Rail Internet of Things: An Architectural Platform and Assured Requirements Model

Eiza, Mahmoud Hashem; Randles, Martin; Johnson, Princy; Shone, Nathan; Pang, Jennifer M.; Bhih, Amhmed

doi:10.1109/cit/iucc/dasc/picom.2015.52

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…IoT systems cannot be protected using traditional security techniques because of their computational limitations and inherent resources [26][27][28][29]. ML approaches in IDSs identify unknown and known attacks on IoT devices in real time [30,31]. IoT protocols and network structure are not relevant to an IDS proposed in [32].…”

Section: Related Workmentioning

confidence: 99%

A Novel Anomaly Detection System on the Internet of Railways Using Extended Neural Networks

et al. 2022

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The Internet of Railways (IoR) network is made up of a variety of sensors, actuators, network layers, and communication systems that work together to build a railway system. The IoR’s success depends on effective communication. A network of railways uses a variety of protocols to share and transmit information amongst each other. Because of the widespread usage of wireless technology on trains, the entire system is susceptible to hacks. These hacks could lead to harmful behavior on the Internet of Railways if they spread sensitive data to an infected network or a fake user. For the previous few years, spotting IoR attacks has been incredibly challenging. To detect malicious intrusions, models based on machine learning and deep learning must still contend with the problem of selecting features. k-means clustering has been used for feature scoring and ranking because of this. To categorize attacks in two datasets, the Internet of Railways and the University of New South Wales, we employed a new neural network model, the extended neural network (ENN). Accuracy and precision were among the model’s strengths. According to our proposed ENN model, the feature-scoring technique performed well. The most accurate models in dataset 1 (UNSW-NB15) were based on deep neural networks (DNNs) (92.2%), long short-term memory LSTM (90.9%), and ENN (99.7%). To categorize attacks, the second dataset (IOR dataset) yielded the highest accuracy (99.3%) for ENN, followed by CNN (87%), LSTM (89%), and DNN (82.3%).

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Section: Related Workmentioning

confidence: 99%

A Novel Anomaly Detection System on the Internet of Railways Using Extended Neural Networks

et al. 2022

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“…The Internet of Things (IoT) currently receiving much research attention and effort from the academia as well as the industry player to provide the backbone of modern and smart societies (Eiza et al, 2015). Therefore, most of the organizations together with government sectors hunger for leverage the latest IoT solutions so that they can harness the IoT capabilities and extend their reach well beyond their traditional parameters like usual perception amongst citizens.…”

Section: Introductionmentioning

confidence: 99%

The Internet of Things Readiness in Public Organization: Descriptive Analysis

et.al¹

2021

TURCOMAT

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The Internet of Things (IoT) is a system of interrelated computing devices. IoT involves mechanical and digital machines, objects, animals or people that are indicated with unique identifiers and the ability to transfer data over a network without requiring human-to-human as well as human-to-computer interaction. Government of Malaysia in 2015 has introduced the National Internet of Things Strategic Road Map as a way to provide the backbone of modern and smart cities as well to sustain the economic growth. However, the factors influencing IoT readiness in Malaysian organization needs to be further investigated. The purpose of this study is to investigate, identify and propose the readiness factors of IoT implementation. A survey involving a distribution of questionnaire to 30 respondents to get a descriptive indication coupled with interviews with 5 key expert employees from relevant agencies were executed. As a result, there are five factors being proposed and validated descriptively to influence the readiness of implementing IoT in Malaysia. These factors are Human Perspective, Technology Perspective, Governance and Management Perspective, Security Perspective and Policy and Law Perspective. For future work, this study suggest to include more participants using power statistics, hypothetical model testing and expand the questionnaire to other organization in order to develop a more comprehensive result ofIoT readiness in Malaysia.

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Rail Safety System Based on Temperature Calibration and Track Crack Detection

Subhashini,

Shanthini

2024

Lecture Notes in Networks and Systems

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Rail Internet of Things: An Architectural Platform and Assured Requirements Model

Cited by 8 publications

References 14 publications

A Novel Anomaly Detection System on the Internet of Railways Using Extended Neural Networks

A Novel Anomaly Detection System on the Internet of Railways Using Extended Neural Networks

The Internet of Things Readiness in Public Organization: Descriptive Analysis

Rail Safety System Based on Temperature Calibration and Track Crack Detection

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