| Title | Deep Learning Based Approach for Hardware Trojan Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Sankaran, Sriram, Mohan, Vamshi Sunku, Purushothaman., A |
| Conference Name | 2021 IEEE International Symposium on Smart Electronic Systems (iSES) |
| Date Published | dec |
| Keywords | Analytical models, composability, Deep Learning, Distance measurement, Hardware, Hardware Trojans, Neural networks, Non-Invasive approach, Outlier detection, pubcrawl, resilience, Resiliency, Transistors, trojan horse detection, Trojan horses |
| Abstract | Hardware Trojans are modifications made by malicious insiders or third party providers during the design or fabrication phase of the IC (Integrated Circuits) design cycle in a covert manner. These cause catastrophic consequences ranging from manipulating the functionality of individual blocks to disabling the entire chip. Thus, a need for detecting trojans becomes necessary. In this work, we propose a deep learning based approach for detecting trojans in IC chips. In particular, we insert trojans at the circuit-level and generate data by measuring power during normal operation and under attack. Further, we develop deep learning models using Neural networks and Auto-encoders to analyze datasets for outlier detection by profiling the normal behavior and leveraging them to detect anomalies in power consumption. Our approach is generic and non-invasive in that it can be applied to any block without any modifications to the design. Evaluation of the proposed approach shows an accuracy ranging from 92.23% to 99.33% in detecting trojans. |
| DOI | 10.1109/iSES52644.2021.00050 |
| Citation Key | sankaran_deep_2021 |
Deep Learning Based Approach for Hardware Trojan Detection