Intrusion Detection System based on Pattern Recognition using CNN

The Intrusion Detection System (IDS) is a security tool that uses pattern recognition algorithms to detect unusual activity in a network. IDS-based pattern recognition techniques search through network traffic for patterns that might indicate a security issue. IDS-based pattern recognition may find attacks of various types, such as malware infections, brute-force attacks, and insider threats. IDS-based pattern recognition techniques may be trained using methods based on machine learning, which allows the system to learn from previous attacks and improve its accuracy. Even though signature-based IDS analyzes network data for known attack patterns, anomaly-based IDS looks for anomalous network behaviour. IDS uses pattern recognition-based machine learning methods to enhance detection precision while decreasing false positives. The ability to identify previously unidentified attacks and the flexibility to shifting attack patterns are just two advantages of pattern recognition-based IDS. The 2 categories of pattern recognition-based IDS are signature-based and anomaly-based systems. Security staff can get alerts and notifications, that allows them to respond quickly to reduce security hazards. IDS-based pattern recognition systems rely on training data to learn what constitutes a security threat. If the training data is biased or incomplete, this can lead to inaccurate or incomplete detection. For example, if the training data only includes examples of attacks from a specific geographic region or industry sector, the system may not be able to detect attacks that occur outside of these parameters