ProTECT: A Programmable Threat Evaluation and Control Unit for Zero Trust Networks
DOI:
https://doi.org/10.22399/ijcesen.673Keywords:
Device Security, Threat Monitoring, Network Security, Zero Trust Architectures, FPGAAbstract
As Zero Trust Architectures (ZTA) become increasingly adopted in enterprise networks, so it is essential to continuously monitor the security status of connected devices. Real-time threat monitoring within the devices that are connected to the network is necessary for this. It's challenging, especially in resource-constrained settings, to ensure continuous monitoring in current devices available in market. ProTECT (Programmable Threat Evaluation and Control Unit) addresses this challenge by providing a continuous real-time monitoring, non-tamperable, trust score for ZTA network connected devices. Trust score in security coprocessor segregated from device computing architecture has been determined employing real-time hardware monitoring of CPU micro-architectural signals. We examined ProTECT on an open-source RISC-V processor based architecture against ransomware, RoP & cache-based micro-architectural attacks. While illustrating area overheads, we implement framework on an AMD Virtex XC7V2000T FPGA Module.
References
Tsai, M., Lee, S., & Shieh, S. W. (2024). Strategy for implementing of zero trust architecture. IEEE Transactions on Reliability. 73(1);93-100, doi: 10.1109/TR.2023.3345665
Singh, N., Pal, S., Leupers, R., Merchant, F., & Rebeiro, C. (2024). PROMISE: A Programmable Hardware Monitor for Secure Execution in Zero Trust Networks. IEEE Embedded Systems Letters. Pp(99) Doi: 10.1109/LES.2024.3354831
Federici, F., Martintoni, D., & Senni, V. (2023). A zero-trust architecture for remote access in industrial IoT infrastructures. Electronics, 12(3);566.
Singh, N., Ganesan, V., & Rebeiro, C. (2022). Secure Processor Architectures. In Handbook of Computer Architecture (pp. 1-29). Singapore: Springer Nature Singapore.
Kuruvila, A. P., Mahapatra, A., Karri, R., & Basu, K. (2021). Hardware performance counters: Ready-made vs tailor-made. ACM Transactions on Embedded Computing Systems (TECS), 20(5s), 1-26.
Stafford, V. (2020). Zero trust architecture. NIST special publication, 800, 207.
Delshadtehrani, L., Canakci, S., Zhou, B., Eldridge, S., Joshi, A., & Egele, M. (2020). {PHMon}: A programmable hardware monitor and its security use cases. In 29th USENIX Security Symposium (USENIX Security 20) (pp. 807-824).
Das, S., Werner, J., Antonakakis, M., Polychronakis, M., & Monrose, F. (2019, May). Sok: The challenges, pitfalls, and perils of using hardware performance counters for security. In 2019 IEEE Symposium on Security and Privacy (SP) (pp. 20-38). IEEE.
Zhou, B., Gupta, A., Jahanshahi, R., Egele, M., & Joshi, A. (2018, May). Hardware performance counters can detect malware: Myth or fact?. In Proceedings of the 2018 on Asia conference on computer and communications security (pp. 457-468).
Hunt, G., Letey, G., & Nightingale, E. (2017). The seven properties of highly secure devices. tech. report MSR-TR-2017-16.
Yoon, M. K., Mohan, S., Choi, J., Kim, J. E., & Sha, L. (2013, April). SecureCore: A multicore-based intrusion detection architecture for real-time embedded systems. In 2013 IEEE 19th Real-Time and Embedded Technology and Applications Symposium (RTAS) (pp. 21-32). IEEE.
Sherwood, T., Perelman, E., Hamerly, G., Sair, S., & Calder, B. (2003). Discovering and exploiting program phases. IEEE micro, 23(6), 84-93. DOI: 10.1109/MM.2003.1261391
Weicker, R. P. (1984). Dhrystone: a synthetic systems programming benchmark. Communications of the ACM, 27(10), 1013-1030.
“Shakti: Open Source Processor Development Ecosystem, IIT Madras..”Available: https://shakti.org.in/.
D. Patterson et al., “Embench: A Modern Embedded Benchmark Suite,” 2019. Available: https://github.com/embench/embench-iot.
“Coremark: An EEMBC Benchmark.” https://www.eembc.org/.
Godavarthi, S., & G., D. V. R. (2024). Federated Learning’s Dynamic Defense Against Byzantine Attacks: Integrating SIFT-Wavelet and Differential Privacy for Byzantine Grade Levels Detection. International Journal of Computational and Experimental Science and Engineering, 10(4);775-786. https://doi.org/10.22399/ijcesen.538
P. Jagdish Kumar, & S. Neduncheliyan. (2024). A novel optimized deep learning based intrusion detection framework for an IoT networks. International Journal of Computational and Experimental Science and Engineering, 10(4)1169-1180. https://doi.org/10.22399/ijcesen.597
ONAY, M. Y. (2024). Secrecy Rate Maximization for Symbiotic Radio Network with Relay-Obstacle. International Journal of Computational and Experimental Science and Engineering, 10(3);381-387. https://doi.org/10.22399/ijcesen.413
Jha, K., Sumit Srivastava, & Aruna Jain. (2024). A Novel Texture based Approach for Facial Liveness Detection and Authentication using Deep Learning Classifier. International Journal of Computational and Experimental Science and Engineering, 10(3);323-331. https://doi.org/10.22399/ijcesen.369
S, P., & A, P. (2024). Secured Fog-Body-Torrent : A Hybrid Symmetric Cryptography with Multi-layer Feed Forward Networks Tuned Chaotic Maps for Physiological Data Transmission in Fog-BAN Environment. International Journal of Computational and Experimental Science and Engineering, 10(4);671-681. https://doi.org/10.22399/ijcesen.490
R, U. M., P, R. S., Gokul Chandrasekaran, & K, M. (2024). Assessment of Cybersecurity Risks in Digital Twin Deployments in Smart Cities. International Journal of Computational and Experimental Science and Engineering, 10(4);695-700. https://doi.org/10.22399/ijcesen.494
Prasada, P., & Prasad, D. S. (2024). Blockchain-Enhanced Machine Learning for Robust Detection of APT Injection Attacks in the Cyber-Physical Systems. International Journal of Computational and Experimental Science and Engineering, 10(4);799-810. https://doi.org/10.22399/ijcesen.539
S, P. S., N. R., W. B., R, R. K., & S, K. (2024). Performance Evaluation of Predicting IoT Malicious Nodes Using Machine Learning Classification Algorithms. International Journal of Computational and Experimental Science and Engineering, 10(3);341-349. https://doi.org/10.22399/ijcesen.395
C, A., K, S., N, N. S., & S, P. (2024). Secured Cyber-Internet Security in Intrusion Detection with Machine Learning Techniques. International Journal of Computational and Experimental Science and Engineering, 10(4);663-670. https://doi.org/10.22399/ijcesen.491
guven, mesut. (2024). Dynamic Malware Analysis Using a Sandbox Environment, Network Traffic Logs, and Artificial Intelligence. International Journal of Computational and Experimental Science and Engineering, 10(3);480-490. https://doi.org/10.22399/ijcesen.460
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.