Open AccessMalicious Node Detection in Smart Grid Networks
Author(s) -
Faisal Y Al Yahmadi,
Muhammad Ejaz Ahmed
Publication year - 2021
Publication title -
computer science and information technology ( cs and it )
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5121/csit.2021.110716
Subject(s) - computer science , smart grid , computer security , support vector machine , constant false alarm rate , malware , node (physics) , cyber attack , electricity , block (permutation group theory) , table (database) , computer network , real time computing , data mining , engineering , artificial intelligence , geometry , mathematics , structural engineering , electrical engineering
Many countries around the world are implementing smart grids and smart meters. Malicious users that have moderate level of computer knowledge can manipulate smart meters and launch cyber-attacks. This poses cyber threats to network operators and government security. In order to reduce the number of electricity theft cases, companies need to develop preventive and protective methods to minimize the losses from this issue. In this paper, we propose a model based on software that detects malicious nodes in a smart grid network. The model collects data (electricity consumption/electric bill) from the nodes and compares it with previously obtained data. Support Vector Machine (SVM) model is implemented to classify nodes into good or malicious nodes by (high dimensional) giving the statues of 1 for good nodes and status of -1 for malicious (abnormal) nodes. The detection model also displays the network graphically as well as the data table. Moreover, this model displays the detection error in each cycle. It has a very low false alarm rate (2%) and a high detection rate as high as (98%). Future developments can trace the attack origin to eliminate or block the attack source minimizing losses before human control arrives.