Pacheco J, Benitez VH, Felix-Herran LC, Satam P (2020) Artificial neural networks-based intrusion detection system for internet of things fog nodes. Ge M, Syed NF, Fu X, Baig Z, Robles-Kelly A (2021) Towards a deep learning-driven intrusion detection approach for internet of things. Comput Netw 186:107784Ībiodun OI, Abiodun EO, Alawida M, Alkhawaldeh RS, Arshad H (2021) A review on the security of the internet of things: challenges and solutions. Wirel Pers Commun 1–35 Lee B, Amaresh S, Green C, Engels D (2018) Comparative study of deep learning models for network intrusion detection. It can be applied at the customer end without requiring any hardware alteration/IP updates from FPGA vendors. The proposed Trojan detection approach is, to the best of our knowledge during publication, the only open source, software-based, low-cost alternative to time consuming design re-construction and analysis based approaches. Once a reference database is created, the Trojan detection is very fast and takes less than a minute on a general-purpose Intel Core i5 processor-based computer. An example of Trojan detection is shown for Xilinx Virtex5 VLX50T FPGA. The method is generic and can be applied to a wide range of FPGAs. We leverage open-source tools, publicly available datasets and vendor provided tools.
Due to this bottom-up approach, any stealthy malicious circuit can be detected, which is difficult to achieve through conventional design analysis methods. Our approach is bottom-up: it starts with bitstream inspection, FPGA component identification from the layout, and follows up with identification of malicious configurations. In this paper, we turn the tables and provide a proof of concept for an approach that utilizes bitstream reverse engineering for Trojan detection. Attackers can attempt to gain control of the FPGA during run-time for Trojan insertion by bitstream reverse engineering. As a result, critical security issues arise, such as hardware Trojan (HT) insertion. These constraints force a tradeoff for FPGA developers regarding FPGA security features such as encryption of FPGA bitstreams. Low-end FPGAs are widely used in many networking applications where severe cost, power, and resource constraints persist. FPGAs incorporate runtime reconfiguration capabilities, which allow modular designs to be mapped. According to recent reports and surveys over the last 2 years, field-programmable gate arrays (FPGAs) have increasingly become the prime targets for carrying out extremely sophisticated cyber-attacks.
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