There are several techniques for anomaly detection, including:

Statistical methods: These methods involve using statistical measurements such as mean, standard deviation, and quantiles to identify data points that deviate significantly from the norm.

Machine learning-based methods: These methods involve using algorithms such as clustering, density estimation, and classification to identify patterns in the data and identify data points that do not conform to these patterns.

Time-series analysis: These methods are used to identify anomalies in time-series data such as stock prices, network traffic, and sensor data. They involve analyzing patterns over time and identifying data points that deviate significantly from these patterns.

To develop a system for anomaly detection, you would need to first decide on the specific technique or techniques that you want to use. Once you have decided on the technique, you would need to collect and preprocess the data, train the model, and evaluate its performance. Finally, you would need to deploy the model in a production environment and monitor its performance over time to ensure that it continues to detect anomalies effectively.

It's good to consider the context and the specific requirements of the application in which you want to use the anomaly detection system. This can help you choose the most appropriate techniques and fine-tune the system for optimal performance.