Cyber crime incidents in India have escalated in frequency and complexity, impacting individuals, organizations,

and critical infrastructure. Despite existing law enforcement mechanisms and reporting platforms, gaps remain

in public awareness, real-time alerts, and consolidated reporting.

This research proposes the design and development of a Public Cyber Alert and Reporting System (PCARS)

tailored for India’s cyber security landscape. The system integrates real-time threat alerts, user-centric reporting

interfaces, automated categorization using machine learning, and seamless communication with law enforcement

With rapid digitalization in India, cyber crimes such as phishing, identity theft, ransomware, and financial fraud

have grown exponentially. Existing reporting platforms are centralized and lack proactive alert dissemination.

The public often remains unaware of emerging cyber threats, leading to repeated victimization and under-

reporting. The objective of this research is to design and develop a user-friendly cyber crime reporting interface,

enable real-time threat alerts, integrate machine learning-based automated classification, facilitate coordination

with law enforcement, and evaluate system effectiveness.

India has witnessed a sharp rise in cyber crime cases, financial frauds, and digital exploitation. Complaints via

national portals have increased significantly. Global frameworks such as centralized reporting systems

demonstrate the importance of multichannel reporting, automated analytics, dashboards, and law enforcement

Implementation

The technology stack includes React.js/Flutter frontend, Node.js/Python backend, PostgreSQL database, and

machine learning models such as Naïve Bayes, Random Forest, SVM, and LSTM. Security measures include

SSL encryption, token-based authentication, data anonymization, and consent-based data collection.

Machine Learning Classification

Evaluation

User testing with 200 participants from urban and rural regions showed 4.5/5 ease of use, 2.3 minutes average

reporting time, and 4.3/5 satisfaction with alerts. The results indicate improved usability, faster reporting, and

enhanced engagement.

The proposed system significantly increases cyber incident reporting participation by simplifying the reporting

process and improving user accessibility across urban and rural populations. Faster data aggregation and analysis

enable earlier detection of emerging attack trends, allowing stakeholders to respond proactively. Additionally,

the delivery of customized alerts enhances user awareness and engagement by providing timely, context-relevant

notifications.Despite these advantages, several challenges remain. Ensuring data privacy is critical, particularly

given the sensitive nature of cyber incident information. Integration with heterogeneous law enforcement

information systems presents technical and organizational difficulties, requiring standardized interfaces and

interoperability frameworks. Furthermore, managing false positives generated by automated classification

models is essential to prevent alert fatigue and maintain user trust.

Overall, addressing these challenges will be key to achieving scalable, secure, and reliable deployment of the

cyber resilience and proactive cyber defense capabilities.

Future Work

Future enhancements to the system will focus on incorporating advanced natural language understanding

techniques to improve the accuracy and depth of cyber incident analysis. Integration with financial institutions

is envisioned to enable rapid fraud detection and mitigation, thereby reducing response time and financial losses.

Additionally, expanding regional language support will promote inclusive access, ensuring broader participation

and usability across diverse linguistic communities.