A SURVEY ON SECURESCAN 360: CYBER INTEGRATED SYSTEM FOR THREAT DETECTION AND IMAGE AUTHENTICATION

Computer Science and Engineering

The rapid expansion of digital communication and internet-based services has significantly increased the occurrence of cyber threats such as phishing websites, spam emails, and manipulated digital images. These attacks pose serious risks to individuals and organizations, including data theft, financial fraud, and the spread of misinformation. Traditional cybersecurity solutions often focus on detecting a single type of threat, resulting in fragmented protection and limited effectiveness against modern multi-vector attacks. To address these challenges, this paper proposes SecureScan 360, a multi-faceted cybersecurity detection platform that integrates multiple threat detection mechanisms within a unified system. The proposed system incorporates three primary modules: phishing website detection, spam email classification, and image forgery detection. The phishing detection module utilizes the Random Forest algorithm to analyze URL features and identify fraudulent websites. The spam detection module employs Natural Language Processing (NLP) techniques with Long Short-Term Memory (LSTM) to categorize emails as spam or legitimate. Additionally, the image forgery detection module applies digital image processing techniques to detect manipulated or tampered images. The platform also includes a role-based interface that allows users to access detection services, submit complaints, and view activity reports, while administrators can monitor users, analyze reports, and manage system notifications. By integrating machine learning and image analysis techniques into a single platform, SecureScan 360 enhances cybersecurity protection and improves the reliability of digital communication systems. redistribution platforms represent a scalable and replicable model for achieving zero hunger and environmental sustainability across developing nations. The system is designed with scalability and usability in mind, ensuring efficient threat detection for both individuals and organizations. Experimental evaluation indicates that the proposed model achieves reliable detection performance across multiple threat categories. By combining machine learning algorithms with image analysis techniques, the platform provides a comprehensive approach to modern cybersecurity challenges. The unified framework improves response time, enhances threat visibility, and supports proactive monitoring of suspicious activities. Overall, SecureScan 360 demonstrates the potential of intelligent security systems in creating safer and more trustworthy digital environments.

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