Towards Resilient Intrusion Detection in CubeSats: Challenges, TinyML Solutions, and Future Directions
- URL: http://arxiv.org/abs/2604.06411v1
- Date: Tue, 07 Apr 2026 19:47:51 GMT
- Title: Towards Resilient Intrusion Detection in CubeSats: Challenges, TinyML Solutions, and Future Directions
- Abstract summary: CubeSats have revolutionized access to space by providing affordable and accessible platforms for research and education.<n>Traditional security measures, such as intrusion detection systems (IDS), are impractical for CubeSats due to resource constraints and unique operational environments.<n>This paper provides an in-depth review of current cybersecurity practices for CubeSats, highlighting limitations and identifying gaps in existing methods.
- Score: 4.127363880327862
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: CubeSats have revolutionized access to space by providing affordable and accessible platforms for research and education. However, their reliance on Commercial Off-The-Shelf (COTS) components and open-source software has introduced significant cybersecurity vulnerabilities. Ensuring the cybersecurity of CubeSats is vital as they play increasingly important roles in space missions. Traditional security measures, such as intrusion detection systems (IDS), are impractical for CubeSats due to resource constraints and unique operational environments. This paper provides an in-depth review of current cybersecurity practices for CubeSats, highlighting limitations and identifying gaps in existing methods. Additionally, it explores non-cyber anomaly detection techniques that offer insights into adaptable algorithms and deployment strategies suitable for CubeSat constraints. Open research problems are identified, including the need for resource-efficient intrusion detection mechanisms, evaluation of IDS solutions under realistic mission scenarios, development of autonomous response systems, and creation of cybersecurity frameworks. The addition of TinyML into CubeSat systems is explored as a promising solution to address these challenges, offering resource-efficient, real-time intrusion detection capabilities. Future research directions are proposed, such as integrating cybersecurity with health monitoring systems, and fostering collaboration between cybersecurity researchers and space domain experts.
Related papers
- CyberFactory: Scaling Cyber Security Capabilities with Instances from the Wild [49.44069582581181]
CyberFactory transforms public vulnerability artifacts, including CVEs from the wild, into executable and verifiable task instances.<n>It uses a reusable vulnerability-analysis skill to guide the teacher through source inspection, problem solving with domain prior, and evidence-based validation.<n>On CyberGym, modelname reaches 52.4% Pass@1 under a one-hour budget, improving over its Qwen3.5 base model by +22.8 points.
arXiv Detail & Related papers (2026-08-24T12:30:10Z) - A Survey of Security Challenges and Solutions for UAS Traffic Management (UTM) and small Unmanned Aerial Systems (sUAS) [25.67972631925627]
Small Unmanned Aerial Systems (sUAS) for civil and commercial missions are vulnerable to cyber-security threats.<n>This paper presents a comprehensive survey of cyber-security vulnerabilities and defenses tailored to the sUAS and UTM ecosystem.
arXiv Detail & Related papers (2026-01-13T05:18:49Z) - SoK: Securing the Final Frontier for Cybersecurity in Space-Based Infrastructure [0.0]
This study analyzes the range of possible space cyber-attack vectors, which include ground, space, satellite, and satellite constellations.<n>It also assesses the efficacy of mitigation measures that are linked with space infrastructures.<n>Based on the analysis, this paper identifies potential research challenges for developing and testing cutting-edge technology solutions.
arXiv Detail & Related papers (2025-07-22T22:51:31Z) - Towards Safety and Security Testing of Cyberphysical Power Systems by Shape Validation [42.350737545269105]
complexity of cyberphysical power systems leads to larger attack surfaces to be exploited by malicious actors.<n>We propose to meet those risks with a declarative approach to describe cyber power systems and automatically evaluate security and safety controls.
arXiv Detail & Related papers (2025-06-14T12:07:44Z) - Modern DDoS Threats and Countermeasures: Insights into Emerging Attacks and Detection Strategies [49.57278643040602]
Distributed Denial of Service (DDoS) attacks persist as significant threats to online services and infrastructure.<n>This paper offers a comprehensive survey of emerging DDoS attacks and detection strategies over the past decade.
arXiv Detail & Related papers (2025-02-27T11:22:25Z) - Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey [37.07938402225207]
Non- Continual Learning (NCCL) has become an emerging paradigm for enabling distributed devices to handle streaming data from a joint non-stationary environment.<n>This survey focuses on the development of the non-centralized continual learning algorithms and the real-world deployment across distributed devices.
arXiv Detail & Related papers (2024-12-18T13:33:28Z) - ACRIC: Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
Recent security incidents in safety-critical industries exposed how the lack of proper message authentication enables attackers to inject malicious commands or alter system behavior.<n>These shortcomings have prompted new regulations that emphasize the pressing need to strengthen cybersecurity.<n>We introduce ACRIC, a message authentication solution to secure legacy industrial communications.
arXiv Detail & Related papers (2024-11-21T18:26:05Z) - Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors [41.94295877935867]
This paper presents a novel approach to detecting stuck values within the Accelerometer and Inertial Measurement Unit of a drone-like spacecraft.
A multi-channel Convolutional Neural Network (CNN) is used to perform multi-target classification and independently detect faults in the sensors.
An integration methodology is proposed to enable the network to effectively detect anomalies and trigger recovery actions at the system level.
arXiv Detail & Related papers (2024-10-11T09:36:38Z) - Counter Denial of Service for Next-Generation Networks within the Artificial Intelligence and Post-Quantum Era [2.156208381257605]
DoS attacks are becoming increasingly sophisticated and easily executable.
State-of-the-art systematization efforts have limitations such as isolated DoS countermeasures.
The emergence of quantum computers is a game changer for DoS from attack and defense perspectives.
arXiv Detail & Related papers (2024-08-08T18:47:31Z) - A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes [1.3631461603291568]
Generative Adversarial Networks (GANs) have emerged as powerful solutions for addressing the constantly changing security issues.
This survey studies the significance of the deep learning model, precisely on GANs, in strengthening cybersecurity defenses.
The focus is to examine how GANs can be influential tools to strengthen cybersecurity defenses in these domains.
arXiv Detail & Related papers (2024-07-11T19:51:48Z) - Ensemble learning techniques for intrusion detection system in the
context of cybersecurity [0.0]
Intrusion Detection System concept was used with the application of the Data Mining and Machine Learning Orange tool to obtain better results.
The main objective of the study was to investigate the Ensemble Learning technique using the Stacking method, supported by the Support Vector Machine (SVM) and kNearest Neighbour (kNN) algorithms.
arXiv Detail & Related papers (2022-12-21T10:50:54Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z) - Survey of Network Intrusion Detection Methods from the Perspective of
the Knowledge Discovery in Databases Process [63.75363908696257]
We review the methods that have been applied to network data with the purpose of developing an intrusion detector.
We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods.
As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
arXiv Detail & Related papers (2020-01-27T11:21:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.