A Biologically Inspired Cost-Efficient Zero-Trust Security Approach for Attacker Detection and Classification in Inter-Satellite Communication Networks

In Next-Generation Low-Earth-Orbit (LEO) satellite networks, securing inter-satellite communication links (ISLs) through robust authentication is critical due to the dynamic and distributed nature of non-terrestrial environments. Traditional authentication frameworks often fall short under these conditions, prompting the adoption of Zero-Trust Security (ZTS) models. However, existing ZTS protocols incur significant computational overhead, especially as the number of satellite nodes increases, thereby affecting both communication network efficiency and security. To address this, a novel bio-inspired intelligent ZTS approach, i.e., Manta Ray Foraging Cost-Optimized Zero-Trust Security (MRFCO-ZTS), has been developed to leverage bio-inspired data-enabled learning principles to enhance secure satellite communication. The model ingests high-density satellite network data and continuously verifies access requests by formulating a cost function that balances the risk level, attack likelihood, and computational delay in an effective manner. The Manta Ray Foraging Optimization (MRFO) algorithm is applied to minimize this cost function and to enable efficient classification of nodes as detector or attacker based on historical authentication as well as nodes dynamic behaviors. MRFCO-ZTS enables precise identification of attacker behavior while ensuring secure data transmission among verified satellites. The developed MRFCO-ZTS framework is evaluated using a series of numerical simulations under varying satellite user loads, with performance assessed in terms of security accuracy, latency, and operational efficiency.