Collaborative adversary nodes learning paper acceptance
Congratulations to Dr Nagender! His paper, Collaborative adversary nodes learning on the logs of IoT devices in an IoT network accepted
Dr Nagender Aneja’s paper, Collaborative adversary nodes learning on the logs of IoT devices in an IoT network, has been accepted for the 2022 14th International Communication Systems and Networks and Workshops, COMSNETS. His paper is published at its conference proceedings.
“In this paper, Dr Nagender and his team proposed 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. They define network events as a sequence of the time series packets of protocols captured in the log. They 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.”
“Their 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 Things.” - https://ieeexplore.ieee.org/abstract/document/9668602
Related Links:
Dr Nagender Aneja Profile Link
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Universiti Brunei Darussalam
Jalan Tungku Link, BE1410
Negara Brunei Darussalam
office.sds@ubd.edu.bn