NSF MRI project (MRI: Development of High-Confidence Medical Cyber-Physical System Research Instrument with Benchmark Security Software, CNS-2117785 ), Link

Project 1: An Object Detection based Solver for Google’s Image reCAPTCHA v2

You can request the customized data set in Opensource.

Project 2:  Development of two VR-assisted low-cost online courses leading to security certificates

Abstract: We aim to bridge the students to the industry in cybersecurity. We plan to 1) increase college students’ marketability in cybersecurity placement; 2) provide opportunities to different student groups to be trained to fill the job vacancies; 3) create a pathway for community college students to transit to four-year colleges in cybersecurity. We will: 1) create 1 online Certified Ethical Hacker (CEH) certificate preparation course and 1 online Global Industrial Control System Professional (GICSP) certificate preparation course. Within this course, we will build an interactive virtual lab environment in the context of Industrial Control System (ICS) security for all Louisiana college-level students. 2) conduct a 3-hour online ICS training workshop by TechNeaux Tech. Services with real-world use cases and experience, 3) host 3-4 CAREER fair events in the State of Louisiana with local companies, the State government, security agencies in cybersecurity fields. 

Project 3:  Remotely take over Tesla and drive it away

The study revealed that adversaries could compromise security-critical Internet of Things (IoT) systems by exploiting smart glasses. For instance, to unlock a Tesla vehicle, an adversary can trigger the voice assistant on a victim’s screen-locked phone via electromagnetic interference on the capacitive touch sensor of smart glasses and subsequently play synthesized voice commands. These compromised functionalities are managed by automation tools like Apple Shortcuts and IFTTT. Many of these functions are critical to security and safety, such as unlocking doors, disabling sentry mode, and remote starting the vehicle for keyless driving. The researchers noted that the adversary needs to be aware of the specific phrases used for Shortcuts or IFTTT actions. Additionally, the attacker must be near the victim’s device, while the smart glasses are within Bluetooth range (which can reach more than 70 meters) of the user’s paired smartphone. However, the phrases are usually available online. For instance, Tesla Shortcuts can be found online once the user downloads the Tesla app. Paper     Link 

Project 4:  Hacking unshielded/shielded temperature sensors on infant incubator and 3D printer

Temperature sensing and control systems are widely used in the closed-loop control of critical processes, such as maintaining the thermal stability of patients or in alarm systems for detecting temperature-related hazards.

In this paper, we investigate the reliability of temperature-based control systems from a security and safety perspective. We show how unexpected consequences and safety risks can be induced by physical-level attacks on analog temperature-sensing components. For instance, we demonstrate that an adversary could remotely manipulate the temperature sensor measurements of an infant incubator to cause potential safety issues without tampering with the victim system or triggering automatic temperature alarms. This attack exploits the unintended rectification effect that can be induced in operational and instrumentation amplifiers to control the sensor output, tricking the internal control loop of the victim system to heat up or cool down. Furthermore, we show how the exploit of this hardware-level vulnerability could affect different classes of analog sensors that share similar signal conditioning processes. Our experimental results indicate that conventional defenses commonly deployed in these systems are not sufficient to mitigate the threat, so we propose a prototype design of a low-cost anomaly detector for critical applications to ensure the integrity of temperature sensor signals. Paper Video

Project 5:  Detect and correct the sensor error caused by EMI using a matched dummy circuit

The reliability of control systems often relies on the trustworthiness of sensors. As process automation and robotics keep evolving, sensing methods such as pressure sensing are extensively used in both conventional systems and rapidly emerging applications. The goal of this paper is to investigate the threats and design a low-complexity defense method against EMI injection attacks on sensors.
To ensure the security and usability of sensors and automated processes, we propose to leverage a matched dummy sensor circuit that shares the sensor’s vulnerabilities to EMI but is insensitive to legitimate signals that the sensor is intended to measure. Our method can detect and correct corrupted sensor measurements without introducing components or modules that are highly complex compared to an original low-end sensor circuit. We analyze and evaluate our method on sensors with EMI injection experiments using different attack parameters. We investigate several attack scenarios, including manipulating the DC voltage of the sensor output, injecting sinusoidal signals, white noises, and malicious voice signals. Our experimental results suggest that, with relatively low cost and computation overhead, the proposed method not only detects the attack but also can correct corrupted sensor data to help maintain the functioning of systems based on different kinds of sensors in the presence of attacks. Paper

Project 6:  Auditory Eyesight: Demystifying {μs-Precision} Keystroke Tracking Attacks on Unconstrained Keyboard Inputs

In various scenarios, from system login to writing emails, documents, and forms, keyboard inputs carry alluring data such as passwords, addresses, and IDs. Due to commonly existing non-alphabetic inputs, punctuation, and typos, users’ natural inputs rarely contain only constrained, purely alphabetic keys/words. This work studies how to reveal unconstrained keyboard inputs using auditory interfaces. Audio interfaces are not intended to have the capability of light sensors, such as cameras to identify compactly located keys. Our analysis shows that effectively distinguishing the keys can require a fine localization precision level of keystroke sounds close to the range of microseconds. This work (1) explores the limits of audio interfaces to distinguish keystrokes, (2) proposes a µs-level customized signal processing and analysis-based keystroke tracking approach that takes into account the mechanical physics and imperfect measuring of keystroke sounds, (3) develops the first acoustic side-channel attack study on unconstrained keyboard inputs that are not purely alphabetic keys/words and do not necessarily follow known sequences in a given dictionary or training dataset, and (4) reveals the threats of non-line-of-sight keystroke sound tracking. Our results indicate that, without relying on vision sensors, attacks using limited-resolution audio interfaces can reveal unconstrained inputs from the keyboard with a fairly sharp and bendable “auditory eyesight.” Paper

Project 7 Automatic attacks on IMUs without feedback: Towards adversarial process control on inertial sensor systems with physical feedback side channels

Real-world process control requires continuous sensor measurements and automatic control of the environment. Typical process control systems consist of three main components: controllers functioning as the system’s “brain”, sensors acting as measurement devices, and final control elements that modify the environment.

Prior works showed that adversaries could inject signals into analog sensors to affect the control process; however, an adversarial controller that is necessary to achieve process control is inherently missing in conventional physical-level sensor signal injection attacks, which revealed mechanisms to perturb sensor systems but did not describe the computations necessary to adjust and regulate the process over time. This paper introduces an adversarial control loop approach that computes attack signals during the attack to guide the adversarial process control. Our approach allows constructing the external “brain” of the adversarial process control with programs. Further, we characterize the Physical Feedback Side Channel (PFSC) in outof-band signal injection attacks, and study how the adversarial prototype system can be constructed non-invasively to gain control over two types of inertial sensor-actuator systems, including a MegaWheels self-balancing scooter. We demonstrate proof-ofconcept process control without accessing or tampering with internal modules of the victim system.

An illustration of the structure and basic modules of the proposed method. Paper