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Bimonthly Since 1986 |
ISSN 1004-9037
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Publication Details |
Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
Distributed by:
China: All Local Post Offices
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05 July-September 2023, Volume 38 Issue 4
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Abstract
Abstract:
In order to safeguard “Wireless Sensor Networks (WSNs),” this research suggests a new rule-based intrusion detection architecture that includes a number of security techniques. The framework employs a set of rules generated by the Random Forest algorithm, which outperforms other machine learning algorithms in terms of detection accuracy, false positive rate, and overhead. The generated rules are based on features extracted from WSNs data, including packet size, energy consumption, and hop count. The framework’s performance is evaluated using a set of metrics, demonstrating its effectiveness in identifying numerous attack kinds. Combining rule-based systems and machine learning approaches in this proposed framework can improve the accuracy and efficiency of intrusion detection mechanisms for secure WSNs. The proposed framework can be a valuable addition to the existing intrusion detection mechanisms for secure WSNs.
Keyword
Intrusion Detection System, Wireless Sensor Networks, Security Attacks, Machine Learning,Base Station,Security threat.
PDF Download (click here)
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