Smart City Gnosys

Smart city article details

Title	Ram-Based Firmware Attestation For Iot Security: A Representation Learning Framework
ID_Doc	44110
Authors	Iqbal A.; Zia U.; Aman M.N.; Sikdar B.
Year	2024
Published	IEEE Internet of Things Journal, 11, 21
DOI	http://dx.doi.org/10.1109/JIOT.2024.3436057
Abstract	With the proliferation of 4G and 5G mobile networks in smart cities, the adoption of Internet of Things (IoT) devices has surged, emphasizing the critical need for robust security measures. Existing firmware attestation techniques often require high computational budget or access to the device's authentic firmware, posing challenges due to resource and proprietary constraints. To counter these two fundamental challenges, this article introduces a novel software-based attestation framework utilizing RAM traces from IoT devices for remote verification. In the proposed framework, the need for an authentic firmware copy is eliminated, and the most computationally intensive task is assigned to the gateway node of the IoT ecosystem. This approach yields a robust and highly accurate device attestation strategy, while imposing minimal computational demands on the verification device itself. Employing deep learning models trained in a representation learning paradigm, our framework enables the remote verifier to authenticate the internal state of IoT devices. Leveraging data collected from real-world prototype devices, under eight different applications, our approach achieves a remarkable 100% accuracy in detecting critical attacks on IoT devices with a false positive rate of 10-3 Notably, our framework preserves device availability and maintains low authentication latency, underscoring its efficacy and practicality for securing IoT ecosystems. © 2024 IEEE.
Author Keywords	Device attestation; firmware; Internet of Things (IoT); RAM trace; variational autoencoder (VAE)

Similar Articles

Id	Similarity	Authors	Title	Published
33773	0.97	Iqbal A.; Aman M.N.; Sikdar B.	Iot Device Authentication Via Ram Trace Analysis: A Representation Learning Framework	Proceedings - IEEE Global Communications Conference, GLOBECOM (2024)
52961	0.872	Ankergård S.F.J.J.; Dushku E.; Dragoni N.	State-Of-The-Art Software-Based Remote Attestation: Opportunities And Open Issues For Internet Of Things	Sensors, 21, 5 (2021)
34727	0.863	Huo D.; Wang Y.; Liu C.; Li M.; Wang Y.; Xu Z.	Lape: A Lightweight Attestation Of Program Execution Scheme For Bare-Metal Systems	Proceedings - 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City and IEEE 6th International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020 (2020)
1610	0.861	Alghofaili Y.; Rassam M.A.	A Dynamic Trust-Related Attack Detection Model For Iot Devices And Services Based On The Deep Long Short-Term Memory Technique	Sensors, 23, 8 (2023)
18089	0.859	Ullah F.; Salam A.; Amin F.; Khan I.A.; Ahmed J.; Zaib S.A.; Choi G.S.	Deep Trust: A Novel Framework For Dynamic Trust And Reputation Management In The Internet Of Things (Iot)-Based Networks	IEEE Access, 12 (2024)
46683	0.859	Oser P.; Van Der Heijden R.W.; Lüders S.; Kargl F.	Risk Prediction Of Iot Devices Based On Vulnerability Analysis	ACM Transactions on Privacy and Security, 25, 2 (2022)
36064	0.856	Alfahaid A.; Alalwany E.; Almars A.M.; Alharbi F.; Atlam E.; Mahgoub I.	Machine Learning-Based Security Solutions For Iot Networks: A Comprehensive Survey	Sensors, 25, 11 (2025)
41989	0.855	Pashamokhtari A.	Phd Forum Abstract: Dynamic Inference On Iot Network Traffic Using Programmable Telemetry And Machine Learning	Proceedings - 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020 (2020)
52813	0.853	Kohli V.; Aman M.N.; Sikdar B.	Sram And Generative Network-Based Physical Fingerprinting For Trust Management In The Internet Of Things	Proceedings - 2023 IEEE Conference on Dependable and Secure Computing, DSC 2023 (2023)
2519	0.852	El Houda Z.A.; Brik B.; Ksentini A.; Khoukhi L.	A Mec-Based Architecture To Secure Iot Applications Using Federated Deep Learning	IEEE Internet of Things Magazine, 6, 1 (2023)