Summary

Now that you have finished this chapter, we hope you understand what fraud, phishing, and spam detection are. We also explored how to construct simple phishing detection systems with traditional machine learning algorithms. These examples are also very easy to extend to other deep learning-based methods. On the other hand, we hope you now understand the data imbalance issues in several AI applications and why we need collaborative anomaly detection systems to consider the performance and privacy issues. These topics were covered in this chapter. We exemplified federated learning as an example of collaborative anomaly detection in this chapter as well. It is also very easy to extend this to MPC-based or TEE-based collaborative anomaly detection.

In the next chapter, we will focus on user authentication and access control.