AI-Powered Monitoring System for Detecting Drug Trafficking on Social Media

Social media has become an unexpected battle- ground in the fight against drug trafficking, with dealers finding new ways to sell illegal substances online. Our research tackles this problem head-on by developing a smart monitoring system that acts like a digital detective, watching over social media platforms to spot potential drug-related activities. Think of our system as an extra set of eyes for law enforcement, but powered by artificial intelligence. Instead of officers spending endless hours scrolling through social media posts, our system does this work automatically. It’s designed to understand both images and text conversations, picking up on subtle clues that might signal drug- related activity. What makes our approach special is how it presents information to law enforcement officers. Rather than drowning them in complex data, the system provides clear, easy- to-understand alerts and visual reports. It’s like having a smart assistant that taps you on the shoulder when something suspicious needs attention. This matters because traditional monitoring methods are struggling to keep up with how quickly drug dealers change their tactics on social media. Our system helps law enforcement work smarter, not harder. It processes huge amounts of social media content in real-time, giving officers the insights they need to take action quickly. The research shows that bringing artificial intelligence into the fight against online drug trafficking isn’t just about working faster – it’s about working better. By automating the tedious parts of monitoring social media, officers can focus their energy on what they do best: investigating leads and stopping drug trafficking.

Authors

Publication Details

Published
2025-03-27
DOI
https://doi.org/10.38124/ijisrt/25mar413
Citations
1
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
1.49
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article

AI-Powered Monitoring System for Detecting Drug Trafficking on Social Media

Riaz Ahmed Shaikh, Himabindu Kudapa, Gaurav Joshi
1 citations
Advanced Malware Detection Techniques
1.49
article

AI-Powered Monitoring System for Detecting Drug Trafficking on Social Media

Riaz Ahmed Shaikh, Himabindu Kudapa, Gaurav Joshi
article en
1 citations

Abstract

Social media has become an unexpected battle- ground in the fight against drug trafficking, with dealers finding new ways to sell illegal substances online. Our research tackles this problem head-on by developing a smart monitoring system that acts like a digital detective, watching over social media platforms to spot potential drug-related activities. Think of our system as an extra set of eyes for law enforcement, but powered by artificial intelligence. Instead of officers spending endless hours scrolling through social media posts, our system does this work automatically. It’s designed to understand both images and text conversations, picking up on subtle clues that might signal drug- related activity. What makes our approach special is how it presents information to law enforcement officers. Rather than drowning them in complex data, the system provides clear, easy- to-understand alerts and visual reports. It’s like having a smart assistant that taps you on the shoulder when something suspicious needs attention. This matters because traditional monitoring methods are struggling to keep up with how quickly drug dealers change their tactics on social media. Our system helps law enforcement work smarter, not harder. It processes huge amounts of social media content in real-time, giving officers the insights they need to take action quickly. The research shows that bringing artificial intelligence into the fight against online drug trafficking isn’t just about working faster – it’s about working better. By automating the tedious parts of monitoring social media, officers can focus their energy on what they do best: investigating leads and stopping drug trafficking.

Openalex Percentile: Top 21%
Advanced Malware Detection Techniques
1.49
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