AI-DRIVEN SECURITY FRAMEWORK FOR ENHANCED THREAT DETECTION IN MOBILE SATELLITE NETWORKS

ICTACT Journal on Communication Technology ( Volume: 16 , Issue: 1 )

Abstract

The increasing reliance on Mobile Satellite Networks (MSNs) for secure and reliable global communication has led to heightened concerns over cybersecurity threats. Traditional security mechanisms often struggle to counter adaptive and sophisticated attacks, necessitating the integration of Artificial Intelligence (AI)-driven security frameworks. The dynamic nature of MSNs, characterized by high latency, intermittent connectivity, and diverse attack vectors, presents unique security challenges. A key challenge is the real-time detection and mitigation of cyber threats, including eavesdropping, jamming, spoofing, and denial-of-service (DoS) attacks. Conventional cryptographic techniques and firewall-based security solutions are inadequate against evolving threats, necessitating intelligent intrusion detection and adaptive defense mechanisms. To address these challenges, an AI-enhanced security framework is proposed, incorporating Deep Learning (DL) and Reinforcement Learning (RL) models for threat detection and response optimization. The framework employs a Hybrid CNN-LSTM model for anomaly detection, achieving an accuracy of 98.7% in detecting intrusion attempts. Furthermore, a Q-learning-based adaptive security policy dynamically adjusts encryption levels and resource allocation to mitigate ongoing attacks, reducing response time by 37.5% compared to traditional methods. The proposed approach was validated using the NSL-KDD dataset and real- world satellite telemetry logs, demonstrating a 45.3% improvement in threat mitigation efficiency over conventional rule-based systems.

Authors

S. Mythili1, R. Nidhya2, R. Arun Kumar3
United Institute of Technology, India1, Madanapalle Institute of Technology and Science, India2, University of South Wales, United Kingdom3

Keywords

AI-Driven Security, Mobile Satellite Networks, Deep Learning, Threat Detection, Reinforcement Learning

Published By
ICTACT
Published In
ICTACT Journal on Communication Technology
( Volume: 16 , Issue: 1 )
Date of Publication
March 2025
Pages
3443 - 3448

ICT Academy is an initiative of the Government of India in collaboration with the state Governments and Industries. ICT Academy is a not-for-profit society, the first of its kind pioneer venture under the Public-Private-Partnership (PPP) model

Contact Us

ICT Academy
Module No E6 -03, 6th floor Block - E
IIT Madras Research Park
Kanagam Road, Taramani,
Chennai 600 113,
Tamil Nadu, India

For Journal Subscription: journalsales@ictacademy.in

For further Queries and Assistance, write to us at: ictacademy.journal@ictacademy.in