Time Aware F-Score for Cybersecurity Early Detection Evaluation

• Published in: Applied sciences Vol. 14; no. 2; p. 574
• Main Authors: López-Vizcaíno, Manuel, Nóvoa, Francisco J., Fernández, Diego, Cacheda, Fidel
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2024
• Subjects: classification algorithms; Cyberbullying; Cybersecurity; Cyberterrorism; Data security; Datasets; Decision making; Detectors; early detection; Machine learning; network security; Safety and security measures; Social networks; time-aware metrics
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14020574

With the increase in the use of Internet interconnected systems, security has become of utmost importance. One key element to guarantee an adequate level of security is being able to detect the threat as soon as possible, decreasing the risk of consequences derived from those actions. In this paper, a new metric for early detection system evaluation that takes into account the delay in detection is defined. Time aware F-score (TaF) takes into account the number of items or individual elements processed to determine if an element is an anomaly or if it is not relevant to be detected. These results are validated by means of a dual approach to cybersecurity, Operative System (OS) scan attack as part of systems and network security and the detection of depression in social media networks as part of the protection of users. Also, different approaches, oriented towards studying the impact of single item selection, are applied to final decisions. This study allows to establish that nitems selection method is usually the best option for early detection systems. TaF metric provides, as well, an adequate alternative for time sensitive detection evaluation.