Analysing the Impact of Cyber-Threat to ICS and SCADA Systems

Kaura, Cheerag; Sindhwani, Nidhi; Chaudhary, Alka

doi:10.1109/mecon53876.2022.9752425

Cited by 5 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cyberattacks on industrial organizations are considered a perilous threat [ 4 ], as they have the potential to cause significant material losses and lead to disruption of the production cycle of the entire system [ 5 ]. They target, among others, industrial control and data collection systems (ICSs, SCADA) [ 6 , 7 ]. In addition, due to the sensitive information available to industrial organizations, they are usually an attractive target for attackers [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Semi-Self-Supervised Intrusion Detection System for Multilevel Industrial Cyber Protection

Zhao

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Industry 4.0 affects all components of the modern industry value chain. The accelerating use of the Internet and the convergence of industrial and operational networks constantly increase the need for secure industrial communication solutions. Therefore, “multilevel industrial cyber protection” is critical to Industry 4.0. In general, industrial protection refers to safeguarding information and data and the intellectual property rights of production processes related to the overall industry environment. The availability, integrity, and confidentiality of systems must be maintained. The goal challenge is the best possible protection from attacks and threats which create immediate financial damage and other risks in the industry (reputation, etc.). Based on the Defense-in-Depth strategy, a holistic, multilayered, and in-depth protection of industrial systems is developed in this paper. Specifically, a Semi-Self-Supervised Intrusion Detection System (S3IDS) is proposed, which combines advanced machine learning techniques for industrial data noise reduction to automate the discovery and separation of classes, which are essentially equivalent to cyber-related anomalies. As demonstrated by a mathematical simulation based on computational number theory and specifically on the concept of the single object, the proposed S3IDS learns to accurately reconstruct samples to predict the nature of an anomaly created directly by the industrial ecosystem.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Also, in the first quarter of 2021, in the city of Oldsmar in Florida, there was an attack on the government infrastructure responsible for the city's water supply. In essence, a remote attempt was made to change the mix of related chemicals with water disinfection, resulting in the mass poisoning of consumers [ 7 , 15 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%