Optimizing trust, Cloud Environments Fuzzy Neural Network, Intrusion Detection System
Keywords:
Cloud Computing, Intrusion Detection System, Neuro-Fuzzy, K-Means, Denial of Services, Cloud SecurityAbstract
In the dynamic landscape of cloud computing, ensuring the security and integrity of services is paramount. This article introduces a novel approach to cloud intrusion detection by leveraging the synergies of fuzzy logic and neural networks. The proposed Fuzzy Neural Network Aided Cloud Intrusion Detection System (FNN-CIDS) integrates the adaptability of fuzzy systems with the learning capabilities of neural networks to enhance the detection accuracy of malicious activities within cloud environments. The system is designed to discern subtle patterns indicative of intrusion attempts, thereby fortifying the defense mechanisms for trusted services hosted in the cloud. The article presents the conceptual framework of FNN-CIDS, detailing the integration of fuzzy logic for rule-based inference and neural networks for pattern recognition. Experimental results demonstrate the system's efficacy in identifying diverse intrusion scenarios while minimizing false positives. This research provides a promising path for improving the reliability of cloud computing infrastructures and advances strong security frameworks for cloud-based applications. In this sense, the research effort provides a trust evaluation system to determine the reliability of cloud services and an intrusion detection system to guarantee intrusion-free cloud services. the construction of a cloud intrusion detection system using a neuro-fuzzy based self-constructing clustering algorithm. The performance of this method has been compared to other well-known clustering methods in the field of cloud intrusion detection using result analysis.
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