This Python-based tool conducts a probabilistic vulnerability assessment for Space IoT systems. It leverages data from the National Vulnerability Database (NVD) and VarIoT Database to provide a comprehensive analysis of potential security risks in space-based IoT deployments.
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NIST National Vulnerability Database https://nvd.nist.gov/
VARIot IoT Vulnerabilities and Exploits Databses https://www.variotdbs.pl/
- Data retrieval from NVD (https://nvd.nist.gov/developers/request-an-api-key) and VarIoT (https://www.variotdbs.pl/api/) databases
- Monte Carlo simulation for probabilistic risk assessment
- Comparison of probabilistic and Euclidean distance-based vulnerability scoring
- Visualization of vulnerability score distributions
- Statistical analysis of results
Utilizes Monte Carlo simulation to account for uncertainties in space environments. Each vulnerability is assigned a space context factor to reflect potentially increased severity in space applications.
Calculates Euclidean distance-based scores to measure deviation from mean severity, helping identify outlier vulnerabilities.
Performs statistical comparison between probabilistic and Euclidean approaches, offering insights into different aspects of vulnerability severity in Space IoT contexts.
Vulnerabilities are selected based on relevance to Space IoT systems:
- Applicability to space-based embedded systems and IoT devices
- Relevance to satellite network communication protocols
- Potential impact on critical space operations
- Suitability for low-power, resource-constrained devices
- Security issues in long-distance, high-latency communication
- Threats specific to harsh space environments
- Vulnerabilities in ground station systems interacting with space-based IoT
python space_iot_vulnerability_assessment.pyEnsure nvd_and_variot_api_keys.txt contains required API keys for NVD and VarIoT databases.
Probabilistic Vulnerability Assessment for Space IoT Systems
-----------------------------------------------------------
Total vulnerabilities: 2010
Results generated on: 2024-08-24 15:10:21
Data sources:
- National Vulnerability Database (NVD): 2000 vulnerabilities
- VarIoT Database: 10 vulnerabilities
Total vulnerabilities analyzed: 2010
Sample of Vulnerabilities (showing first 10):
+------------------+--------+------------+----------------------+---------------------+-----------------+----------------------------------------------------+
| Vulnerability ID | Source | Base Score | Space Context Factor | Probabilistic Score | Euclidean Score | Description |
+------------------+--------+------------+----------------------+---------------------+-----------------+----------------------------------------------------+
| CVE-2024-41806 | NVD | 5.3 | 1.49 | 7.88 | 0.78 | The Open edX Platform is a learning management ... |
| CVE-2024-7101 | NVD | 7.3 | 1.11 | 8.10 | 1.22 | A vulnerability, which was classified as critic... |
| CVE-2024-36542 | NVD | 8.8 | 1.11 | 9.81 | 2.72 | Insecure permissions in kuma v2.7.0 allows atta... |
| CVE-2024-40872 | NVD | 8.4 | 1.08 | 9.04 | 2.32 | There is an elevation of privilege vulnerabilit... |
| CVE-2024-41800 | NVD | 4.8 | 1.48 | 7.08 | 1.28 | Craft is a content management system (CMS). Cra... |
| CVE-2024-41801 | NVD | 4.7 | 1.24 | 5.82 | 1.38 | OpenProject is open source project management s... |
| CVE-2024-7007 | NVD | 0.0 | 1.37 | 0.00 | 6.08 | Positron Broadcast Signal Processor TRA7005 v1.... |
| CVE-2022-32759 | NVD | 7.5 | 1.14 | 8.53 | 1.42 | IBM Security Directory Integrator 7.2.0 and IBM... |
| CVE-2024-28772 | NVD | 5.4 | 1.07 | 5.75 | 0.68 | IBM Security Directory Integrator 7.2.0 and IBM... |
| CVE-2024-40873 | NVD | 3.4 | 1.48 | 5.05 | 2.68 | There is a cross-site scripting vulnerability i... |
+------------------+--------+------------+----------------------+---------------------+-----------------+----------------------------------------------------+
Simulation Results:
Average Total Vulnerability Score: 15281.82
Maximum Total Vulnerability Score: 15472.20
Minimum Total Vulnerability Score: 15078.22
Average Probabilistic Score (per vulnerability): 7.60
Average Euclidean Score (per vulnerability): 2.25
Statistical Comparison:
T-statistic: 57.0694
P-value: 0.0000
The difference between Probabilistic and Euclidean approaches is statistically significant.
