Automatic False Alarm Detection Based on XAI and Reliability Analysis

Abstract

Many studies attempt to apply artificial intelligence (AI) to cyber security to effectively cope with the increasing number of cyber threats. However, there is a black box problem such that it is difficult to understand the basis for AI prediction. False alarms for malware or cyberattacks can cause serious side effects. Due to this limitation, all AI predictions must be confirmed by an expert, which is a considerable obstacle to AI expansion. Compared to the increasing number of cyberattack alerts, the number of alerts that can be analyzed by experts is limited. This paper provides explainability through an interpretation of AI prediction results and a reliability analysis of AI predictions based on explainable artificial intelligence (XAI). In addition, we propose a method for screening high-quality data that can efficiently detect false predictions based on reliability indicators. Through this, even a small security team can quickly respond to false predictions. To validate the proposed method, experiments were conducted using the IDS dataset and the malware dataset. AI errors were detected better than they could be by the existing AI models, with about 262% in the IDS dataset and 127% in the malware dataset from the top 10% of analysis targets. Therefore, the ability to respond to cyberattacks can be improved using the proposed method.

Keywords

• cyberattack
• false alarm detection
• reliability analysis
• explainable artificial intelligence
• shapley value