Securing the Digital Perimeter Intrusion Detection for Robust Data Protection in Cybersecurity

Abstract

In cybersecurity, protecting the digital boundaries is very important to avoid data leaks and illegal access. In order to keep data safe, intrusion detection systems (IDS) are very important for keeping an eye on things and finding strange behavior. This paper suggests a new way to make IDS better at protecting the digital border. Propsoed method uses complex encryption algorithms to make sure that data is safer within the digital borders. When you protect private data, it stays safe even if someone breaks into your network and gets to the data without the decoding key. In addition, our system uses machine learning techniques to constantly look at network traffic trends and find strange things that could mean an attack. First, it gives you tracking and reports in real time, so you can quickly deal with security risks. The second reason is that encryption protects the privacy, security, and validity of data. Third, adding machine learning improves the accuracy of IDS by lowering the number of false alarms and finding new risks. The paper discuss strong data protection in cybersecurity, the digital boundaries must be protected with advanced encryption methods and IDS that are based on machine learning. This method improves network security, keeps private data safe, and lowers the risk of hacking.