Autoregressive integrated moving average model–based secure data aggregation for wireless sensor networks

Song, Hongtao; Sui, Shanshan; Han, Qilong; Hui, Zhang; Yang, Zhong-Jian

doi:10.1177/1550147720912958

Cited by 21 publications

(7 citation statements)

References 29 publications

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“…A set of equations is proposed to perform predictive analysis using specific algorithms and models, such as ARIMA (Autoregressive Integrated Moving Average) [ 34 ], to assess and quantify relevant metrics in the field. These processes will facilitate decision-making and increase situational awareness within threat intelligence.…”

Section: Design and Implementation Of The Security Framework For Assu...mentioning

confidence: 99%

Enhancing Cyber-Resilience for Small and Medium-Sized Organizations with Prescriptive Malware Analysis, Detection and Response

Ilca,

Lucian,

Balan

2023

Sensors

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In this study, the methodology of cyber-resilience in small and medium-sized organizations (SMEs) is investigated, and a comprehensive solution utilizing prescriptive malware analysis, detection and response using open-source solutions is proposed for detecting new emerging threats. By leveraging open-source solutions and software, a system specifically designed for SMEs with up to 250 employees is developed, focusing on the detection of new threats. Through extensive testing and validation, as well as efficient algorithms and techniques for anomaly detection, safety, and security, the effectiveness of the approach in enhancing SMEs’ cyber-defense capabilities and bolstering their overall cyber-resilience is demonstrated. The findings highlight the practicality and scalability of utilizing open-source resources to address the unique cybersecurity challenges faced by SMEs. The proposed system combines advanced malware analysis techniques with real-time threat intelligence feeds to identify and analyze malicious activities within SME networks. By employing machine-learning algorithms and behavior-based analysis, the system can effectively detect and classify sophisticated malware strains, including those previously unseen. To evaluate the system’s effectiveness, extensive testing and validation were conducted using real-world datasets and scenarios. The results demonstrate significant improvements in malware detection rates, with the system successfully identifying emerging threats that traditional security measures often miss. The proposed system represents a practical and scalable solution using containerized applications that can be readily deployed by SMEs seeking to enhance their cyber-defense capabilities.

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Section: Design and Implementation Of The Security Framework For Assu...mentioning

confidence: 99%

Enhancing Cyber-Resilience for Small and Medium-Sized Organizations with Prescriptive Malware Analysis, Detection and Response

Ilca,

Lucian,

Balan

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…When processing a large amount of data and data in the same direction, the wireless sensor network under ZigBee technology can effectively avoid the collision time of multiple data. It can resend the data after detecting the data transmission problem [ 36 ]. Therefore, ZigBee technology has a strong application capability, and the system also guarantees the security of data transmission.…”

Section: Analysis Of Embedded Cloud Computing Data Monitoring System ...mentioning

confidence: 99%

Energy Optimization of Wireless Sensor Embedded Cloud Computing Data Monitoring System in 6G Environment

Yang

Zhang

Liu

et al. 2023

Sensors

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With the construction and development of modern and smart cities, people’s lives are becoming more intelligent and diversified. Surveillance systems increasingly play an active role in target tracking, vehicle identification, traffic management, etc. In the 6G network environment, facing the massive and large-scale data information in the monitoring system, it is difficult for the ordinary processing platform to meet this computing demand. This paper provides a data governance solution based on a 6G environment. The shortcomings of critical technologies in wireless sensor networks are addressed through ZigBee energy optimization to address the shortage of energy supply and high energy consumption in the practical application of wireless sensor networks. At the same time, this improved routing algorithm is combined with embedded cloud computing to optimize the monitoring system and achieve efficient data processing. The ZigBee-optimized wireless sensor network consumes less energy in practice and also increases the service life of the network, as proven by research and experiments. This optimized data monitoring system ensures data security and reliability.

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“…Moreover, authors have reported that proper Cluster Head selection is also considered as promising technique which can minimize the energy depletion. The authors in [30] have focused on improving the data aggregation process along with security concerns in the Wireless Sensor Networks (WSN). This scheme uses a novel secure data aggregation scheme where autoregressive model is used for time series analysis.…”

Section: Data Aggregationmentioning

confidence: 99%

An Efficient Routing Mechanism for Node Localization, Cluster Based Approach and Data Aggregation to Extend WSN Lifetime

2022

IJIES

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The Last decade the demand for wireless communication has increased massively. Wireless Sensor Networks (WSN) have turned out as a potential solution for various real-time applications namely environmental monitoring, health and military applications. These networks follow the random placement strategy hence, identifying the location of sensor nodes is a tedious task. Moreover, these networks suffer from various issues such as limited power resources. This work, focusses on the sensor node localization and network lifetime enhancement of Wireless Sensor Networks (WSN). In localization, security is an important issue, so a trust-based model to secure localization is investigated in this work. Further, energy aware clustering and Cluster Head (CH) selection has been undertaken to prolong the lifetime of the network. Finally, a data aggregation model which decreases the network energy depletion by discarding the redundant data is presented. The outcome of the model which is proposed has been assessed and expressed in the form of localization error, energy depletion and alive node. The comparative analysis shows that the localization error for varied anchor nodes is obtained as 1.51, 2.28, 2.52 and 1.25 using existing techniques DMA (Distance Mapping Algorithm), MDS-Map (Multidimensional Scaling), DV-Hop (Distance Vector-Hop) and Proposed Approach, respectively. Similarly, the average energy consumption is obtained as 1.02, 1.67, 1.90 and 0.82 using DMA (Distance Mapping Algorithm), MDS-Map (Multidimensional Scaling), DV-Hop (Distance Vector-Hop) and Proposed Hybrid Approach. The comparative analysis shows that proposed hybrid approach of localization, CH (Cluster Head) formation and aggregation attains better performance when compared with existing techniques in WSN(Wireless Sensor Networks).

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Autoregressive integrated moving average model–based secure data aggregation for wireless sensor networks

Cited by 21 publications

References 29 publications

Enhancing Cyber-Resilience for Small and Medium-Sized Organizations with Prescriptive Malware Analysis, Detection and Response

Enhancing Cyber-Resilience for Small and Medium-Sized Organizations with Prescriptive Malware Analysis, Detection and Response

Energy Optimization of Wireless Sensor Embedded Cloud Computing Data Monitoring System in 6G Environment

An Efficient Routing Mechanism for Node Localization, Cluster Based Approach and Data Aggregation to Extend WSN Lifetime

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