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Abstract
Wireless Sensor Networks (WSNs) are vulnerable to various security threats, making it crucial to ensure their privacy and security. In this paper, we propose a Privacy-Aware and Secure Proof of WSN using Sequential Probability Ratio Test (SPRT) to detect intrusions and ensure the privacy of the data. The Existing method uses SPRT, a statistical test that allows for the detection of changes in the data while minimizing the number of false alarms. The proposed system is designed to be lightweight and energy-efficient, making it suitable for WSNs. The system is evaluated using extensive simulations, and the results demonstrate its effectiveness in detecting intrusions while maintaining a low false alarm rate. The proposed system is a promising approach for enhancing the security and privacy of WSNs, and it can be used in a variety of applications, such as monitoring environmental conditions, industrial process control, and home automation.
The proposed method aims to ensure the integrity of the data collected by the WSN while preserving the privacy of the users. The SPRT is a statistical hypothesis testing technique that can be used to detect anomalous behavior in the data collected by the WSN. The proposed method uses a two-level SPRT scheme to detect malicious attacks and ensure that the data collected by the WSN is trustworthy. Simulation results show that the proposed method is effective in detecting attacks while maintaining the privacy of the users.
The SPRT algorithm is used to detect and reject malicious data injections from adversaries, which can significantly improve the accuracy and reliability of the sensor readings. Furthermore, the proposed scheme incorporates a privacy-preserving mechanism to protect sensitive information from unauthorized access. Experimental results demonstrate that the proposed PAS-POW using SPRT outperforms existing approaches in terms of security and privacy while achieving a high detection rate and low false alarm rate.