Security Improvement Technique for Distributed Control System (DCS) and Supervisory Control‐Data Acquisition (SCADA) Using Blockchain at Dark Web Platform

Rajawat, Anand Singh; Rawat, Romil; Barhanpurkar, Kanishk

doi:10.1002/9781119795667.ch14

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…Attacks on IT system networks result in congestion or data leakage, but attacks on ICS networks may inflict damage to the physical infrastructure in addition to data leakage. The SCADA smart grid systems [27,28] are protected by a variety of machine learning and deep learning-based technologies that have been developed in the past. However, they continue to have issues with overfitting, a confounding model, an ineffective attack detection rate, and a complex system architecture.…”

Section: Problem Statementmentioning

confidence: 99%

A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security

Rabie

Balachandran²,

Shitharth

2022

Electronics

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Smart grids are complex cyber-physical systems that incorporate smart devices’ communication capabilities into the grid to enable remote management and the control of power systems. However, this integration reveals numerous SCADA system flaws, which could compromise security goals and pose severe cyber threats to the smart grid. In conventional works, various attack detection methodologies are developed to strengthen the security of smart grid SCADA systems. However, they have several issues with complexity, slow training speed, time consumption, and inaccurate prediction outcomes. The purpose of this work is to develop a novel security framework for protecting smart grid SCADA systems against harmful network vulnerabilities or intrusions. Therefore, the proposed work is motivated to develop an intelligent meta-heuristic-based Artificial Intelligence (AI) mechanism for securing IoT-SCADA systems. The proposed framework includes the stages of dataset normalization, Zaire Ebola Search Optimization (ZESO), and Deep Random Kernel Forest Classification (DRKFC). First, the original benchmarking datasets are normalized based on content characterization and category transformation during preprocessing. After that, the ZESO algorithm is deployed to select the most relevant features for increasing the training speed and accuracy of attack detection. Moreover, the DRKFC technique accurately categorizes the normal and attacking data flows based on the optimized feature set. During the evaluation, the performance of the proposed ZESO-DRKFC method is validated and compared in terms of accuracy, detection rate, f1-score, and false acceptance rate. According to the results, it is observed that the ZESO-DRKFC mechanism outperforms other techniques with high accuracy (99%) by precisely spotting intrusions in the smart grid systems.

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Section: Problem Statementmentioning

confidence: 99%

A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security

Rabie

Balachandran²,

Shitharth

2022

Electronics

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“…The DF approach depends on the investigators’ experience [ 24 , 25 ]. Some of the challenges related to the existing DF are presented as trustworthiness, integrity, provenance improvement, scalability, and availability [ 26 , 27 ]. In terms of trustworthiness, the system is supposed to check the trust if insider threats to the blockchain environment improve the trust of evidence [ 28 ].…”

Section: Related Workmentioning

confidence: 99%

NLP-Based Digital Forensic Analysis for Online Social Network Based on System Security

Shahbazi

Byun

2022

IJERPH

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Social media evidence is the new topic in digital forensics. If social media information is correctly explored, there will be significant support for investigating various offenses. Exploring social media information to give the government potential proof of a crime is not an easy task. Digital forensic investigation is based on natural language processing (NLP) techniques and the blockchain framework proposed in this process. The main reason for using NLP in this process is for data collection analysis, representations of every phase, vectorization phase, feature selection, and classifier evaluation. Applying a blockchain technique in this system secures the data information to avoid hacking and any network attack. The system’s potential is demonstrated by using a real-world dataset.

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