ECS Lab & Research

Welcome to Embedded Computing Security (ECS) Lab at San Francisco State University, established by Dr. Fatemeh Tehranipoor. In ESC lab, we work on developing novel design techniques and methodologies for securing hardware and embedded systems. We implement new architectures for the improvement of hardware-based random functions that meet the desired requirements of new embedded system security needs.  Our low-cost and lightweight solutions can be used to provide physical objects security in a variety of different applications such as the Internet of Things (IoT) and Healthcare domain. We also apply Machine Learning (ML) techniques to detect security vulnerabilities of existing secured designs and ensure trustworthiness of integrated circuits (ICs) and systems against reverse engineering attacks.  For more information, please take a look at our "Research Directions" that we are involved in ECS Lab and visit our "Publications" list.

Research Directions:

Hardware-Intrinsic Security:

Hardware-Intrinsic Security deals with secure secret key storage. By generating the secret keys from the intrinsic properties of the silicon, e.g., from intrinsic physical unclonable functions (PUFs) and true random number generations (TRNGs), no permanent secret key storage is required anymore and the key is only present in the device for a minimal amount of time. On the other hand, the field of Hardware Intrinsic Security is extension to hardware-based security primitives (PUFs and TRNGs). PUFs can derive secrets from the complex physical characteristics of ICs rather than storing the secrets in digital memories. PUFs can significantly increase physical security by generating volatile secrets (keys) that only exist in a digital form when an IC is powered on and operating. TRNGs are important security primitive used in a variety of applications including cryptographic algorithms, statistics, communication systems, simulations, etc. It is critical that a TRNG be able to produce outputs consisting of fully unpredictable and unbiased bits in a cost-effective manner.  In general, these hardware security primitives should provide low-cost and efficient trustworthiness of the physical hardware platforms. One should note that while these primitives can provide advantages to ICs, there are properties and details of the design that need to be considered (e.g. power usage, overhead, heat).    

 

 

Internet of Things (IoT) and Cyber-Physical Systems (CPS) Security:

IoT is the ability of everyday devices to connect and transfer data to each other, is already carving out a place in the consumer market.  Both CPS and IoT play an increasingly important role in critical application of our everyday life such as medical, banking, govermental, infrastructure, home use, smart grid, etc. With the exponential growth and adoption of IoT components, there are fundamental and enormous security concerns and risks associated with the interaction of potentially unsecure devices. The problem with this fundamental desire for greater interconnectivity of digital lives is that the expansion of IoT and CPS will broaden the potential attack surface for cyber-criminals and hackers. Due to the increasing sophistication of these malicious individuals, new methods of authentication need to be developed in order to establish safe and secure communication or exchange of sensitive data over the IoT.  Proactive and coordinated efforts are needed to strengthen security and reliance for CPS and IoT.

 

 

Machine Learning (ML) in Hardware Security Enhancement: 

Over the past two decades, many of the leading semiconductor companies have become fabless (i.e. outsourced chip fabrication to external fabrication facility) due to the increasing costs and complexity associated with modern IC fabrication facility.  The trend in the recent past has been to move toward the globalization of supply chains. Unfortunately, there are a number of security threats associated with such exposure. It is well-known that various vulnerabilities introduce numerous opportunities for malicious parties to engage in Intellectual Property (IP) piracy, counterfeiting, overproduction and reverse engineering. Furthermore, with electronics ubiquitously deployed in sensitive domains and critical infrastructure (finance, healthcare, military applications, industrial sensors and actuators, data communication), understanding the corresponding risks and developing appropriate remedies have become paramount.  To this end, Machine Learning (ML) based algorithms are potential techniques that use computational methods to detect circuit vulnerabilities, ensure security and trustworthiness of ICs, and to provide strong predictive modeling and anomaly detection to secure circuits. In ECS lab,  we experimentally investigate the robustness of existing secured design techniques (hardware obfuscation –logic locking) against reverse engineering attacks. Then we will also apply machine learning on the experimentally collected data to detect security vulnerabilities of a design and propose countermeasures to enhance the level of security.

 

 

Embedded System Design Security: 

Embedded systems are dedicated computer system designed for specific functions. The widespread usage of embedded systems at all levels of population and workforce make their security vulnerabilities. As these systems get more and more complex, the electronics and embedded systems required to keep their operational also get more complex. Thus, for attackers, these electronics provide a rich opportunity to exploit vulnerabilities in the system. These vulnerabilities could be introduced at the device, circuit, architecture, board, or firmware level and at different steps in the system life-cycle including design, manufacturing, deployment, and field. Current approach to improve the security posture of these embedded systems is to build various defensive cyber layers, or create last-minute security patches to address vulnerabilities. These approaches are necessarily reactive and not particularly effective in addressing new attacks. Security has always been an afterthought in computer systems design, particularly in the design of the type of embedded systems found in large scale infrastructure. However, it must be a first class design objective from the start. Therefore, improving the design of secure embedded systems which are the backbone of many of our critical infrastructure systems including transportation, energy, manufacturing, etc. is a serious need.

 

ECS Lab Members:

Graduate Students:

  1. Shubhankar Samar Pataskar
  2. Jerin Johnson
  3. Rohan Panda
  4. Anugayathiri (Anu) Pugazhenthi

Undergraduate Students:

  1. Juan C. Angeles Acuna
  2. Dylan Wright
  3. QuangMinh Ho

Interns (High School Students):

  1. Sreetama Chowdhury, Mission San Jose High School
  2. Selena Sun, Carlmont High School

Research Collaborators: