Custom ROMs: Enhancing Enterprise-Grade Android Security

With the rapid development of computer and communication technology, embedded systems are widely used in smart devices. The increasing connectivity of these systems and the difficulties in providing comprehensive security have made such devices vulnerable to malicious attacks. Passive defense technologies and traffic-based intrusion detection technologies are not fully effective against such attacks. Trusted execution environment (TEE) technology can ensure system security against unknown attacks to some extent. Most researchers use TrustZone to implement TEE. However, the problem is that the API interface of the TEE module which provides the service is not secure. Therefore, to actively defend against attacks, we developed a trusted computing active measurement architecture based on TrustZone. To overcome the serious problem that modules in the trusted execution environment need to be passively invoked to provide services, we have proposed an active measurement closed-loop immune mechanism. To reduce the trusted computing base and reduce the performance overhead, we removed certain functional modules from the trusted execution environment. In addition, based on this architecture, we developed a trust chain and dynamic measurement method to ensure the security of the target applications. We changed the traditional attack response method, which requires the entire system to be restarted after an attack, by developing a fallback mechanism that is more suitable for the system. Finally, we verified the effectiveness of the architecture by developing an attack model. Performance testing and analysis showed that the architecture reduced the impact of the security mechanisms on the system. In the future, we will extend our research to more fine-grained measurements.

With the extensive deployment and application of the Internet of Things (IoT), 5G and 6G technologies and edge intelligence, the volume of data generated by IoT and the number of intelligence applications derived from these data are rapidly growing. However, the absence of effective mechanisms to safeguard the vast data generated by IoT, along with the security and privacy of edge intelligence applications, hinders their further development and adoption. In recent years, Trusted Execution Environment (TEE) has emerged as a promising technology for securing cloud data storage and cloud processing, demonstrating significant potential for ensuring data and application confidentiality in more scenarios. Nevertheless, applying TEE technology to enhance security in IoT and edge intelligence scenarios still presents several challenges. This paper investigates the technical challenges faced by current TEE solutions, such as performance overhead and I/O security issues, in the context of the resource constraints and data mobility that are inherent to IoT and edge intelligence applications. Using Intel Software Guard Extensions (SGX) technology as a case study, this paper validates these challenges through extensive experiments. The results provide critical assessments and analyses essential for advancing the development and usage of TEE in IoT and edge intelligence scenarios.

Trusted Execution Environments (TEEs) such as ARM TrustZone are in widespread use in both mobile and embedded devices, and they are used to protect sensitive secrets while often sharing the same computational hardware as untrusted code. Although there has been limited research in the area, the threat of microarchitectural attacks against ARM TrustZone has not been thoroughly studied. This is not the case for other TEEs, such as Intel SGX, where the security promises of the TEE have been violated numerous times by the academic community, showing that it is possible to use side-channel attacks to gain detailed insight into the microarchitectural behavior of trusted code. In this work, we show that TrustZone is susceptible to similar attacks, and we demonstrate the ability to achieve cache attacks with high temporal precision, high spatial precision, and low noise. These tools make it easy to monitor the data flow and code flow of TrustZone code with great resolution, and we apply our techniques to investigate the security of a real-world application. We examine ECDSA signing in Qualcomm's implementation of Android's hardware-backed keystore and identify a series of vulnerabilities that leak sensitive cryptographic information through shared microarchitectural structures. By using the powerful attacks developed in this paper, we are able to successfully extract this sensitive information and fully recover a 256-bit private key from Qualcomm's version of the hardware-backed keystore.

