Secure localization techniques in wireless sensor networks against routing attacks based on hybrid machine learning models

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

doi:10.1016/j.aej.2023.09.064

Cited by 11 publications

(4 citation statements)

References 52 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%

“…However, the suggested approach did not analyze the computational overhead or resource consumption associated with implementing the system. Detection of various attacks and safe positioning in wireless sensor networks were suggested by Gebremariam GG et al 27 utilizing Hybrid DL (HDL) approaches and Sage for ideal distance, site, and data transfer. The purpose was to identify the finest distance among sensors and the finest location of sensors.…”

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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“…Gebremariam et al [23] delved into the dynamic field of researching the identification and localization of malicious nodes within WSNs, a pursuit that holds significant potential for extending the network's lifespan and enhancing its overall value. The utilization of anchor nodes, whose positions are known, facilitates informed estimations of unidentified nodes' placements.…”

Section: Methodsmentioning

confidence: 99%

Location Protection Technology for Wireless Sensor Networks Based on Differential Privacy using DSRNN-MOA

Yalin Nie

2024

jes

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Wireless Sensor Networks (WSNs) play a pivotal role in modern data-driven applications, yet concerns persist regarding the privacy and security of sensitive location information. The attributes of Wireless Sensor Networks (WSNs) make them susceptible to eavesdropping, enabling attackers to intercept data packets across single or multiple communication links. This interception allows for the extraction of sensitive data from various sensor information, presenting a significant challenge to location privacy. Consequently, it becomes crucial to implement effective measures for safeguarding the privacy of training sample data when utilizing WSNs. In this manuscript, Dynamically Stabilized Recurrent Neural Network (DSRNN) optimized with Mother Optimization Algorithm (DSRNN-MOA) is proposed. Initially data is taken from WSN dataset. Afterward the data is fed to Variational Bayesian-based Maximum Correntropy Cubature Kalman Filtering (VBMCCKF) based pre-processing process. The pre-processing output is provided to the Dynamically Stabilized Recurrent Neural Network to enhance source location protection while addressing challenges related to recurrent network stability and gradient issues. The learnable parameters of the DSRNN is optimized using MOA. The proposed strategy, LPWSN-DSRNN-MOA, is implemented in MATLAB, and its effectiveness is assessed using a number of performance evaluation measures, including ROC analysis, accuracy, precision, recall, f1-score, mean squad error, and recall. The proposed LPWSN-DSRNN-MOA method shows the highest accuracy of 98%, precision of 99%, specificity of 98% and F1-score of 99% while comparing other existing methods such as Location Protection for Wireless Sensor Networks based on Artificial Neural Network(LPWSN-ANN), Location Protection for Wireless Sensor Networks based on Deep Neural Network (LPWSN-DNN), and Location Protection for Wireless Sensor Networks based on Machine Learning (LPWSN-ML) respectively.

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“…This technique, based on Semi-Supervised Learning, provides robustness to various types of attacks through continuous learning of scenario characteristics. Similar logic was also used in [127] to detect routing-type threats, such as Wormhole and Sybil, using a hybrid ML approach optimized for distance, location, and data communication. The same attacks were also discussed in [128], where innovative detection algorithms based on the concept of the highest-rank common ancestor were introduced and validated.…”

Section: Authenticitymentioning

confidence: 99%

Trustworthy Localization in IoT Networks: A Survey of Localization Techniques, Threats, and Mitigation

Pettorru,

Pilloni,

Martalò

2024

Sensors

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The Internet of Things (IoT) has revolutionized the world, connecting billions of devices that offer assistance in various aspects of users’ daily lives. Context-aware IoT applications exploit real-time environmental, user-specific, or situational data to dynamically adapt to users’ needs, offering tailored experiences. In particular, Location-Based Services (LBS) exploit geographical information to adapt to environmental settings or provide recommendations based on users’ and nodes’ positions, thus delivering efficient and personalized services. To this end, there is growing interest in developing IoT localization systems within the scientific community. In addition, due to the sensitivity and privacy inherent to precise location information, LBS introduce new security challenges. To ensure a more secure and trustworthy system, researchers are studying how to prevent vulnerabilities and mitigate risks from the early design stages of LBS-empowered IoT applications. The goal of this study is to carry out an in-depth examination of localization techniques for IoT, with an emphasis on both the signal-processing design and security aspects. The investigation focuses primarily on active radio localization techniques, classifying them into range-based and range-free algorithms, while also exploring hybrid approaches. Next, security considerations are explored in depth, examining the main attacks for each localization technique and linking them to the most interesting solutions proposed in the literature. By highlighting advances, analyzing challenges, and providing solutions, the survey aims to guide researchers in navigating the complex IoT localization landscape.

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Secure localization techniques in wireless sensor networks against routing attacks based on hybrid machine learning models

Cited by 11 publications

References 52 publications

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

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

Location Protection Technology for Wireless Sensor Networks Based on Differential Privacy using DSRNN-MOA

Trustworthy Localization in IoT Networks: A Survey of Localization Techniques, Threats, and Mitigation

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