International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
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AI-Based Satellite Surveillance and Cyber Threat Detection Framework for Securing Border Zones Tejasva Jadhav Cybersecurity Research Enthusiast, BCA Graduate Birla Institute of Technology Mesra, Jaipur Campus Jaipur, Rajasthan – 302017, India Email: Jadhavrk2007@gmail.com ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - This research proposes a dual-layer surveillance
defense has focused on network intrusion detection systems (IDS), GPS spoofing detection, and electromagnetic jamming countermeasures.
framework aimed at enhancing border security by integrating AI-powered satellite imagery analysis with communicationlevel cybersecurity threat monitoring. The visual layer uses machine learning models such as YOLOv8 and Vision Transformers to identify unauthorized troop gatherings, suspicious vehicle movements, or sudden terrain alterations near sensitive border regions. Concurrently, the cybersecurity layer monitors wireless traffic, GPS spoofing attempts, and potential signal jamming, providing a hybrid defense against both physical and digital intrusions. This unified system addresses modern hybrid warfare challenges and proposes a scalable model for use by defense and intelligence agencies)
However, current literature lacks a combined system for border surveillance that fuses satellite imagery with cybersecurity intelligence. This paper fills that gap by proposing a two-layered approach for simultaneous monitoring.
2. PROPOSED FRAMEWORK 2.1 Layer 1: Satellite Surveillance with AI This layer focuses on monitoring physical activity in and around border areas using satellite imagery combined with artificial intelligence. With advancements in computer vision, satellite data can now be processed in near real-time to detect threats or unauthorized actions without relying solely on ground personnel.
Key Words: Border Security, Satellite Surveillance, AI Threat Detection, Cybersecurity, GPS Spoofing, Signal Jamming, Intrusion Detection
1.INTRODUCTION India’s border regions—especially along the LoC and LAC— have faced increasing threats from both physical intrusions and modern digital warfare. Incidents like the 2020 Galwan Valley clash and frequent drone incursions in Punjab and Jammu underscore the need for real-time, wide-area surveillance. Simultaneously, cyber operations such as GPS spoofing, signal jamming, and network intrusion have targeted defense infrastructure and border communications.
AI models can identify patterns resembling temporary or permanent military camps—such as tent arrangements, vehicle clusters, or artificial structures—especially in previously unoccupied zones. This helps in flagging possible infiltration bases or foreign troop presence.
Traditional methods—manned posts, drones, and ground sensors—are limited by terrain and latency. With advances in AI and satellite imaging, it is now feasible to automate border surveillance and detect cyber threats simultaneously.
1.1 Related Work
Impact Factor value: 8.315
Terrain disruption near fenced or sensitive areas: Changes in land texture or topography, such as digging, trench formation, or collapsed fencing, can indicate intrusion or tunneling attempts. These anomalies are detected using semantic segmentation and change detection algorithms.
Many studies have explored satellite image analysis using convolutional neural networks (CNNs), object detection via YOLO, and Vision Transformers for identifying terrain changes or large vehicles. Separately, research in cyber
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Convoy or vehicle movement: Movement of groups of vehicles, especially in sensitive regions, is detected through frame-byframe comparison of imagery. The system can track movement patterns, distinguish military vehicles from civilian ones, and alert authorities in case of unusual activity.
This paper proposes a dual-layer system combining AI-based satellite vision with communication-layer threat detection — offering real-time situational awareness of both physical and digital threats across border zones.
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