Vulnerability Selection Criteria:
The vulnerabilities analyzed in this assessment were selected based on their relevance to Space IoT systems. The selection criteria include:
1. Applicability to embedded systems and IoT devices commonly used in space applications.
2. Relevance to communication protocols and technologies used in satellite and space-based networks.
3. Potential impact on critical space operations, including data transmission, remote sensing, and navigation.
4. Vulnerabilities affecting low-power, resource-constrained devices typical in space IoT.
5. Security issues related to long-distance, high-latency communication characteristic of space systems.
6. Threats specific to the harsh environmental conditions of space (e.g., radiation effects on hardware).
7. Vulnerabilities in ground station systems that interact with space-based IoT devices.
These criteria ensure that the vulnerabilities analyzed are particularly relevant to the unique challenges and constraints of Space IoT systems. This focused approach allows for a more accurate assessment of the specific risks faced by these specialized IoT deployments in space environments.
Interpretation:
- The system-wide scores represent the total vulnerability across all identified vulnerabilities.
- Individual scores (Probabilistic and Euclidean) represent the severity of single vulnerabilities.
- The large difference between system-wide and individual scores is due to the cumulative effect of multiple vulnerabilities.
- The statistical difference between Probabilistic and Euclidean scores suggests they capture different aspects of vulnerability severity.
- The high T-statistic (57.0059) indicates a large standardized difference between the Probabilistic and Euclidean scoring methods.
- The p-value of 0.0 suggests that this difference is statistically significant, not due to random chance.
- This implies that the Probabilistic and Euclidean methods are capturing fundamentally different aspects of vulnerability:
* Probabilistic scores incorporate the space context factor and random variations, reflecting potential real-world fluctuations.
* Euclidean scores measure how much each vulnerability deviates from the average, highlighting outliers.
Implications for Space IoT Security:
1. System-wide vulnerability:
- The large total scores indicate that the cumulative effect of multiple vulnerabilities is substantial.
- This suggests a need for a holistic approach to security, addressing multiple vulnerabilities simultaneously.
2. Individual vulnerability assessment:
- The difference between Probabilistic and Euclidean scores for individual vulnerabilities shows the importance of context in risk assessment.
- Probabilistic scores may better reflect the dynamic nature of space environments.
- Euclidean scores help identify vulnerabilities that are significantly different from the norm.
3. Security strategy:
- Use Probabilistic scores for day-to-day risk management and prioritization.
- Use Euclidean scores to identify outlier vulnerabilities that may require special attention.
- The combination of both methods provides a more comprehensive view of the system's security posture.
4. Continuous monitoring:
- The variation between minimum and maximum total scores suggests that the system's overall vulnerability can fluctuate.
- Regular reassessment is crucial to capture changes in the threat landscape and system configuration.
5. Resource allocation:
- Prioritize resources based on vulnerabilities with high scores in both Probabilistic and Euclidean methods.
- Consider the system-wide scores when determining overall security budgets and efforts.
Conclusion:
This analysis provides a nuanced view of vulnerabilities in the space IoT system.
By combining Probabilistic and Euclidean approaches, we gain insights into both the dynamic nature of space-based risks
and the relative severity of individual vulnerabilities. This dual perspective enables more informed decision-making
in securing complex space IoT environments.
- The system-wide scores represent the total vulnerability across all identified vulnerabilities.
- Individual scores (Probabilistic and Euclidean) represent the severity of single vulnerabilities.
- The large difference between system-wide and individual scores is due to the cumulative effect of multiple vulnerabilities.
- The statistical difference between Probabilistic and Euclidean scores suggests they capture different aspects of vulnerability severity.
- Probabilistic scores incorporate the space context factor and random variations, reflecting potential real-world fluctuations.
- Euclidean scores measure how much each vulnerability deviates from the average, highlighting outliers.
- System-wide vulnerability assessment indicates a need for a holistic security approach.
- Individual vulnerability scores highlight the importance of context in risk assessment.
- The combination of Probabilistic and Euclidean methods provides a comprehensive view of the system's security posture.
- Regular reassessment is crucial due to potential fluctuations in overall vulnerability.
- Resource allocation should prioritize vulnerabilities with high scores in both methods.
- requests
- numpy
- matplotlib
- pandas
- scipy
- prettytable
Install via:
pip install requests numpy matplotlib pandas scipy prettytable
This tool is for educational and research purposes only. It should not be the sole basis for security decisions in real-world Space IoT deployments. The vulnerability data and risk assessments are based on publicly available information and simplified models, which may not fully represent the complex, evolving nature of cybersecurity threats in space environments. Always consult cybersecurity experts and follow industry best practices when implementing security measures for Space IoT systems.
Copyright 2024 Eric Yocam
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.