Time dependent anomaly detection system for smart environment using probabilistic timed automaton

Venkatraman, S.; Muthusamy, P.; Balusa, Bhanuchander; Jayasankar, T.; Kavithaa, G; Sekar, K. R.; Bharatiraja, C.

doi:10.1007/s12652-020-02769-3

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…In parallel, another group of researchers has focused on using IoT to improve security in smart electrical networks. A relevant study in this context is the work of [15], which explores how IoT devices can provide an additional layer of anomaly detection by constantly monitoring the status of power grid assets [16]. This approach aligns with the growing trend of using IoT devices to collect real-time data and enable faster responses to anomalies or threats.…”

Section: Review Of Similar Workmentioning

confidence: 99%

IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities

Villegas-Ch,

Govea,

Jaramillo-Alcazar

2023

Applied Sciences

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This study addresses anomaly detection in smart city environments driven by the Internet of Things. In these cities, digital interconnection and the extensive network of sensors generate enormous amounts of data, which are essential to improving citizens’ efficiency and quality of life. However, this data may also contain strange events that require early detection to ensure the proper functioning of urban systems. For this, anomaly detection models are explored to identify unusual patterns in urban data. The work focuses on the applicability and effectiveness of these models in different urban scenarios supported by the Internet of Things. Furthermore, its performance is evaluated by comparing it with existing approaches, and its advantages and limitations are analyzed. The results show that the proposed models, including Isolation Forest, recurrent neural network, and variational autoencoder, are highly effective in detecting anomalies in urban data. This work contributes to the field of smart cities by improving the safety and efficiency of urban systems. Early detection of anomalies makes it possible to prevent unplanned interruptions, ensure the safety of citizens, and maintain the integrity of urban systems. Furthermore, the relevance of this work in the existing literature and its importance for the evolution of smart cities supported by the Internet of Things are highlighted.

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Section: Review Of Similar Workmentioning

confidence: 99%

IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities

Villegas-Ch,

Govea,

Jaramillo-Alcazar

2023

Applied Sciences

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“…Time-series data [43,44,45,46,47,48,49,50] Smart city [51,52,53,54] Monitoring machinery health [55,5] Robotics and manufacturing [56,57,58] Detection applications for IoT sensors [59,60] Other industrial or manufacturing [61,62,63,64] Surveillance and video [65,66,67] General-purpose frameworks [68,69,70] Network and communication frameworks [71,72,73,74] User security and privacy frameworks [75,76,77] Other frameworks [78,79] Network traffic in IoT [80,81,82] Device and infrastructure security [83,21,84,85,86,87] Data transport security [88,…”

Section: Application Category Referencementioning

confidence: 99%

“…GNN monitoring is performed to monitor nodes and devices connected to those nodes using the underlying graph structure. To protect private data from home devices, Venkatraman et al [86] used a Probabilistic Timed Automaton (PTA) to model the activities of smart devices, keeping intruders from stealing confidential data. Furthermore, Said et al [87] use the support vector machine (SVM) classifier to help preserve data accuracy while avoiding sending false alerts to hospital infrastructure.…”

Section: Security Applications For Iot Devices and Infrastructurementioning

confidence: 99%

IoT anomaly detection methods and applications: A survey

2022

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“…Meanwhile, IDS is recommended as a highly important security solution for newly-developed online web-based applications in relation to smart cities and Internet of Things (IoT) atmosphere. In general, IDS-based systems commence a greater quantity of unsuitable and fake alarms, whenever abnormal performances are identified [8]. If the activated fake alarm rate is too high, I t tend to diminish the performance of IDS in contrast to cyber-attacks and makes the job of security analysts completely challenging.…”

Section: Introductionmentioning

confidence: 99%

Privacy Preserving Blockchain with Optimal Deep Learning Model for Smart Cities

Kumar¹,

Mahilraj²,

Swathi³

et al. 2022

Computers, Materials &Amp; Continua

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Recently, smart cities have emerged as an effective approach to deliver high-quality services to the people through adaptive optimization of the available resources. Despite the advantages of smart cities, security remains a huge challenge to be overcome. Simultaneously, Intrusion Detection System (IDS) is the most proficient tool to accomplish security in this scenario. Besides, blockchain exhibits significance in promoting smart city designing, due to its effective characteristics like immutability, transparency, and decentralization. In order to address the security problems in smart cities, the current study designs a Privacy Preserving Secure Framework using Blockchain with Optimal Deep Learning (PPSF-BODL) model. The proposed PPSF-BODL model includes the collection of primary data using sensing tools. Besides, z-score normalization is also utilized to transform the actual data into useful format. Besides, Chameleon Swarm Optimization (CSO) with Attention Based Bidirectional Long Short Term Memory (ABiLSTM) model is employed for detection and classification of intrusions. CSO is employed for optimal hyperparameter tuning of ABiLSTM model. At the same time, Blockchain (BC) is utilized for secure transmission of the data to cloud server. This cloud server is a decentralized, distributed, and open digital ledger that is employed to store the transactions in different methods. A detailed experimentation of the proposed PPSF-BODL model was conducted on benchmark dataset and the outcomes established the supremacy of the proposed PPSF-BODL model over recent approaches with a maximum accuracy of 97.46%.

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Time dependent anomaly detection system for smart environment using probabilistic timed automaton

Cited by 3 publications

References 14 publications

IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities

IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities

IoT anomaly detection methods and applications: A survey

Privacy Preserving Blockchain with Optimal Deep Learning Model for Smart Cities

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