Trustable Network Intrusion Detection System through Wisdomnet and Uncertainty Measures

In the dynamic realm of cybersecurity, ensuring network infrastructure security is an imperative task. With organizations increasingly relying on interconnected systems for their operations, robust and trustworthy defenses against malicious activities are necessary. Network Intrusion Detection Systems (NIDS) play a pivotal role in this defense, functioning as vigilant guardians that monitor network traffic for suspicious patterns and potential security threats. This study introduces a trustworthy NIDS designed not only to detect attacks accurately but also to abstain from making predictions in case of doubt. In the cases of unsure predictions, the system chooses to reject the predictions, thus increasing the correctness of the NIDS results. The rejected cases can be deferred to a human administrator for further verification. The methodology utilizes two approaches: WisdomNet trustable neural networks and Uncertainty Estimation with Monte Carlo dropout. The proposed method can be applied to pre-trained NIDS models to enhance their trustworthiness. Evaluation results demonstrate that the method effectively reduces the classification error rate to zero while categorizing challenging or uncertain predictions as 'reject' at a substantial rejection rate.