.iso

en

DOI

https://doi.org/10.82465/4590

OAI identifier oai:identifier

oai:uregina.scholaris.ca:10294/15536