The unique characteristics of edge computing, such as limited resources and decentralized architecture, pose distinct challenges to traditional security measures. As the adoption of edge computing continues to proliferate across diverse domains, the security of edge devices becomes a paramount concern. This paper outlines a comprehensive approach for the detection of malicious activities (DDoS, Okiru and PartofHorizontalPortScan) on edge computing devices. The proposed solution leverages a combination of anomaly detection, Recurrent Neural Network (RNN) algorithm, and behaviour analysis tailored to the constraints of edge devices. By considering the specific context of edge environments, the model aims to distinguish between normal and malicious behaviour in edge computing, offering a proactive defence against emerging threats. Furthermore, the integration of threat intelligence feeds enhances the system`s ability to adapt to evolving attack vectors. The efficiency of the proposed solution ensures minimal impact on the performance of resource-constrained edge devices. This paperwork contributes to the ongoing efforts to fortify the security of edge computing ecosystems. By addressing the unique challenges associated with these devices, the proposed RNN algorithm provides a robust and adaptive framework for the detection and mitigation of malicious activities in edge computing, safeguarding the integrity and reliability of edge computing applications with an accuracy of 99.9%.

Edge Computing, Malicious packets, recurrent neural network, Random Forest Classifier

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