Abstract:

Using an intrusion detection system (IDS) is one method to address network security issues and guarantee that there are no attacks. IDS operate only on attack behaviour patterns that have been defined in the database since it is based on two models that use signature-based detection. Next is the anomaly-based IDS model. It works by spotting unusual network activity in normal situations, although this method produces a lot of false positive results. Several previous studies have shown that the IDS strategy in conjunction with machine learning techniques can produce highly accurate results. The initial step in using the machine learning technique is to pre-process the One way to deal with network security concerns and ensure that there are no attacks is to implement an intrusion detection system (IDS). IDS is based on two models that use signature-based detection, which restricts its operation to the attack behaviour patterns that have been specified in the database. The anomaly-based IDS model comes next. It functions by identifying anomalous network activity under typical circumstances, but this approach generates a large number of false positive results. Numerous earlier research have demonstrated that the IDS strategy combined with machine learning approaches can yield findings with a high degree of accuracy. When applying the machine learning technique, the first thing that has to be done is pre-process the feature/attribute selection to maximize

Description:

.