Cost-Effective Valuable Data Detection Based on the Reliability of Artificial Intelligence

• Published in: IEEE access Vol. 9; pp. 108959 - 108974
• Main Authors: Kim, Hongbi, Lee, Yongsoo, Lee, Eungyu, Lee, Taejin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; cyber security; Cyberattack; Cybersecurity; Datasets; Decision analysis; Error detection; Explainable artificial intelligence; Feature extraction; feature outlier score; Malware; Predictive models; Reliability; Reliability analysis; reliability indicator; Security; Side effects; Statistical analysis; valuable data
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3101257

Many previous studies have investigated applying artificial intelligence (AI) to cyber security. Despite considerable performance advantages, AI for cyber security requires final confirmation by an analyst, e.g. malware misdetection can cause significant adverse side effects. Thus, a human analyst must check all AI predictions, which poses a major obstacle to AI expansion. This paper proposes a reliability indicator for AI prediction using explainable artificial intelligence and statistical analysis techniques. This will enable analysts with limited daily workload to focus upon valuable data, and quickly verify AI predictions. Analysts generally make decisions based on several features that they know exactly what they mean, rather than all available features. Since the proposed reliability indicator is calculated using features meaningful to analysts, it can be easily understood and hence speed final decisions. To verify the performance of the proposed method, an experiment was conducted using the IDS dataset and the malware dataset. The AI error was detected better than the existing AI model at about 114% in IDS and 95% in malware. Thus, cyberattack response could be greatly improved by adopting the proposed method.