Biblio

Embedded devices are becoming increasingly pervasive in safety-critical systems of the emerging cyber-physical world. While trusted execution environments (TEEs), such as ARM TrustZone, have been widely deployed in mobile platforms, little attention has been given to deployment on real-time cyber-physical systems, which present a different set of challenges compared to mobile applications. For safety-critical cyber-physical systems, such as autonomous drones or automobiles, the current TEE deployment paradigm, which focuses only on confidentiality and integrity, is insufficient. Computation in these systems also needs to be completed in a timely manner (e.g., before the car hits a pedestrian), putting a much stronger emphasis on availability.To bridge this gap, we present RT-TEE, a real-time trusted execution environment. There are three key research challenges. First, RT-TEE bootstraps the ability to ensure availability using a minimal set of hardware primitives on commodity embedded platforms. Second, to balance real-time performance and scheduler complexity, we designed a policy-based event-driven hierarchical scheduler. Third, to mitigate the risks of having device drivers in the secure environment, we designed an I/O reference monitor that leverages software sandboxing and driver debloating to provide fine-grained access control on peripherals while minimizing the trusted computing base (TCB).We implemented prototypes on both ARMv8-A and ARMv8-M platforms. The system is tested on both synthetic tasks and real-life CPS applications. We evaluated rover and plane in simulation and quadcopter both in simulation and with a real drone.

The proliferation of real-time cyber-physical systems (CPS) is making profound changes to our daily life. Many real-time CPSs are security and safety-critical because of their continuous interactions with the physical world. While the general perception is that the security protection mechanism deployment is often absent in real-time embedded systems, there is no existing empirical study that measures the adoption of these mechanisms in the ecosystem. To bridge this gap, we conduct a measurement study for real-time embedded firmware from both a security perspective and a real-time perspective. To begin with, we collected more than 16 terabytes of embedded firmware and sampled 1,000 of them for the study. Then, we analyzed the adoption of security protection mechanisms and their potential impacts on the timeliness of real-time embedded systems. Besides, we measured the scheduling algorithms supported by real-time embedded systems since they are also security-critical.

Microcontroller-based embedded systems have become ubiquitous with the emergence of IoT technology. Given its critical roles in many applications, its security is becoming increasingly important. Unfortunately, MCU devices are especially vulnerable. Code reuse attacks are particularly noteworthy since the memory address of firmware code is static. This work seeks to combat code reuse attacks, including ROP and more advanced JIT-ROP via continuous randomization. Previous proposals are geared towards full-fledged OSs with rich runtime environments, and therefore cannot be applied to MCUs. We propose the first solution for ARM-based MCUs. Our system, named HARM, comprises a secure runtime and a binary analysis tool with rewriting module. The secure runtime, protected inside the secure world, proactively triggers and performs non-bypassable randomization to the firmware running in a sandbox in the normal world. Our system does not rely on any firmware feature, and therefore is generally applicable to both bare-metal and RTOS-powered firmware. We have implemented a prototype on a development board. Our evaluation results indicate that HARM can effectively thaw code reuse attacks while keeping the performance and energy overhead low.

The Internet of Vehicles (IoV) can offer safe and comfortable driving experiences with the cooperation communications between central servers and cache-enabled road side units (RSUs) as edge severs, which also can provide high-speed, high-quality and high-stability communication access for vehicle users (VUs). However, due to the huge popular traffic volume, the burden of backhaul link will be seriously enlarged, which will greatly degrade the service experience of the IoV. In order to alleviate the backhaul load of IoV network, in this paper, we propose an asynchronous coded caching strategy composed of two phases, i.e., content placement and asynchronous coded transmission. The asynchronous request and request deadline are closely considered to design our asynchronous coded transmission algorithm. Also, we derive the close-form expression of average backhaul load under the nonuniform demands of IoV users. Finally, we formulate an optimization problem of minimizing average backhaul load and obtain the optimized content placement vector. Simulation results verify the feasibility of our proposed strategy under the asynchronous situation.

