Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

IoT Network Traffic Analysis with Deep Learning

Mei Liu, Leon Yang

TL;DR

The paper addresses anomaly detection in IoT network traffic by advocating deep learning as a scalable, often unsupervised approach capable of identifying Unknown anomalies in near real-time. It surveys deep anomaly detection (DAD) methods, categorizes them by objective, and demonstrates a CNN+LSTM ensemble augmented with KNN and RF for robust detection on the KDD Cup 99 intrusion dataset. The study provides detailed implementation and training settings, showing that the ensemble achieves over 98% accuracy and outperforms individual models like AE and GAN, with favorable precision and recall. The findings suggest practical, high-performance DL strategies for IoT security and point to future work on improving interpretability and adapting to diverse IoT traffic scenarios in real-world deployments.

Abstract

As IoT networks become more complex and generate massive amounts of dynamic data, it is difficult to monitor and detect anomalies using traditional statistical methods and machine learning methods. Deep learning algorithms can process and learn from large amounts of data and can also be trained using unsupervised learning techniques, meaning they don't require labelled data to detect anomalies. This makes it possible to detect new and unknown anomalies that may not have been detected before. Also, deep learning algorithms can be automated and highly scalable; thereby, they can run continuously in the backend and make it achievable to monitor large IoT networks instantly. In this work, we conduct a literature review on the most recent works using deep learning techniques and implement a model using ensemble techniques on the KDD Cup 99 dataset. The experimental results showcase the impressive performance of our deep anomaly detection model, achieving an accuracy of over 98\%.

IoT Network Traffic Analysis with Deep Learning

TL;DR

The paper addresses anomaly detection in IoT network traffic by advocating deep learning as a scalable, often unsupervised approach capable of identifying Unknown anomalies in near real-time. It surveys deep anomaly detection (DAD) methods, categorizes them by objective, and demonstrates a CNN+LSTM ensemble augmented with KNN and RF for robust detection on the KDD Cup 99 intrusion dataset. The study provides detailed implementation and training settings, showing that the ensemble achieves over 98% accuracy and outperforms individual models like AE and GAN, with favorable precision and recall. The findings suggest practical, high-performance DL strategies for IoT security and point to future work on improving interpretability and adapting to diverse IoT traffic scenarios in real-world deployments.

Abstract

As IoT networks become more complex and generate massive amounts of dynamic data, it is difficult to monitor and detect anomalies using traditional statistical methods and machine learning methods. Deep learning algorithms can process and learn from large amounts of data and can also be trained using unsupervised learning techniques, meaning they don't require labelled data to detect anomalies. This makes it possible to detect new and unknown anomalies that may not have been detected before. Also, deep learning algorithms can be automated and highly scalable; thereby, they can run continuously in the backend and make it achievable to monitor large IoT networks instantly. In this work, we conduct a literature review on the most recent works using deep learning techniques and implement a model using ensemble techniques on the KDD Cup 99 dataset. The experimental results showcase the impressive performance of our deep anomaly detection model, achieving an accuracy of over 98\%.
Paper Structure (15 sections, 4 equations, 3 figures, 3 tables)

This paper contains 15 sections, 4 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Research Approach & Methodology
  4. Popular Deep Learning Models for DAD
  5. GAN
  6. CNN
  7. LSTM
  8. AutoEncoder
  9. Experiments
  10. Dataset
  11. Experiment Settings
  12. Data Pre-processing
  13. Model Implementation
  14. Results and Evaluations
  15. Conclusion

Figures (3)

  • Figure 1: A simple example of anomalies in 2d data space as the red cross points while the blue solid points are the normal data.
  • Figure 2: The illustration of the experimental process in our work
  • Figure 3: The flowchart of CNN+LSTM ensemble model.