Enhancing cyber-physical system security: a data-driven approach for attack detection using DNN

K.Vijay Baskar; M.Poojitha; P.Akhila; P.Sushma

Author(s)

K.Vijay Baskar School of computer science and engineering, Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammguda, Hyderabad, Telangana, India

M.Poojitha School of computer science and engineering, Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammguda, Hyderabad, Telangana, India

P.Akhila School of computer science and engineering, Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammguda, Hyderabad, Telangana, India

P.Sushma School of computer science and engineering, Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammguda, Hyderabad, Telangana, India

Abstract

Cyber-physical systems enabled by the Internet of Things (IoT), such as industrial equipment and operational IT, facilitate the transmission and reception of data via the internet. This equipment will be equipped with sensors to monitor its status and transmit data to a centralized server via an internet connection. Occasionally, malevolent people may compromise or infiltrate such sensors, subsequently manipulating their data, which will then be sent to a centralized server, resulting in erroneous actions being executed. Many countries' equipment and production systems have failed due to erroneous data, prompting the development of numerous algorithms to detect attacks. However, these algorithms are hindered by data imbalance, where one class may contain a substantial number of records (e.g., NORMAL records), while the other class, such as attacks, may have only a few records. This imbalance can result in the failure of detection algorithms to predict accurately. To address data imbalance, current methods included OVER and UNDER sampling, which create new records exclusively for the minority class. To address this problem, we are using a unique approach that does not use any under- or oversampling techniques. The suggested methodology has two components. An autoencoder will be trained on an unbalanced dataset to extract features, which will then be used to train a decision tree algorithm for predicting labels for both known and unknown assaults. The decision tree is trained on a reduced set of features derived from the principal component analysis (PCA) technique. Deep Neural Networks (DNN) are trained on both known and undiscovered assaults. If any records have an attack signature, the DNN will detect and ascribe the corresponding attack label or class.

Publication Details

Page(s) 761-767

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 05, Year: 2024

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