## 3 Analysis of AI Techniques in SVAD
### Algorithms
#### 3.2.6 Analysis of Algorithms  
Statistics-based algorithms assume that normal behavior follows a certain statistical pattern, and any deviation from this pattern is considered an anomaly. They are simple and efficient and can detect anomalies in real-time without requiring a large amount of training data. However, they may not be effective at detecting novel anomalies or anomalies that do not follow a statistical pattern.  
Classification-based algorithms use machine learning techniques to classify behavior or events as normal or abnormal based on labeled training data. They can detect novel anomalies and adapt to changing environments with high accuracy. However, they require a large amount of training data, and the labeling process can be time-consuming and costly.  
Reconstruction-based algorithms reconstruct normal behavior or events and compare them to the actual behavior or events to detect subtle anomalies. They do not require labeled training data, but can be computationally expensive and may not be suitable for real-time anomaly detection.  
Prediction-based algorithms use machine learning techniques to predict future behavior or events based on past behavior or events. Any deviation from the predicted behavior or events is considered an anomaly. They can detect anomalies before they occur, which can be useful in preventing security threats or safety issues. However, they require a large amount of training data, and the accuracy of the predictions may decrease over time as the environment changes.  
In conclusion, the selection of the algorithm depends on the specific application and requirements. Statistics-based algorithms are simple and efficient, but may not detect novel anomalies. Classification-based algorithms have a high accuracy rate, but require a large amount of training data. Reconstruction-based algorithms can detect subtle anomalies, but can be computationally expensive. Prediction-based algorithms can detect anomalies before they occur, but require a large amount of training data, and the accuracy of predictions may decrease over time. Table 6 shows and overview of the algorithms.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Algorithms** & **Strengths** & **Weaknesses** \\ \hline \multirow{4}{*}{Statistics-based} & Generally, they are suitable for real-time applications as they are simple and computationally efficient. & They cannot detect subtle or complex anomalies, such as changes in spatial or temporal relationships. \\  & Subtle or complex anomalies, such as those involving spatial or temporal relationship changes, cannot be detected. & False alarms may also occur when the data distribution deviates from a Gaussian distribution. \\  & High-dimensional datasets can be handled, and robustness to noise can be exhibited. & Generally, they could be improved for real-time applications, being more computationally expensive than statistics-based algorithms. \\  & & A large amount of labeled training data is also required, which can be difficult and time-consuming. \\  & & Only the anomalies encountered in the training data can be detected, and new unseen anomalies cannot be detected. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Overview of algorithms.