Background

In recent years, India has witnessed a significant rise in hoax bomb threat emails targeting critical institutions such as courts, airports, schools, stadiums, and government offices. These emails create widespread panic, disrupt public services, and consume substantial law enforcement resources for verification and response.

Each hoax triggers:

• Emergency evacuations
• Disruption of critical services
• Deployment of security forces
• Significant economic and psychological impact

A major challenge in addressing this issue is the use of anonymity-enabling technologies by perpetrators, such as VPNs, Tor networks, temporary email services, and spoofed identities. These techniques make traditional tracking and attribution extremely difficult, delaying investigation and increasing operational burden on security agencies.

Currently, most email systems rely on basic spam filters that are insufficient to detect sophisticated threat emails. These systems often fail to analyse deeper contextual signals such as linguistic patterns, intent, metadata anomalies, and behavioural indicators. As a result, threatening emails frequently reach official inboxes, triggering emergency protocols even when they are hoaxes.

There is a critical need for an intelligent, proactive, and automated system that can detect, classify, and neutralize such threat emails before they reach the intended recipients, while also supporting investigative agencies with actionable intelligence.

Problem Statement

Design and develop an AI-Powered Threat Email Detection & Prevention System that acts as an intelligent security layer for identifying, analysing, and filtering hoax bomb threat emails targeting critical infrastructure.

The system should analyse incoming emails in real-time using multiple parameters such as content semantics, sender identity, email headers, IP intelligence, and behavioural patterns. Based on risk assessment, the system must automatically classify emails as high-risk threat, suspicious, or safe, and take appropriate actions such as quarantining, discarding, or redirecting them to spam without delivering them to the primary inbox.

The solution should integrate advanced Natural Language Processing (NLP), machine learning models, and email forensics to detect disguised threats, even when sent via anonymization tools like VPNs or Tor. It should also generate structured alerts and intelligence reports for law enforcement agencies to support investigation and threat analysis.

The goal is to build a robust, scalable, and intelligent system that enhances proactive threat detection, reduces panic caused by hoax emails, and strengthens cybersecurity for critical institutions.

Key Objectives

• Detect and prevent hoax bomb threat emails before inbox delivery
• Perform real-time content and intent analysis using AI/NLP
• Analyse email headers, sender reputation, and network metadata
• Identify anonymization patterns (VPN, Tor, disposable emails)
• Classify emails into risk categories (high, medium, low)
• Automatically quarantine, discard, or mark emails as spam
• Generate alerts for high-risk communications
• Support law enforcement with actionable intelligence and logs
• Reduce false positives while maintaining high detection accuracy
• Ensure scalability across multiple institutions and domains

Functional Requirements

• Real-time email scanning with deep inspection of content, attachments, and links.
• NLP-based semantic analysis for detecting contextual and disguised threats.
• Email header and metadata analysis (IP, routing path, SPF/DKIM/DMARC validation).
• Sender reputation checks using blacklists, disposable email databases, and domain intelligence.
• Detection of anonymization techniques such as VPN, Tor, and proxy usage.
• Risk classification engine using machine learning models.
• Automated filtering actions (block, quarantine, spam redirection).
• Real-time alerting system for high-risk emails.
• Investigation dashboard with trace details, metadata insights, and risk scoring.
• Continuous learning module for improving detection accuracy.
• Integration with institutional email systems and SIEM platforms.
• Security controls including role-based access, audit logs, and secure storage.

Evaluation Criteria

• Accuracy of threat detection and classification
• Ability to detect anonymized and spoofed emails
• False positive and false negative rates
• Real-time processing performance
• Robustness of header and metadata analysis
• Effectiveness of automated filtering actions
• Usability of dashboard and alert system
• Scalability across institutions
• Innovation in AI-based threat intelligence

Suggested Tools/Technologies

• Backend: Python (Django/Flask) / Node.js
• Frontend: React.js / Angular
• Database: MongoDB / PostgreSQL
• Email Processing: Mailparser, Python email libraries
• NLP/AI: spaCy, BERT, Transformers
• Machine Learning: Scikit-learn, TensorFlow, PyTorch
• Threat Intelligence APIs (IP reputation, domain analysis)
• SIEM Integration: ELK Stack (Elasticsearch, Logstash, Kibana)

Bonus Points

• Integration with national cybercrime reporting systems
• AI-based threat intent scoring (severity prediction)
• Voice alert system for critical threats
• Automated report generation for law enforcement
• Graph-based analysis of sender networks
• Dark web / threat intelligence feed integration

Deliverables

• Working prototype/demo of the detection system
• Documentation (architecture, detection logic, workflows)
• Deployment guide (cloud/on-premises setup)
• Sample dataset of threat vs normal emails
• Demonstration of detection, classification, and filtering
• Dashboard showcasing alerts and forensic analysis