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Introduction
India’s IRCTC railway ticketing platform faced a sophisticated cyber scam where automated bots and scripts manipulated the Tatkal booking system, allowing black-market agents to hoard and resell tickets at inflated prices. This case highlights critical cybersecurity vulnerabilities in web APIs, CAPTCHA systems, and session management—along with how digital forensics dismantled the operation.
Learning Objectives
- Understand how attackers bypass CAPTCHA, OTP, and API security.
- Learn key digital forensics techniques used to trace fraudulent bookings.
- Discover security measures implemented to prevent future abuses.
1. Bypassing CAPTCHA with OCR & Automation
Command/Tool Example:
from pytesseract import image_to_string import cv2 OCR-based CAPTCHA bypass captcha_image = cv2.imread('captcha.png') text = image_to_string(captcha_image) print(f"Extracted CAPTCHA: {text}")
Step-by-Step Explanation:
- Attackers used Optical Character Recognition (OCR) tools like Tesseract to decode CAPTCHA images.
- Automated scripts then fed the extracted text into IRCTC’s booking form, bypassing human verification.
2. Exploiting IRCTC’s Web APIs
Command/Tool Example:
import requests Direct API abuse for ticket booking headers = {"Authorization": "Bearer stolen_token"} payload = {"train_no": "12301", "class": "SL", "passengers": 4} response = requests.post("https://irctc-api/book", headers=headers, json=payload)
Step-by-Step Explanation:
- Fraudsters reverse-engineered IRCTC’s booking APIs to send automated requests.
- Stolen session tokens or credentials were used to mimic legitimate users.
3. Masking Identity with VPNs & Proxies
Command/Tool Example (Linux):
Rotating proxies using cURL curl --proxy http://proxy-ip:port https://irctc.com --interface eth0
Step-by-Step Explanation:
- Attackers routed traffic through VPNs and proxy chains to evade IP-based blocking.
- Tools like MAC spoofing (
ifconfig eth0 hw ether 00:11:22:33:44:55
) further hid device identities.
4. Digital Forensics: Detecting Fraudulent Bookings
Command/Tool Example (Log Analysis):
Analyzing IRCTC logs for rapid bookings grep "POST /book" access.log | awk '{print $1}' | sort | uniq -c | sort -nr
Step-by-Step Explanation:
- Investigators identified suspicious IPs making sub-second bookings.
- Pattern analysis revealed automated scripts (e.g., 100+ requests from a single IP in 1 minute).
5. Mobile Forensics: Extracting Chat Evidence
Command/Tool Example (WhatsApp Extraction):
Using ADB to pull WhatsApp data from seized devices adb pull /sdcard/Android/media/com.whatsapp/ ./evidence
Step-by-Step Explanation:
- Forensic teams extracted chat logs from Telegram/WhatsApp groups selling tickets.
- Metadata (timestamps, user IDs) linked agents to bulk bookings.
6. IRCTC’s Security Upgrades
Implementation Example (AI-Based Detection):
AI model flagging suspicious bookings from sklearn.ensemble import IsolationForest model = IsolationForest(contamination=0.01) model.fit(booking_patterns) anomalies = model.predict(new_requests)
Step-by-Step Explanation:
- IRCTC deployed AI to detect abnormal booking speeds or geolocations.
- Dynamic CAPTCHAs and OTP logins were enforced to curb automation.
What Undercode Say
- Key Takeaway 1: API security is critical—attackers exploited unsecured endpoints with simple Python scripts.
- Key Takeaway 2: Digital forensics (log, network, and mobile analysis) was pivotal in tracing and prosecuting fraudsters.
Analysis:
This case underscores the need for layered security—CAPTCHA alone is ineffective against OCR bots. IRCTC’s shift to AI-driven monitoring and stricter authentication sets a precedent for other e-ticketing platforms. Future attacks may leverage generative AI for more advanced evasion, requiring adaptive defenses.
Prediction
As automation tools grow more sophisticated, ticket scalping will evolve into AI-driven fraud. Platforms must adopt real-time behavioral analytics and zero-trust API security to stay ahead.
IT/Security Reporter URL:
Reported By: Aravindhan A – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