Hasan Muhammad Zulkifl, Hanapi Zurina Mohd, Zukarnain Zuriati Ahmad, Huyop Fahrul Hakim, Abdullah Muhammad Daniel Hafiz
Department of Communication Technology and Network,Faculty of Computer Science and Information Technology, Universiti Putra Malaysia(UPM), Serdang, Malaysia.
PLoS One. 2025 Mar 17;20(3):e0309532. doi: 10.1371/journal.pone.0309532. eCollection 2025.
In the realm of Wireless Sensor Networks (WSNs), the detection and mitigation of sinkhole attacks remain pivotal for ensuring network integrity and efficiency. This paper introduces SFlexCrypt, an innovative approach tailored to address these security challenges while optimizing energy consumption in WSNs. SFlexCrypt stands out by seamlessly integrating advanced machine learning algorithms to achieve high-precision detection and effective mitigation of sinkhole attacks. Employing a dataset from Contiki-Cooja, SFlexCrypt has been rigorously tested, demonstrating a detection accuracy of 100% and a mitigation rate of 97.31%. This remarkable performance not only bolsters network security but also significantly extends network longevity and reduces energy expenditure, crucial factors in the sustainability of WSNs. The study contributes substantially to the field of IoT security, offering a comprehensive and efficient framework for implementing Internet-based security strategies. The results affirm that SFlexCrypt is a robust solution, capable of enhancing the resilience of WSNs against sinkhole attacks while maintaining optimal energy efficiency.
在无线传感器网络（WSN）领域，检测和缓解黑洞攻击对于确保网络完整性和效率仍然至关重要。本文介绍了SFlexCrypt，这是一种创新方法，旨在应对这些安全挑战，同时优化无线传感器网络中的能耗。SFlexCrypt通过无缝集成先进的机器学习算法脱颖而出，以实现对黑洞攻击的高精度检测和有效缓解。利用来自Contiki-Cooja的数据集，SFlexCrypt经过了严格测试，检测准确率达到100%，缓解率为97.31%。这一卓越性能不仅增强了网络安全性，还显著延长了网络寿命并减少了能源消耗，这些都是无线传感器网络可持续性的关键因素。该研究对物联网安全领域做出了重大贡献，提供了一个全面而高效的框架来实施基于互联网的安全策略。结果证实，SFlexCrypt是一种强大的解决方案，能够增强无线传感器网络抵御黑洞攻击的能力，同时保持最佳能源效率。
