Collaborative adversary nodes learning on the logs of IoT devices in an IoT network

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Abstract: Artificial Intelligence (AI) development has encouraged many new research areas, including AI-enabled Internet of Things (IoT) networks. AI analytics and intelligent paradigms greatly improve learning efficiency and accuracy. Applying these learning paradigms to network scenarios provides technical advantages of new networking solutions. In this paper, we propose an improved approach to IoT security from a data perspective. The network traffic of IoT devices can be analyzed using AI techniques. The Adversary Learning (AdLIoTLog) model is proposed using a Recurrent Neural Network (RNN) with an attention mechanism on sequences of network events in the network traffic. We define network events as a sequence of the time series packets of protocols captured in the log. We have considered different packets, such as TCP packets, UDP packets, and HTTP packets, in the network log to make the algorithm robust. The distributed IoT devices can collaborate to cripple our world, which is extending to the Internet of Intelligence. The time series packets are converted into structured data by removing noise and adding timestamps. The resulting data set is trained using RNN and can detect collaborating node pairs. We used the BLEU score to evaluate the model performance. Our results show that the predicting performance of the AdLIoTLog model trained by our method degrades by 3-4% in the presence of an attack compared to the scenario when the network is not under attack. AdLIoTLog can detect adversaries because when adversaries are present, the model gets duped by collaborative events and predicts the next event incorrectly rather than the correct event. We conclude that AI can provide ubiquitous learning for the new generation of the Internet of Things.