Threats to data processing in the cloud computing environment are increasing with emerging cloud applications. Intel Software Guard Extensions (SGX) implements a Trusted Execution Environment (TEE) through hardware-based isolation, which can enhance the security of cloud applications by providing a separate secure space that does not reveal its interior. A trusted memory area isolated by SGX, so-called enclave, protects the confidential code and data from any other software, including highly privileged system software. However, the adversary also benefits from isolation by inserting malicious code into the enclave where the system cannot detect it. Existing studies suggest various measures to deal with enclave malware, but these are based on an underlying assumption that the system must be trusted, which does not apply to the semi-honest cloud platform. To this end, we propose a novel method, named Interclave, that protects the system from enclave malware in the cloud computing environment without trusting any component other than the intermediate enclave. Interclave forces every ECALL and OCALL of a suspicious enclave to be executed through an intermediate enclave. This prevents the malicious behavior of enclave malware such as code-reuse attacks that modify the stack or register values associated with the program's execution address. Interclave stores and restores all stack and register values before and after the execution of the suspicious enclave, ensuring the integrity of these values. Our implementation is done with slight code modifications which occupy only a little storage capacity without complicated hardware changes.

Cyber-physical systems (CPS) are devices with sensors and actuators which link the physical with the virtual world. For CPS there is a strong trend towards open systems, which can be extended during operation by instantly adding functionalities on demand. The main goal of the TAPPS (Trusted Apps for open CPS) project is the development of a platform for CPS apps which can also access and modify safety critical device internals. As current, rich execution platforms for apps are limited in security, TAPPS will provide and validate an end-to-end solution for development and deployment of trusted apps. The project will develop a dedicated, real-time Trusted Execution Environment (TEE) for highly-trusted CPS apps. Additionally, TAPPS also includes an App Store and a model-based tool chain for trusted application development including verification tools. The multi-level trusted apps platform and tool chain are matured and validated in health and automotive application domains using industrial, realistic use cases paving the way for future exploitation in further demanding application domains.

Challenges and opportunities in modelling of proton exchange membrane fuel cells (PEMFC)

Current need to the development of advanced 28nm and beyond OTP (One-time-Programmable) nonvolatile memory is limited by the compatibility with advanced nodes with particular high-k metal-gate gate stack feature. On the other hand, there is a strong demand for future use in IoT and AI in security applications. In this talk, we will provide a solution on how to develop a unique structure of OTP for the above purpose. A 4kb macro of One Time Programming (OTP) memory, implemented by a new breakdown, named dielectric fuse (dFuse) breakdown, will be demonstrated which is constructed on a foundry pure logic 28nm HKMG CMOS platform. The feature size of a unit cell is 1.5T per cell with 7.5F2. The experimental results show that dFuse macro exhibits high programming (PGM) speed of 100ns at 4V, read time smaller than 10ns at 0.75V, and excellent data retention under one-month baking at 150°C. More importantly, the program voltage is weakly dependent on the environmental temperature, suitable for automotive applications. This OTP is also expected to be scalable to advanced technology node such as FinFET and provides an ideal and reliable solution for the storage purpose in IoT and 5G era. In particular, it has great potential for embedded applications in MCU, e.g., automotive applications.

