  Satellite networks are vital in facilitating communication services for
various critical infrastructures. These networks can seamlessly integrate with
a diverse array of systems. However, some of these systems are vulnerable due
to the absence of effective intrusion detection systems, which can be
attributed to limited research and the high costs associated with deploying,
fine-tuning, monitoring, and responding to security breaches. To address these
challenges, we propose a pretrained Large Language Model for Cyber Security ,
for short PLLM-CS, which is a variant of pre-trained Transformers [1], which
includes a specialized module for transforming network data into contextually
suitable inputs. This transformation enables the proposed LLM to encode
contextual information within the cyber data. To validate the efficacy of the
proposed method, we conducted empirical experiments using two publicly
available network datasets, UNSW_NB 15 and TON_IoT, both providing Internet of
Things (IoT)-based traffic data. Our experiments demonstrate that proposed LLM
method outperforms state-of-the-art techniques such as BiLSTM, GRU, and CNN.
Notably, the PLLM-CS method achieves an outstanding accuracy level of 100% on
the UNSW_NB 15 dataset, setting a new standard for benchmark performance in
this domain.
