This study investigated the role of emotion analysis and acoustic speech features in
enhancing situation assessment and decision-making within the field of cybersecurity. The
purpose of the study was to explore how emotional cues, such as stress or frustration, and
acoustic speech features, such as tone of voice and speech rhythm, could aid in early threat
detection, particularly in identifying phishing or social engineering attacks. The research
questions guiding the study were: (1) How could emotion analysis, particularly stress and
frustration, improve the detection of cybersecurity threats? (2) How did acoustic speech
features contribute to identifying unauthorized individuals in communication systems or in
analyzing conversations linked to cyberattacks? To address these questions, the study
employed a methodology that integrated emotion analysis and acoustic feature extraction
from communication data, utilizing pattern recognition and machine learning algorithms
for threat detection. The findings suggested that the combination of emotional and
acoustic indicators significantly improved the accuracy of early threat detection, making
cybersecurity systems more responsive and efficient. The main results showed that
emotion and speech feature analysis enhanced security systems’ ability to identify and
prevent attacks, especially in real-time scenarios. Based on these findings, it was
recommended that cybersecurity systems incorporate emotion and speech analysis
technologies to better anticipate and address emerging threats. The study suggested
further exploration into integrating these tools with existing cybersecurity frameworks for
more proactive defense mechanisms.
Keywords: Emotion analysis, acoustic features, cybersecurity, threat detection,
decision-making.
JEL code: L86 Information and Internet Services • Computer Software