Summary An intrusion detection system (IDS) is valuable for detecting anomalies and unauthorized access to a system or network. Due to the black‐box nature of these IDS models, network experts need more trust in systems to act on alerts and transparency to understand the model's inner logic. Moreover, biased models' decisions affect the model performance and increase the false positive rates, directly affecting the model's accuracy. So, maintaining Transparency and Fairness simultaneously in IDS models is essential for accurate decision‐making. Existing methods face challenges of the tradeoff between fairness and accuracy, which also affects the reliability and robustness of the model. Motivated by these research gaps, we developed the Fair‐XIDS model. This model clarifies its internal logic with visual explanations and promotes fairness across its entire lifecycle. The Fair‐XIDS model successfully integrates complex transparency and fairness algorithms to address issues like Imbalanced datasets, algorithmic bias, and postprocessing bias with an average 85% reduction in false positive rate. To ensure reliability, the proposed model effectively mitigates the tradeoff between accuracy and fairness with an average of 90% accuracy and more than 85% fairness. The assessment results of the proposed model over diverse datasets and classifiers mark its model‐agnostic nature. Overall, the model achieves more than 85% consistency among diverse classifiers.

The significant expansion in network size to support new paradigms such as cloud computing, IoT (Internet of Things), etc. together with the exponential increase in vulnerabilities has challenged the existing security mechanisms greatly. These challenges have opened many avenues for research in network security. However, while attack graphs play an important role in analyzing vulnerabilities, analyzing large attack graphs itself is a major issue. Therefore, it is necessary to extract only the critical part of the attack graph. Although technologies have been developed for attack path characterization, there is a lack of hybrid technology that can differentiate between similar behavior attack paths. We have proposed a cost-based path characterization technique that takes the attack node's vulnerability complexity into account and significantly reduces the number of vulnerabilities that need to be patched to avoid the major segment of attack graph. Moreover, we have used a real network prototype to validate the performance of the proposed scheme. The proposed scheme works well in cases where some vulnerabilities have similar risk scores. To the best of our knowledge, this is the first time that a cost-effective approach for attack path analysis has been proposed.