A Machine learning approach to detect intrusion

Abstract

The rapid growth of interconnected digital device and the advancement of technology, the data security become an issue now a days. This leads various type of cyberattacks. In order to detect and effectively analyze malicious activities in a system or network, the implementation of an intrusion detection system is needed. It’s a system in a form of hardware or software that searches the network system for unusual behavior. Intrusion detection becomes paramount in ensuring network security as computer becoming more interconnected. Therefore, intrusion detection systems actively monitor the traffic of computers on the network to detect and alert to threats or malicious activities. In this study I develop intelligent detection system that can detect intrusion using various machine learning technique. I also use different metrics to evaluate the effectiveness of our solutions and make comparisons to determine the best intrusion detection network. Results from various studies were carefully analyzed and compared; this provided insight and direction for future work in this area. Data breaches often lead to unauthorized access to, alteration or deletion of sensitive data, leading to privacy and confidentiality issues. This can impact service because denial of service can cause outages and prevent important operations. The consequences of such incidents can be devastating, including security breaches and legal and financial damages to the organization's reputation.