Recently, there has been significant enthusiasms in exploiting physical (PHY-) layer characteristics for secure wireless communication. However, most existing PHY-layer security paradigms are information theoretical methodologies, which are infeasible to real and practical systems. In this paper, we propose a weighted fractional Fourier transform (WFRFT) pre-coding scheme to enhance the security of wireless transmissions against eavesdropping. By leveraging the concept of WFRFT, the proposed scheme can easily change the characteristics of the underlying radio signals to complement and secure upper-layer cryptographic protocols. We demonstrate a running prototype based on the LTE-framework. First, the compatibility between the WFRFT pre-coding scheme and the conversational LTE architecture is presented. Then, the security mechanism of the WFRFT pre-coding scheme is demonstrated. Experimental results validate the practicability and security performance superiority of the proposed scheme.

Electromagnetic (EM) radiation is an inherent phenomenon in the operation of electronic information equipment. The side-channel attack, malicious hardware and software implantation attack by using the EM radiation are implemented to steal information. This form of attacks can be used in air-gap information equipment, which bring great danger for information security. The malicious implantation hidden in circuits are difficult to detect. How to detect the implantation is a challenging problem. In this paper, a malicious hardware implantation is analyzed. A method that leverages EM signals for Trojan-embedded computer video cable detection is proposed. The method neither needs activating the Trojan nor requires near-field probe approaching at close. It utilizes recognizable patterns in the spectrum of EM to predict potential risks. This paper focuses on the extraction of feature vectors via the empirical mode decomposition (EMD) algorithm. Intrinsic mode functions (IMFs) are analyzed and selected to be eigenvectors. Using a common classification technique, we can achieve both effective and reliable detection results.

Hardware design, especially the very large scale integration(VLSI) and systems on chip design(SOC), utilizes many codes from third-party intellectual property (IP) providers and former designers. Hardware Trojans (HTs) are easily inserted in this process. Recently researchers have proposed many HTs detection techniques targeting the design codes. State-of-art detections are based on the testability including Controllability and Observability, which are effective to all HTs from TrustHub, and advanced HTs like DeTrust. Meanwhile, testability based detections have advantages in the timing complexity and can be easily integrated into recently industrial verification. Undoubtedly, the adversaries will upgrade their designs accordingly to evade these detection techniques. Designing a variety of complex trojans is a significant way to perfect the existing detection, therefore, we present a novel design of HTs to defeat the testability based detection methods, namely DeTest. Our approach is simple and straight forward, yet it proves to be effective at adding some logic. Without changing HTs malicious function, DeTest decreases controllability and observability values to about 10% of the original, which invalidates distinguishers like clustering and support vector machines (SVM). As shown in our practical attack results, adversaries can easily use DeTest to upgrade their HTs to evade testability based detections. Combined with advanced HTs design techniques like DeTrust, DeTest can evade previous detecions, like UCI, VeriTrust and FANCI. We further discuss how to extend existing solutions to reduce the threat posed by DeTest.

As an extension of Internet of Things (IoT) in transportation sector, the Internet of Vehicles (IoV) can greatly facilitate vehicle management and route planning. With ever-increasing penetration of IoV, the security and privacy of driving data should be guaranteed. Moreover, since vehicles are often left unattended with minimum human interventions, the onboard sensors are vulnerable to physical attacks. Therefore, the physically secure authentication and key agreement (AKA) protocol is urgently needed for IoV to implement access control and information protection. In this paper, physical unclonable function (PUF) is introduced in the AKA protocol to ensure that the system is secure even if the user devices or sensors are compromised. Specifically, PUF, as a hardware fingerprint generator, eliminates the storage of any secret information in user devices or vehicle sensors. By combining password with PUF, the user device cannot be used by someone else to be successfully authenticated as the user. By resorting to public key cryptography, the proposed protocol can provide anonymity and desynchronization resilience. Finally, the elaborate security analysis demonstrates that the proposed protocol is free from the influence of known attacks and can achieve expected security properties, and the performance evaluation indicates the efficiency of our protocol.

On ARM processors with TrustZone security extension, asynchronous introspection mechanisms have been developed in the secure world to detect security policy violations in the normal world. These mechanisms provide security protection via passively checking the normal world snapshot. However, since previous secure world checking solutions require to suspend the entire rich OS, asynchronous introspection has not been widely adopted in the real world. Given a multi-core ARM system that can execute the two worlds simultaneously on different cores, secure world introspection can check the rich OS without suspension. However, we identify a new normal-world evasion attack that can defeat the asynchronous introspection by removing the attacking traces in parallel from one core when the security checking is performing on another core. We perform a systematic study on this attack and present its efficiency against existing asynchronous introspection mechanisms. As the countermeasure, we propose a secure and trustworthy asynchronous introspection mechanism called SATIN, which can efficiently detect the evasion attacks by increasing the attackers' evasion time cost and decreasing the defender's execution time under a safe limit. We implement a prototype on an ARM development board and the experimental results show that SATIN can effectively prevent evasion attacks on multi-core systems with a minor system overhead.

