AI DRIVEN DETECTION OF DECEPTION IN ONLINE INTERACTIONS: STRENGTHENING CYBERSECURITY THROUGH BEHAVIOURAL ANALYSIS

Abstract

The widespread usage of the internet has made online interactions an essential part of modern communication. However, the rise in deceptive practices like identity theft, fraud, and misinformation has also coincided with the expansion of digital interactions. To maintain integrity and confidence in online communities, it is increasingly essential to recognize and deal with these dishonest tactics. The primary challenge is developing a dependable, automated system that can identify false information among the thousands of online conversations. In the lack of advanced AI-based solutions, deception detection in online interactions has mostly relied on human monitoring, rule-based algorithms, and keyword-based filters. These conventional methods' limited effectiveness stems from their incapacity to adapt to the development of deceptive techniques and their tendency to provide false positives or negatives. As a result, the demand for effective deception detection systems in online interactions has never been higher. Since social media, e-commerce, and other online forums have grown in popularity, there is now a context in which acting dishonestly can have far-reaching consequences. These platforms need to be dependable and secure for user confidence, cybersecurity, and the overall wellbeing of online communities. Therefore, the purpose of this research is to develop a powerful tool that uses behavioral pattern recognition, advanced linguistic analysis, and machine learning algorithms to consistently discriminate between genuine and fraudulent online encounters. The proposed approach integrates feature engineering and multi-modal techniques to enhance the precision and effectiveness of deception detection in digital communities. In the end, this would offer a more dependable and secure online environment.