Artificial Intelligence (AI) development has encouraged many new research
areas, including AI-enabled Internet of Things (IoT) network. AI analytics and
intelligent paradigms greatly improve learning efficiency and accuracy.
Applying these learning paradigms to network scenarios provide technical
advantages of new networking solutions. In this paper, we propose an improved
approach for IoT security from data perspective. The network traffic of IoT
devices can be analyzed using AI techniques. The Adversary Learning (AdLIoTLog)
model is proposed using Recurrent Neural Network (RNN) with 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 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 Internet of
Intelligence. The time series packets are converted into structured data by
removing noise and adding timestamps. The resulting data set is trained by RNN
and can detect the node pairs collaborating with each other. 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 attack in comparison to the scenario when the network is not
under attack. AdLIoTLog can detect adversaries because when adversaries are
present the model gets duped by the collaborative events and therefore predicts
the next event with a biased event rather than a benign event. We conclude that
AI can provision ubiquitous learning for the new generation of Internet of

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Author Of this post: <a href="">Sandhya Aneja</a>, <a href="">Melanie Ang Xuan En</a>, <a href="">Nagender Aneja</a>

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