Improving the Ability of Intrusion Detection Systems by Reducing both False Positives and False Negatives

Today, Intrusion Detection Systems (IDS) keep networks safe by noticing and responding to risks on computers. Using the traditional approaches to detect intrusions frequently leads to a lot of wrong alarms and not picking up some actual signs of attack. Such problems wear out security analysts because they become less alert to actual threats which could lead to missing some. In our research, we introduced a straightforward way to help intrusion detection systems become more stable and improve how they work. We use state-ofthe- art machine learning, carefully measured thresholds and direct analysis of goals to support the system’s performance. Thanks to learning from the past and present network activity, this framework knows how to tell regular issues from real security problems. The main aim of this research is to make IDS systems more reliable by cutting down on fake alarms while still being able to spot real security violations.