click here to download project abstract of cyber security threats
At data pro, we provide final year projects with source code in python for computer science students in Hyderabad, Visakhapatnam.
ABSTRACT
Introduction: In the rapidly evolving landscape of social networks, the security of user accounts has become paramount.
Literature Review: Examining existing research on account security, we identify gaps in current methodologies and highlight the need for a more robust and adaptive approach to compromised account detection. The literature review forms the foundation for our proposed methodology.
Methodology: Employing advanced machine learning algorithms and behavioral analytics, our methodology involves real-time monitoring of user activities, device fingerprinting, and anomaly detection. Thus By actively analyzing user behavior patterns, we enhance the accuracy of identifying potential compromises.
Data Collection and Feature Extraction: Effective detection relies on comprehensive data collection. so We gather diverse user data, including login times, locations, devices used, and interaction patterns. Feature extraction from this dataset enables the creation of a nuanced profile for each user, contributing to a more precise detection model.
Model Training and Implementation: The machine learning model is trained on labeled datasets, utilizing supervised learning techniques. Active voice algorithms empower the model to continually adapt and learn from evolving user behaviors, ensuring a dynamic defense against emerging threats. so The implementation phase involves integrating the model seamlessly into existing social network security frameworks.
Results and Evaluation: A rigorous evaluation of our model showcases its efficacy in accurately identifying compromised accounts while minimizing false positives. Real-world testing across diverse social network platforms demonstrates the adaptability and reliability of our solution.
Conclusion: In conclusion, the proposed compromised account detection system offers a proactive and dynamic defense against unauthorized access on social networks.
