Cyber Security Threat Detection Framework Using Artificial Intelligence Techniques

Abstract

The difficulty of ensuring cyber-security is steadily growing as a result of the
alarming development in computer connectivity and the sizeable number of applications
associated to computers in recent years. The system also requires robust defines against the
growing number of cyber threats. As a result, a possible role for cyber-security might be
performed by developing Intrusion Detection Systems (IDS) to detect inconsistencies and threats
in computer networks. An effective data-driven intrusion detection system has been created with
the use of Artificial Intelligence, particularly Machine Learning techniques. This research
proposes a novel Voting Classifier based security model which first considers the security
features ranking according to their relevance before developing an IDS model based on the
significant features that have been selected. By lowering the feature dimensions, this approach
not only improves predictive performance for unidentified tests but also lowers the model's
computational expense. The machine learning techniques used to compare the results to the
current approaches were Decision Tree, Random Decision Forest, Random Tree. The
experimental findings of this study confirm that the suggested methods may be used as learningbased
models for network intrusion detection and demonstrate that, when used in the real world,
they outperform conventional machine learning techniques