Analysis and Protection of DDOS Attack Based on RSSP-II Protocol

Liang, Zhongdi; Hei, Xinhong; Wang, Yichuan; Ji, Wenjiang; Zhu, Lei; Zhang, Yefei

doi:10.1109/nana.2018.8648752

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…80 Other researchers have used Network Simulator (NS) to propose a methodology by updating the Global System for Mobile Communications for Railway (GSM-R) to Long-Term Evolution (LTE), 81 a new security threat, that is, power attack of the real-time system for detecting missing rail blocks, 82 and DDOS attack scenario based on RSSP-II protocol. 83 Table 2 shows summary of observation of cybersecurity papers within the railway based on Simulation and modelling.…”

Section: Critical Review Results By Research Themesmentioning

confidence: 99%

A review on cybersecurity in railways

Kour

Patwardhan

Thaduri

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part F:

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Digitalisation is transforming the railway globally. One of the major considerations in digital transformation of any industry including the railway is the increased exposure to cyberattacks. The railway industry is vulnerable to these attacks because since the number of digital items and also number of interfaces between digital and physical components in the railway systems keep increasing. Increased number of items and interfaces require new frameworks, concepts and architectures to ensure the railway system’s resilience with respect to cybersecurity challenges, such as lack of proactiveness, lack of holistic perspective and obsolescence of safety systems exposed to current and future cyber threats landscape. To this date, there are several works carried out in the literature that studied the cybersecurity aspects and its application on railway infrastructure. However, to develop and implement an appropriate roadmap to cybersecurity in railways, there is a need of describing emerging challenges, and approaches to deal with these challenges and the possibilities and benefits of these. Hence, the objective of this paper is to provide a systematic review and outline cybersecurity emerging trends and approaches, and also to identify possible solutions by querying literature, academic and industrial, for future directions. The authors of this paper conducted separate searches through four popular databases, that is, Google Scholar, Scopus, Web of Science and IEEE explore. For the screening process, authors have used keywords with Boolean operators and database filters and identified 90 articles most relevant to the study domain. The analysis of 90 articles shows that majority of the cybersecurity studies lies within the railways are conceptual and lags in application of Artificial Intelligence (AI) based security. Like other industries, it is very important that railways should also follow latest security technologies, trends and train their workforce for cyber hygiene since railways are already in digitalization transition mode.

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Section: Critical Review Results By Research Themesmentioning

confidence: 99%

A review on cybersecurity in railways

Kour

Patwardhan

Thaduri

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part F:

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“…Moreover, lightweight encryption [ 10 ] and authentication algorithms [ 11 ] provide end-to-end encrypted communication in railway networks. Finally, mitigation techniques against denial-of-service (DoS) attacks have also been discussed, e.g., in [ 12 ], where the authors established a three-level filtering technique to restore Railway Signal Safety Communication (RSSP-II) protocol communication under a distributed DoS attack.…”

Section: Introductionmentioning

confidence: 99%

Hunting Network Anomalies in a Railway Axle Counter System

Kuchař

Holasova

Pospíšil

et al. 2023

Sensors

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This paper presents a comprehensive investigation of machine learning-based intrusion detection methods to reveal cyber attacks in railway axle counting networks. In contrast to the state-of-the-art works, our experimental results are validated with testbed-based real-world axle counting components. Furthermore, we aimed to detect targeted attacks on axle counting systems, which have higher impacts than conventional network attacks. W present a comprehensive investigation of machine learning-based intrusion detection methods to reveal cyber attacks in railway axle counting networks. According to our findings, the proposed machine learning-based models were able to categorize six different network states (normal and under attack). The overall accuracy of the initial models was ca. 70–100% for the test data set in laboratory conditions. In operational conditions, the accuracy decreased to under 50%. To increase the accuracy, we introduce a novel input data-preprocessing method with the denoted gamma parameter. This increased the accuracy of the deep neural network model to 69.52% for six labels, 85.11% for five labels, and 92.02% for two labels. The gamma parameter also removed the dependence on the time series, enabled relevant classification of data in the real network, and increased the accuracy of the model in real operations. This parameter is influenced by simulated attacks and, thus, allows the classification of traffic into specified classes.

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