This report summarizes the final product developed for the US DOE Small Business Innovation Research (SBIR) Phase II grant made to Engineering Mechanics Corporation of Columbus (Emc2) between April 16, 2014 and August 31, 2016 titled ‘Adoption of High Performance Computational (HPC) Modeling Software for Widespread Use in the Manufacture of Welded Structures’. Many US companies have moved fabrication and production facilities off shore because of cheaper labor costs. A key aspect in bringing these jobs back to the US is the use of technology to render US-made fabrications more cost-efficient overall with higher quality. One significant advantage that has emerged in the US over the last two decades is the use of virtual design for fabrication of small and large structures in weld fabrication industries. Industries that use virtual design and analysis tools have reduced material part size, developed environmentally-friendly fabrication processes, improved product quality and performance, and reduced manufacturing costs. Indeed, Caterpillar Inc. (CAT), one of the partners in this effort, continues to have a large fabrication presence in the US because of the use of weld fabrication modeling to optimize fabrications by controlling weld residual stresses and distortions and improving fatigue, corrosion, and fracture performance. This report describes Emc2’s DOE SBIR Phase II final results to extend an existing, state-of-the-art software code, Virtual Fabrication Technology (VFT®), currently used to design and model large welded structures prior to fabrication - to a broader range of products with widespread applications for small and medium-sized enterprises (SMEs). VFT® helps control distortion, can minimize and/or control residual stresses, control welding microstructure, and pre-determine welding parameters such as weld-sequencing, pre-bending, thermal-tensioning, etc. VFT® uses material properties, consumable properties, etc. as inputs. Through VFT®, manufacturing companies can avoid costly design changes after fabrication. This leads to the concept of joint design/fabrication where these important disciplines are intimately linked to minimize fabrication costs. Finally service performance (such as fatigue, corrosion, and fracture/damage) can be improved using this product. Emc2’s DOE SBIR Phase II effort successfully adapted VFT® to perform efficiently in an HPC environment independent of commercial software on a platform to permit easy and cost effective access to the code. This provides the key for SMEs to access this sophisticated and proven methodology that is quick, accurate, cost effective and available “on-demand” to address weld-simulation and fabrication problems prior to manufacture. In addition, other organizations, such as Government agencies and large companies, may have a need for spot use of such a tool. The open source code, WARP3D, a high performance finite element code used in fracture and damage assessment of structures, was significantly modified so computational weld problems can be solved efficiently on multiple processors and threads with VFT®. The thermal solver for VFT®, based on a series of closed form solution approximations, was extensively enhanced for solution on multiple processors greatly increasing overall speed. In addition, the graphical user interface (GUI) was re-written to permit SMEs access to an HPC environment at the Ohio Super Computer Center (OSC) to integrate these solutions with WARP3D. The GUI is used to define all weld pass descriptions, number of passes, material properties, consumable properties, weld speed, etc. for the structure to be modeled. The GUI was enhanced to make it more user-friendly so that non-experts can perform weld modeling. Finally, an extensive outreach program to market this capability to fabrication companies was performed. This access will permit SMEs to perform weld modeling to improve their competitiveness at a reasonable cost.

The ubiquitous nature and great potential of Wireless Sensors Network has not yet been fully exploited in automotive applications. This work deals with the choice of the cost-effective hardware required to face the challenges and issues proposed by the new trend in the development of intelligent transportation systems. With this aim, a preliminary WSN architecture is proposed. Several commercially available open-source platforms are compared and the Raspberry Pi stood out as a suitable and viable solution. The sensing layer is designed with two goals. Firstly, accelerometric, temperature, and relative humidity sensors were integrated on a dedicated PCB to test if mechanical or environmental stresses during bus rides could be harmful to the device or to its performances. The physical quantities are monitored automatically to alert the driver, thus improving the quality of service. Then, the rationale and functioning of the management and service layer is presented. The proposed cost-effective WSN node was employed and tested to transmit messages and videos, while investigating if any quantitative relationship exists between these operations and the environmental and operative conditions experienced by the hardware.

Undeniable signatures are a special kind of digital signature that need the active cooperation of the signer in the verification process, granting properties such as completeness, soundness, unforgeability, and invisibility. Moreover, these schemes techniques allow controlled verification in contrast to traditional signatures, meaning that they are perfect in situations when secrecy and selective disclosure are important. Over the years, undeniable signatures have undergone substantial evolution from the transition from classical encryption such as discrete logarithms to modern algorithms that include lattice-based, hash-based, and code-based post-quantum. In this article, focused on classification and taxonomy of undeniable signature schemes through evolution from early concepts to solutions that are resistant to quantum alterations. Unlike previous surveys, our taxonomy highlights distinctions between classical, convertible, and designated confirmer variants, while also introducing a post-quantum perspective rooted in lattice-based and code-based constructions. We analytically classify key properties for undeniable signature schemes whence inspect their strengths and weaknesses, also offer a methodical framework for comprehending the differences between them. Our contributions comprise a comprehensive taxonomy of undeniable signatures, considering various aspects such as security, efficiency, and practical applicability, as well as insights into potential areas future research paths, especially in terms of post- quantum systems. By displaying our views in this regard, our goal is to provide a more profound comprehension and guide ongoing and effective research in undeniable signature schemes.

