Anomaly detection (Computer security)

The relentless expansion of space exploration necessitates the development of robust and dependable anomaly detection systems (ADS) to safeguard the safety and efficacy of space missions. Conventional anomaly detection methods often falter in the face of the intricate and nuanced dynamics of space systems, resulting in a proliferation of false positives and/or false negatives. In this study, we explore into cutting-edge techniques in deep learning (DL) to tackle the challenges inherent in ADS. This research offers an in-depth examination of recent breakthroughs and hurdles in deep learning-driven anomaly detection tailored specifically for space systems and operations. A key advantage of deep learning-based anomaly detection lies in its adaptability to the diverse data encountered in space missions. For instance, Convolutional Neural Networks (CNNs) excel at capturing spatial dependencies in high-dimensional data, rendering them well-suited for tasks such as satellite imagery analysis. Conversely, Recurrent Neural Networks (RNNs), with their temporal modeling prowess, excel in identifying anomalies in time-series data generated by spacecraft sensors. Despite the potential of deep learning, several challenges persist in its application to anomaly detection in space systems. The scarcity of labeled data presents a formidable hurdle, as acquiring labeled anomalies during space operations is often prohibitively expensive and impractical. Additionally, the interpretability of deep learning models remains a concern, particularly in mission-critical scenarios where human operators need to comprehend the rationale behind anomaly predictions.

The integrity of network communications is constantly being challenged by more sophisticated intrusion techniques. Attackers are shifting to stealthier and more complex forms of attacks in an attempt to bypass known mitigation strategies. Also, many detection methods for popular network attacks have been developed using outdated or non-representative attack data. To effectively develop modern detection methodologies, there exists a need to acquire data that can fully encompass the behaviors of persistent and emerging threats. When collecting modern day network traffic for intrusion detection, substantial amounts of traffic can be collected, much of which consists of relatively few attack instances as compared to normal traffic. This skewed distribution between normal and attack data can lead to high levels of class imbalance. Machine learning techniques can be used to aid in attack detection, but large levels of imbalance between normal (majority) and attack (minority) instances can lead to inaccurate detection results.