The rise of alarming cyber breaches and cyber security attacks is causing the world to consider the security of our cyber space. A Security Operations Center (SOC) is a center where the security of a company is monitored to prevent cyber breaches. Security analysts in the SOC examine alerts that come from different devices and analyse what is causing these alerts. The SOC receives a high amount of false positive alerts and duplicates. Therefore security analysts will only react to alerts that seem critical. The problem is that analysts discard alerts that look like false positives but are actually genuine attacks. To tackle this problem of alert fatigue, related work has tried to implement machine learning models to reduce the number of alerts. However, the amount of workload that is reduced is still unsure. We argue that many machine learning models cluster the alerts that are easy for analysts to assess. This thesis compares traditional machine learning techniques with the state-of-the-art neural network DeepCASE and computes the amount of work that is reduced for the analysts. It also compares the machine learning models with a simple heuristic that reduces the duplicates in the dataset. We also enhance DeepCASE to find more sophisticated attacks. We show that using a simple heuristic is as good as using an advanced machine learning algorithm. We also show that using the enhanced version of a state-of-the-art neural network can find more sophisticated attacks.