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

arXiv: https://arxiv.org/abs/2112.12546 · HTML: https://mlsanity.com/paper/2112.12546/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 22 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Professor Forcing: A New Algorithm for Training Recurrent Networks](https://mlsanity.com/paper/1610.09038/) (2016)
- [IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT](https://mlsanity.com/paper/1611.04880/) (2016)
- [Tiresias: Predicting Security Events Through Deep Learning](https://mlsanity.com/paper/1905.10328/) (2019)
- [IoT Device Fingerprint using Deep Learning](https://mlsanity.com/paper/1902.01926/) (2019)
- [How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups?](https://mlsanity.com/paper/2107.09577/) (2021)
- [On the Veracity of Cyber Intrusion Alerts Synthesized by Generative Adversarial Networks](https://mlsanity.com/paper/1908.01219/) (2019)

## Abstract (arXiv)

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 Things.