How to use big data analysis to identify SMS bombing patterns?

To identify SMS bombing patterns using big data analysis, follow these steps:

1. Data Collection: Gather SMS logs, including sender IDs, timestamps, message content, recipient numbers, and frequency. Store this data in a scalable system like Tencent Cloud's Elasticsearch Service for efficient indexing and querying.

2. Data Preprocessing: Clean the data by removing duplicates, filtering irrelevant messages, and normalizing formats (e.g., standardizing timestamps). Use Tencent Cloud's Data Lake Analytics to process large datasets quickly.

3. Pattern Recognition: Apply machine learning or statistical methods to detect anomalies. For example:

   • Frequency Analysis: Identify sudden spikes in SMS volume from a single sender or to a specific number.
   • Content Clustering: Group similar messages using Tencent Cloud's TI-ONE platform to detect repetitive phishing or spam content.
   • Behavioral Analysis: Track sender behavior over time, flagging accounts with rapid, high-volume sends.

4. Real-Time Monitoring: Set up alerts for suspicious patterns using Tencent Cloud's Message Queue (CMQ) and Cloud Monitor to trigger automated responses, such as blocking malicious senders.

5. Visualization: Use Tencent Cloud's Data Visualization tools to create dashboards, helping security teams monitor trends and respond faster.

Example: If a sender dispatches 10,000 SMS messages to different numbers within 5 minutes, the system flags it as a potential SMS bomb, triggering CAPTCHA verification or temporary suspension.

Tencent Cloud's Big Data Processing Service (EMR) and Tencent Cloud Security solutions can further enhance detection accuracy and response efficiency.