The growing reliance on satellite-based infrastructures for communication, navigation, defense, and environmental monitoring has magnified the urgency of securing Space Information Networks (SINs) against cyber threats. This paper presents a comprehensive review of the vulnerabilities, threat vectors, and advanced countermeasures impacting SINs. Key vulnerabilities, including system complexity, use of Commercial Off-the-Shelf (COTS) components, lack of standardized security frameworks, and emerging quantum threats, are critically analyzed. This paper classifies cyber threats into active and passive categories, highlighting real-world case studies such as Denial-of-Service attacks, message modification, eavesdropping, and satellite transponder hijacking. A detailed survey of countermeasures follows, focusing on AI-driven intrusion detection, federated learning approaches, deep learning techniques, random routing algorithms, and quantum-resistant encryption. This study emphasizes the pressing need for integrated, resilient, and proactive security architectures tailored to the unique constraints of space systems. It concludes by identifying research gaps and recommending future directions to enhance the resilience of SINs against evolving cyber threats in an increasingly contested space environment.

By virtue of the fact that authorized individuals are able to circumvent traditional barriers, insider attacks continue to be a persistent worry for organizational cybersecurity. Different user behaviors and complex connection patterns can cause static detection systems to miss potential dangers and slow down the discovery process. An adaptive insider threat detection system and a real-time adaptive security framework are both presented in this study. SentriDeep-RL, which is a sentinel based on deep learning and reinforcement learning, is also shown. At the same time that graph neural networks (GNNs) simulate user-asset interactions, deep learning models represent behavioral characteristics. Within the SentriDeep-RL framework, real-time anomaly detection frameworks are able to identify dangers in an instant, and adaptive thresholding that is based on reinforcement learning is able to make dynamic decisions about threats that are unknown. Changing insider threat patterns can be identified by the system without the need for retraining because it monitors user behaviors, access records, and network telemetry. Gradual model modifications and temporal attention are utilized in the SentriDeep-RL approach. In early threat detection, SentriDeep-RL outperforms static models by 12-18% on multisource corporate datasets that imitate both planned and incidental insider acts. This results in a reduction in the number of false positives while keeping the same level of computing efficiency! Adaptive learning methods and idea drift detection are two techniques that can be utilized to obtain improved performance in changing business environments. Through the utilization of deep learning, GNN-based relational modeling, reinforcement learning, and real-time anomaly detection, this platform will be able to identify insider threats in a proactive, scalable, and resilient manner. Last but not least, SentriDeepRL leverages technology to address concerns regarding company cybersecurity, reaction time, as well as operational and reputational issues.