With the increasing popularity of augmented reality (AR) services, providing seamless human-computer interactions in the AR setting has received notable attention in the industry. Gesture control devices have recently emerged to be the next great gadgets for AR due to their unique ability to enable computer interaction with day-to-day gestures. While these AR devices are bringing revolutions to our interaction with the cyber world, it is also important to consider potential privacy leakages from these always-on wearable devices. Specifically, the coarse access control on current AR systems could lead to possible abuse of sensor data. Although the always-on gesture sensors are frequently quoted as a privacy concern, there has not been any study on information leakage of these devices. In this article, we present our study on side-channel information leakage of the most popular gesture control device, Myo. Using signals recorded from the electromyography (EMG) sensor and accelerometers on Myo, we can recover sensitive information such as passwords typed on a keyboard and PIN sequence entered through a touchscreen. EMG signal records subtle electric currents of muscle contractions. We design novel algorithms based on dynamic cumulative sum and wavelet transform to determine the exact time of finger movements. Furthermore, we adopt the Hudgins feature set in a support vector machine to classify recorded signal segments into individual fingers or numbers. We also apply coordinate transformation techniques to recover fine-grained spatial information with low-fidelity outputs from the sensor in keystroke recovery. We evaluated the information leakage using data collected from a group of volunteers. Our results show that there is severe privacy leakage from these commodity wearable sensors. Our system recovers complex passwords constructed with lowercase letters, uppercase letters, numbers, and symbols with a mean success rate of 91%.

In Wireless Sensor Networks (WSNs), data aggregation has been used to reduce bandwidth and energy costs during a data collection process. However, data aggregation, while bringing us the benefit of improving bandwidth usage and energy efficiency, also introduces opportunities for security attacks, thus reducing data delivery reliability. There is a trade-off between bandwidth and energy efficiency and achieving data delivery reliability. In this paper, we present a comparative study on the reliability and efficiency characteristics of different data aggregation approaches using both simulation studies and test bed evaluations. We also analyse the factors that contribute to network congestion and affect data delivery reliability. Finally, we investigate an optimal trade-off between reliability and efficiency properties of the different approaches by using an intermediate approach, called Multi-Aggregator based Multi-Cast (MAMC) data aggregation approach. Our evaluation results for MAMC show that it is possible to achieve reliability and efficiency at the same time.

Augmented reality is poised to become a dominant computing paradigm over the next decade. With promises of three-dimensional graphics and interactive interfaces, augmented reality experiences will rival the very best science fiction novels. This breakthrough also brings in unique challenges on how users can authenticate one another to share rich content between augmented reality headsets. Traditional authentication protocols fall short when there is no common central entity or when access to the central authentication server is not available or desirable. Looks Good To Me (LGTM) is an authentication protocol that leverages the unique hardware and context provided with augmented reality headsets to bring innate human trust mechanisms into the digital world to solve authentication in a usable and secure way. LGTM works over point to point wireless communication so users can authenticate one another in a variety of circumstances and is designed with usability at its core, requiring users to perform only two actions: one to initiate and one to confirm. Users intuitively authenticate one another, using seemingly only each other's faces, but under the hood LGTM uses a combination of facial recognition and wireless localization to bootstrap trust from a wireless signal, to a location, to a face, for secure and usable authentication.

Automatic face recognition techniques applied on particular group or mass database introduces error cases. Error prevention is crucial for the court. Reranking of recognition results based on anthropology analysis can significant improve the accuracy of automatic methods. Previous studies focused on manual facial comparison. This paper proposed a weighted facial similarity computing method based on morphological analysis of components characteristics. Search sequence of face recognition reranked according to similarity, while the interference terms can be removed. Within this research project, standardized photographs, surveillance videos, 3D face images, identity card photographs of 241 male subjects from China were acquired. Sequencing results were modified by modeling selected individual features from the DMV altas. The improved method raises the accuracy of face recognition through anthroposophic or morphologic theory.