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.
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
AI-Driven Security, Mobile Satellite Networks, Deep Learning, Threat Detection, Reinforcement Learning
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| Published By :
ICTACT
Published In :
ICTACT Journal on Communication Technology
( Volume: 16 , Issue: 1 , Pages: 3443 - 3448 )
Date of Publication :
March 2025
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