Secure Intrusion Detection System for Hierarchically Distributed Wireless Sensor Networks

Gebremariam, Gebrekiros Gebreyesus; Panda, Jeebananda; Indu, S.

doi:10.1109/iciera53202.2021.9726753

Cited by 4 publications

(5 citation statements)

References 31 publications

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“…Several existing approaches are considered for comparison, including EPK‐DNN, 16 IMOPSO, 17 AIDS‐HML, 19 PSO+KNN, 20 PSO+ANN, 20 FSACM, 21 LightGBM‐DAE, 22 HTF‐DS, 24 DHMLM, 25 SGM, 26 and HDL 27 using NADS datasets.…”

Section: Discussion On Simulation Outcomesmentioning

confidence: 99%

“…Nonetheless, the loss rate and training time of this approach were high. Advanced Intrusion Detection Systems with Hybrid ML (AIDS‐HML) in wireless sensor networks were developed by Gebremariam GG et al 19 utilizing hybrid ML techniques to identify and classify attacks. The system involves trained and evaluated forecast models within the classifiers.…”

Section: Reviewmentioning

confidence: 99%

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NHAPMAD: Novel hybrid approaches for privacy‐preserved multiple attacks detection

Seth,

Sharma,

Ratmele

2024

Concurrency and Computation

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SummaryDetecting anomalies is crucial for maintaining security in Wireless Sensor Networks (WSNs), as they are susceptible to various attacks that compromise nodes and yield inaccurate outcomes. Conventional attack detection approaches face challenges like high false positives, vulnerability to complex attacks, and limited adaptability to changing attacks due to predefined patterns. Additionally, the computational strain on resource‐constrained nodes hampers network efficiency, demanding innovative and resilient security solutions. Therefore, this research work introduces Novel Hybrid Approaches for Privacy‐preserved Multiple Attacks Detection (NHAPMAD) framework in both network attack detection system and host attack detection system. This topology bolsters the network's defense against various types of attacks, thereby ensuring the preservation of sensitive data privacy. Federated learning has been incorporated into the NHAPMAD framework to protect user privacy in WSNs. The introduced framework provides better anomaly detection and reduces false alarms. Additionally, the introduced model protects sensitive data, ensuring the security and integrity of the entire WSN, promoting a stable and dependable operating environment. The introduced method exhibits superior performance across various metrics like overall accuracy (99.6%), F1‐score (99.59%), recall (99.47%), precision (99.63%), detection rate (99.5%), processing time (0.0009 s), etc. compared to traditional approaches, marking a significant advancement in the realm of WSN security.

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Section: Discussion On Simulation Outcomesmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

NHAPMAD: Novel hybrid approaches for privacy‐preserved multiple attacks detection

Seth,

Sharma,

Ratmele

2024

Concurrency and Computation

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“…Feature importance analysis is crucial for understanding the impact of different input features on the prediction of barrier counts [ 7 ]. We employ the Random Forest Regressor to extract and rank the importance of features to identify the most influential features and obtain valuable insights for feature selection and model interpretability.…”

Section: Proposed Workmentioning

confidence: 99%

“…The research of Gebremariam, Panda, and Indu [ 6 ] emphasises the value of combining machine learning with hierarchically designed WSNs and promotes accurate intrusion detection. Collectively, these studies underline the increasing emphasis on leveraging advanced methodologies to strengthen WSN security against sophisticated threats [ 7 ]. More of the existing research works are discussed in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

Optimising barrier placement for intrusion detection and prevention in WSNs

Reddy,

Kaza,

Anisha

et al. 2024

PLoS ONE

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This research addresses the pressing challenge of intrusion detection and prevention in Wireless Sensor Networks (WSNs), offering an innovative and comprehensive approach. The research leverages Support Vector Regression (SVR) models to predict the number of barriers necessary for effective intrusion detection and prevention while optimising their strategic placement. The paper employs the Ant Colony Optimization (ACO) algorithm to enhance the precision of barrier placement and resource allocation. The integrated approach combines SVR predictive modelling with ACO-based optimisation, contributing to advancing adaptive security solutions for WSNs. Feature ranking highlights the critical influence of barrier count attributes, and regularisation techniques are applied to enhance model robustness. Importantly, the results reveal substantial percentage improvements in model accuracy metrics: a 4835.71% reduction in Mean Squared Error (MSE) for ACO-SVR1, an 862.08% improvement in Mean Absolute Error (MAE) for ACO-SVR1, and an 86.29% enhancement in R-squared (R2) for ACO-SVR1. ACO-SVR2 has a 2202.85% reduction in MSE, a 733.98% improvement in MAE, and a 54.03% enhancement in R-squared. These considerable improvements verify the method’s effectiveness in enhancing WSNs, ensuring reliability and resilience in critical infrastructure. The paper concludes with a performance comparison and emphasises the remarkable efficacy of regularisation. It also underscores the practicality of precise barrier count estimation and optimised barrier placement, enhancing the security and resilience of WSNs against potential threats.

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“…In recent years, wireless communication technology has been used extensively in both the military and civilian sectors [1], however, due to the open and broadcast characteristics of wireless channel, the inherent security problem of information leakage in wireless communication is caused [2], the stability of the wireless network signal transmission is relatively low. At the same time, with the increasing development of attack methods, various security problems in wireless communication will become more and more prominent [3], wireless security is confronted with the complexity of the secret key distribution process [4], putting forward new and higher requirements for the security of current wireless communication.…”

Section: Introductionmentioning

confidence: 99%

Physical Layer Key Generation Method Based on SVD Pre-processing

Liu

Guo²,

Ju³

2023

JCSANDM

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Environmental factors such as channel noise and hardware fingerprints affect the encryption effect of physical layer key generation techniques, resulting in low consistency of generated keys. Feature pre-processing is a common means of improving consistency of keys. However, most of the existing feature pre-processing algorithms improve key consistency by sacrificing key generation rate, which is not very usable. Therefore, it is proposed a physical layer key generation method based on SVD pre-processing. This method uses the SVD feature processing algorithm to pre-process the channel features extracted from both sides of the communication before quantization, in order to simultaneously improve key consistency and key generation rate. The simulation results show that when the channel SNR is greater than 10 dB, the BER of the SVD scheme is significantly lower compared to the scheme without pre-processing and the DCT and PCA pre-processing schemes; when the SNR is greater than 20 dB, the SVD scheme KGR can reach a level of 10bit/s, which is significantly higher than the other three schemes. The results show that this scheme can significantly increase the key generation rate while effectively improving key consistency.

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Secure Intrusion Detection System for Hierarchically Distributed Wireless Sensor Networks

Cited by 4 publications

References 31 publications

NHAPMAD: Novel hybrid approaches for privacy‐preserved multiple attacks detection

NHAPMAD: Novel hybrid approaches for privacy‐preserved multiple attacks detection

Optimising barrier placement for intrusion detection and prevention in WSNs

Physical Layer Key Generation Method Based on SVD Pre-processing

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