This paper introduces a new task termed low-latency speaker spotting (LLSS). Related to security and intelligence applications, the task involves the detection, as soon as possible, of known speakers within multi-speaker audio streams. The paper describes differences to the established fields of speaker diarization and automatic speaker verification and proposes a new protocol and metrics to support exploration of LLSS. These can be used together with an existing, publicly available database to assess the performance of LLSS solutions also proposed in the paper. They combine online diarization and speaker detection systems. Diarization systems include a naive, over-segmentation approach and fully-fledged online diarization using segmental i-vectors. Speaker detection is performed using Gaussian mixture models, i-vectors or neural speaker embeddings. Metrics reflect different approaches to characterise latency in addition to detection performance. The relative performance of each solution is dependent on latency. When higher latency is admissible, i-vector solutions perform well; embeddings excel when latency must be kept to a minimum. With a need to improve the reliability of online diarization and detection, the proposed LLSS framework provides a vehicle to fuel future research in both areas. In this respect, we embrace a reproducible research policy; results can be readily reproduced using publicly available resources and open source codes.

A collaborative framework is presented to address the reliability challenges faced in a fabless-foundry environment. Examples are given to show the effectiveness of this framework for both infant mortality and long-term reliability risk. Through design-for-reliability, optimum process standardization and selective customization, defect density reduction and electrical screening, reliability of the highest level has been achieved in FPGA devices suitable for enterprise, automotive and aerospace applications, all in a fabless-foundry environment.

Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. Identifying kinship relations has also garnered interest due to several potential applications in security and surveillance and organizing and tagging the enormous number of videos being uploaded on the Internet. This dissertation has a five-fold contribution where first, a study is conducted to gain insight into the kinship verification process used by humans. Besides this, two separate deep learning based methods are proposed to solve kinship verification in images and videos. Other contributions of this research include interlinking face verification with kinship verification and creation of two kinship databases to facilitate research in this field. WVU Kinship Database is created which consists of multiple images per subject to facilitate kinship verification research. Next, kinship video (KIVI) database of more than 500 individuals with variations due to illumination, pose, occlusion, ethnicity, and expression is collected for this research. It comprises a total of 355 true kin video pairs with over 250,000 still frames. In this dissertation, a human study is conducted to understand the capabilities of human mind and to identify the discriminatory areas of a face that facilitate kinship-cues. The visual stimuli presented to the participants determines their ability to recognize kin relationship using the whole face as well as specific facial regions. The effect of participant gender, age, and kin-relation pair of the stimulus is analyzed using quantitative measures such as accuracy, discriminability index d′, and perceptual information entropy. Next, utilizing the information obtained from the human study, a hierarchical Kinship Verification via Representation Learning (KVRL) framework is utilized to learn the representation of different face regions in an unsupervised manner. We propose a novel approach for feature representation termed as filtered contractive deep belief networks (fcDBN). The proposed feature representation encodes relational information present in images using filters and contractive regularization penalty. A compact representation of facial images of kin is extracted as the output from the learned model and a multi-layer neural network is utilized to verify the kin accurately. The results show that the proposed deep learning framework (KVRL-fcDBN) yields state-of-the-art kinship verification accuracy on the WVU Kinship database and on four existing benchmark datasets. Additionally, we propose a new deep learning framework for kinship verification in unconstrained videos using a novel Supervised Mixed Norm regularization Autoencoder (SMNAE). This new autoencoder formulation introduces class-specific sparsity in the

Secure Nested Transactions are an adaptation of traditional nested transactions to support the synergy of language-based security and multi-level database security. They have application in security for enterprise applications, where transactional semantics are a critical feature in middleware systems. This article considers correctness in terms of transactional properties for secure nested transactions. Correctness is expressed in terms of a labeled transition system, the TauZero calculus.

Review of "High-Assurance Design: Architecting Secure and Reliable Enterprise Applications by Clifford Berg," Addison-Wesley Professional, 2005, $54.99, ISBN: 0321375777.