Detecting Deception with Emotion -The Role of Emotional AI in Combating Social Engineering Attacks by Stephen Mikah Makoshi.
Creators: name: Makoshi, Stephen Mikah
Publisher: name: Zenodo
Year: 2025
Subjects: subject: Social Engineering Attacks
subject: Cybersecurity
subject: Emotion Recognition
subject: Affective Computing
subject: Human-Centered Security
subject: Behavioral Biometrics
subject: Phishing Detection
subject: Insider Threats
subject: Digital Deception
subject: AI Ethics and Privacy
subject: Machine Learning in Security
subject: Emotional Artificial Intelligence (Emotional AI)
subject: Sentiment Analysis
subject: Trust Exploitation
subject: Psychological Manipulation
subject: Cybersecurity &amp; Information Assurance
subject: Artificial Intelligence &amp; Machine Learning
subject: Affective Computing / Emotional AI
subject: Human-Computer Interaction (HCI)
subject: Security Psychology / Behavioral Security
subject: Ethics in AI and Data Privacy
subject: Digital Forensics &amp; Threat Intelligence
subject: Natural Language Processing (NLP)
subject: Biometric Security Systems

description:  



Detecting Deception with Emotion: The Role of Emotional AI in Combating Social Engineering Attacks

Author: Stephen Mikah MakoshiDate: April 12, 2025Type: Copyrighted Manuscript



Detailed Description:This manuscript provides a comprehensive investigation into the integration of Emotional Artificial Intelligence (Emotional AI) into modern cybersecurity practices, with a focus on its potential to combat social engineering attacks. Unlike traditional cyber threats that exploit system vulnerabilities, social engineering targets the human psyche—manipulating users through tactics based on fear, trust, urgency, authority, and curiosity.

Authored by Stephen Mikah Makoshi, the paper begins by unpacking the psychology behind social engineering and detailing the most prevalent attack techniques, including phishing, grooming, pretexting, and baiting. It emphasizes that human error continues to be the weakest link in cybersecurity despite the advancement of technical defenses, thereby underscoring the need for emotionally aware systems that can interpret and respond to human behavior.

The core of the study introduces Emotional AI as an emerging, multidisciplinary approach that combines Natural Language Processing (NLP), computer vision, behavioral biometrics, and machine learning to assess emotional cues in real time. These cues—extracted from text, voice tone, facial expressions, and user interaction patterns—can be used to detect signs of manipulation or distress that precede a social engineering exploit.

The paper outlines how Emotional AI can be applied across various domains within cybersecurity, including:





Phishing email detection through sentiment and tone analysis.




Voice and chatbot security by analyzing stress and urgency in conversations.




Insider threat monitoring through behavioral and emotional baselining.




Continuous real-time monitoring to flag deviations in user behavior tied to emotional shifts.



Beyond its practical applications, the manuscript also explores the ethical, legal, and social implications of deploying Emotional AI. Issues such as emotional data privacy, informed consent, algorithmic bias, and compliance with global data protection regulations (e.g., GDPR, CCPA) are critically examined.

To support real-world adoption, the paper presents a detailed implementation framework covering:





Data collection and multimodal input analysis




Machine learning model training and customization




Integration with SIEM and behavioral analytics tools




Ethical governance and compliance safeguards




Pilot testing and continuous system improvement



Finally, the manuscript concludes that Emotional AI is not a replacement for traditional cybersecurity systems, but a strategic augmentation—a tool that strengthens the "human firewall" by equipping systems with the emotional awareness necessary to detect and disrupt socially engineered cyber threats.