Enhancing Trust in Reconfigurable Hardware Systems.
Degree: PhD, Electrical and Computer Engineering, 2017, Virginia Tech
A Cyber-Physical System (CPS) is a large-scale, distributed, embedded system, consisting of various components that are glued together to realize control, computation and communication functions. Although these systems are complex, they are ubiquitous in the Internet of Things (IoT) era of autonomous vehicles/drones, smart homes, smart grids, etc. where everything is connected. These systems are vulnerable to unauthorized penetration due to the absence of proper security features and safeguards to protect important information. Examples such as the typewriter hack involving subversive chips resulting in leakage of keystroke data and hardware backdoors crippling anti-aircraft guns during an attack demonstrate the need to protect all system functions. With more focus on securing a system, trust in untrusted components at the integration stage is of a higher priority.
This work builds on a red-black security system, where an architecture testbed is developed with critical and non-critical IP cores and subjected to a variety of Hardware Trojan Threats (HTTs). These attacks defeat the classic trusted hardware model assumptions and demonstrate the ability of Trojans to evade detection methods based on physical characteristics. A novel metric is defined for hardware Trojan detection, termed as HTT Detectability Metric (HDM) that leverages a weighted combination of normalized physical parameters. Security analysis results show that using HDM, 86% of the implemented Trojans were detected as compared to using power consumption, timing variation and resource utilization alone. This led to the formulation of the security requirements for the development of a novel, distributed and secure methodology for enhancing trust in systems developed under untrusted environments called FIDelity Enhancing Security (FIDES). FIDES employs a decentralized information flow control (DIFC) model that enables safe and distributed information flows between various elements of the system such as IP cores, physical memory and registers. The DIFC approach annotates/tags each data item with its sensitivity level and the identity of the participating entities during the communication.
Trust enhanced FIDES (TE-FIDES) is proposed to address the vulnerabilities arising from the declassification process during communication between third-party soft IP cores. TE-FIDES employs a secure enclave approach for preserving the confidentiality of the sensitive information in the system. TE-FIDES is evaluated by targeting an IoT-based smart grid CPS application, where malicious third-party soft IP cores are prevented from causing a system blackout. The resulting hardware implementation using TE-FIDES is found to be resilient to multiple hardware Trojan attacks.
Advisors/Committee Members: Patterson, Cameron D (committeechair), Athanas, Peter M (committee member), Reed, Jeffrey H (committee member), Plassmann, Paul E (committee member), Shinpaugh, Kevin A (committee member).
Subjects/Keywords: Secure Computing; Trusted Computing; Resilient Computing; Root of Trust; Hardware Trojans; Cyber Physical System Security; Embedded Systems; Reconfigurable Hardware
to Zotero / EndNote / Reference
APA (6th Edition):
Venugopalan, V. (2017). Enhancing Trust in Reconfigurable Hardware Systems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/75212
Chicago Manual of Style (16th Edition):
Venugopalan, Vivek. “Enhancing Trust in Reconfigurable Hardware Systems.” 2017. Doctoral Dissertation, Virginia Tech. Accessed April 23, 2018.
MLA Handbook (7th Edition):
Venugopalan, Vivek. “Enhancing Trust in Reconfigurable Hardware Systems.” 2017. Web. 23 Apr 2018.
Venugopalan V. Enhancing Trust in Reconfigurable Hardware Systems. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2018 Apr 23].
Available from: http://hdl.handle.net/10919/75212.
Council of Science Editors:
Venugopalan V. Enhancing Trust in Reconfigurable Hardware Systems. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/75212