Seeking frequent episodes in baseline data of In-Situ Decommissioning (ISD) Sensor Network Test Bed with temporal data mining tools

Sun, Zaijing; Duncan, Andrew J.; Kim, Y.; Zeigler, Kristine E.

doi:10.1016/j.pnucene.2020.103372

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…Users frequently install rogue and inferior IoT without authorization on secured networks. These devices merge into the network or replace the existing network units to get private information and data, breaching the network's security perimeter [11]. The IoT device known as a programmable controller was the target of the attackers.…”

Section: Security Concernsmentioning

confidence: 99%

Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications

Aldribi¹,

Singh²,

Bre駉sa³

2023

Computer Systems Science and Engineering

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Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications. Internet of Things (IoT), fog computing, edge computing, cloud computing, and the edge of things are the spine of all real-time and scalable applications. Conspicuously, this study proposed a novel framework for a real-time and scalable application that changes dynamically with time. In this study, IoT deployment is recommended for data acquisition. The Pre-Processing of data with local edge and fog nodes is implemented in this study. The thresholdoriented data classification method is deployed to improve the intrusion detection mechanism's performance. The employment of machine learningempowered intelligent algorithms in a distributed manner is implemented to enhance the overall response rate of the layered framework. The placement of respondent nodes near the framework's IoT layer minimizes the network's latency. For economic evaluation of the proposed framework with minimal efforts, EdgeCloudSim and FogNetSim++ simulation environments are deployed in this study. The experimental results confirm the robustness of the proposed system by its improvised threshold-oriented data classification and intrusion detection approach, improved response rate, and prediction mechanism. Moreover, the proposed layered framework provides a robust solution for real-time and scalable applications that changes dynamically with time.

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Section: Security Concernsmentioning

confidence: 99%

Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications

Aldribi¹,

Singh²,

Bre駉sa³

2023

Computer Systems Science and Engineering

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“…FEM approaches have been extended to explore new ways of seeking important patterns in new practical domains like cyber‐physical systems (CPS) such as intelligent cars (Liu et al, 2021) or for monitoring in situ decommissioning sensor networks (Biswal et al, 2022; Sun et al, 2020). These recent works maintain a well‐defined paradigm of prediction mechanism and mine frequent and abnormal episodes, respectively.…”

Section: Some Recent Applications Of Episode Miningmentioning

confidence: 99%

“…Another recent application that uses episode mining as an analysis technique consists of a set of sensors installed in a nuclear site called in situ decommissioning (ISD). Episode mining techniques are used to process the large amount of data generated by ISD sensors (Biswal et al, 2022; Sun et al, 2020) because the collected data is time‐specific (each action is associated with a timestamp that constitutes its occurrence). ISD sensors are installed in the aim of monitoring the nuclear site.…”

Section: Some Recent Applications Of Episode Miningmentioning

confidence: 99%

A survey of episode mining

Ouarem,

Nouioua,

Fournier‐Viger

2023

WIREs Data Min & Knowl

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Episode mining is a research area in data mining, where the aim is to discover interesting episodes, that is, subsequences of events, in an event sequence. The most popular episode‐mining task is frequent episode mining (FEM), which consists of identifying episodes that appear frequently in an event sequence, but this task has also been extended in various ways. It was shown that episode mining can reveal insightful patterns for numerous applications such as web stream analysis, network fault management, and cybersecurity, and that episodes can be useful for prediction. Episode mining is an active research area, and there have been numerous advances in the field over the last 25 years. However, due to the rapid evolution of the pattern mining field, there is no prior study that summarizes and gives a detailed overview of this field. The contribution of this article is to fill this gap by presenting an up‐to‐date survey that provides an introduction to episode mining and an overview of recent developments and research opportunities. This advanced review first gives an introduction to the field of episode mining and the first algorithms. Then, the main concepts used in these algorithms are explained. After that, several recent studies are reviewed that have addressed some limitations of these algorithms and proposed novel solutions to overcome them. Finally, the paper lists some possible extensions of the existing frameworks to mine more meaningful patterns and presents some possible orientations for future work that may contribute to the evolution of the episode mining field.This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Algorithmic Development > Association Rules Technologies > Association Rules

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“…Precisely, the presented framework is intended to provide real-time assessment and monitoring services for various stakeholders associated with an educational institution. Conspicuously, Temporal Data Mining (TDM) is used to extract valuable data instances for monitoring and reporting [33]. The extracted data segments are further analyzed in-depth to check the vulnerability areas of every stakeholder.…”

Section: Information Mining and Abstraction Layer (Imal)mentioning

confidence: 99%

IoT Inspired Intelligent Monitoring and Reporting Framework for Education 4.0

et al. 2021

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Education 4.0 contemplates the involvement of emerging technologies like IoT (Internet of Things), Fog and cloud computing. Ubiquitous real-time monitoring under the challenging environment of an educational institution is the key requirement for proper implementation of Education 4.0 around the world. Conspicuously, this research presents a novel monitoring and irregularity detection framework for educational institutions. In this research, acquisition of data while deploying IoT is being suggested in the comprehensive environs of the educational institutions. Pre-processing of acquired data for feature extraction is implemented using the Fog-Cloud nodes. Subsequently, the Temporal Chunk (TC)which is articulated with Temporal Data mining, is further employed to detect irregularities based on SoTL (Set of threshold limits)and HAA (Historical Adversity Approximation). Successively, Multi-Layered Bi-Directional Long Short Term Memory (M-Bi-LSTM) oriented irregularity prediction model is deployed. Furthermore, a fog-inspired alert generation and reporting module are employed for real-time reporting to notify the concerned stakeholder for in-time preventive action corresponding to predicted irregularity. To authenticate the proposed framework and to avoid the experimental implementation cost, the experimental simulations are performed. The experimental results verify that the proposed framework is capable to perform better in comparison to other contemporary decision-making methods for Delay Efficiency, Data Classification, Irregularity Prediction and System Stability. Moreover, the proposed framework can successfully estimate most of the irregularities in an educational environment and provide support for the proper implementation of Education 4.0 in a proficient and realistic manner.

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Seeking frequent episodes in baseline data of In-Situ Decommissioning (ISD) Sensor Network Test Bed with temporal data mining tools

Cited by 4 publications

References 4 publications

Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications

Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications

A survey of episode mining

IoT Inspired Intelligent Monitoring and Reporting Framework for Education 4.0

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