Biblio

This work describes the architecture and prototype implementation of a novel trust-aware continuous authorization technology that targets consumer Internet of Things (IoT), e.g., Smart Home. Our approach extends previous authorization models in three complementary ways: (1) By incorporating trust-level evaluation formulae as conditions inside authorization rules and policies, while supporting the evaluation of such policies through the fusion of an Attribute-Based Access Control (ABAC) authorization policy engine with a Trust-Level-Evaluation-Engine (TLEE). (2) By introducing contextualized, continuous monitoring and re-evaluation of policies throughout the authorization life-cycle. That is, mutable attributes about subjects, resources and environment as well as trust levels that are continuously monitored while obtaining an authorization, throughout the duration of or after revoking an existing authorization. Whenever change is detected, the corresponding authorization rules, including both access control rules and trust level expressions, are re-evaluated.(3) By minimizing the computational and memory footprint and maximizing concurrency and modular evaluation to improve performance while preserving the continuity of monitoring. Finally we introduce an application of such model in Zero Trust Architecture (ZTA) for consumer IoT.

Zero Trust Model ensures each node is responsible for the approval of the transaction before it gets committed. The data owners can track their data while it’s shared amongst the various data custodians ensuring data security. The consensus algorithm enables the users to trust the network as malicious nodes fail to get approval from all nodes, thereby causing the transaction to be aborted. The use case chosen to demonstrate the proposed consensus algorithm is the college placement system. The algorithm has been extended to implement a diversified, decentralized, automated placement system, wherein the data owner i.e. the student, maintains an immutable certificate vault and the student’s data has been validated by a verifier network i.e. the academic department and placement department. The data transfer from student to companies is recorded as transactions in the distributed ledger or blockchain allowing the data to be tracked by the student.

The increased use of cloud services and its various security and privacy challenges such as identity theft, data breach, data integrity and data confidentiality has made trust management, which is one of the most multifaceted aspect in cloud computing, inevitable. The growing reputation of cloud computing technology makes it immensely important to be acquainted with the meaning of trust in the cloud, as well as identify how the customer and the cloud service providers establish that trust. The traditional trust management mechanisms represent a static trust relationship which falls deficit while meeting up the dynamic requirement of cloud services. In this paper, a conceptual zero trust strategy for the cloud environment has been proposed. The model offers a conceptual typology of perceptions and philosophies for establishing trust in cloud services. Further, importance of trust establishment and challenges of trust in cloud computing have also been explored and discussed.

Research on the design of data center infrastructure is increasing, both from academia and industry, due to the rapid development of cloud-based applications such as search engines, social networks, and large-scale computing. On a large scale, data centers can consist of hundreds to thousands of servers that require systems with high-performance requirements and low downtime. To meet the network's needs in a dynamic data center, infrastructure of applications and services are growing. It takes a process of designing a network topology so that it can guarantee availability and security. One way to surmount this is by implementing the zero trust security model based on micro-segmentation. Zero trust is a security idea based on the principle of "never trust, always verify" in which no concepts of trust and untrust in network traffic. The zero trust security model implemented network traffic in the form of untrust. Micro-segmentation is a way to achieve zero trust by dividing a network into smaller logical segments to restrict the traffic. In this research, data center network performance based on software-defined networking with zero trust security model using micro-segmentation has been evaluated using a testbed simulation of Cisco Application Centric Infrastructure by measuring the round trip time, jitter, and packet loss during experiments. Performance evaluation results show that micro-segmentation adds an average round trip time of 4 μs and jitter of 11 μs without packet loss so that the security can be improved without significantly affecting network performance on the data center.

LBSs are Location-Based Services that provide certain service based on the current or past user's location. During the past decade, LBSs have become more popular as a result of the widespread use of mobile devices with position functions. Location information is a secondary information that can provide personal insight about one's life. This issue associated with sharing of data in cloud-based locations. For example, a hospital is a public space and the actual location of the hospital does not carry any sensitive information. However, it may become sensitive if the specialty of the hospital is analyzed. In this paper we proposed design presents a combination of methods for providing data privacy protection for location-based services (LBSs) with the use of cloud service. The work built in zero trust and we start to manage the access to the system through different levels. The proposal is based on a model that stores user location data in supplementary servers and not in non-trustable third-party applications. The approach of the present research is to analyze the privacy protection possibilities through data partitioning. The data collected from the different recourses are distributed into different servers according to the partitioning model based on multi-level policy. Access is granted to third party applications only to designated servers and the privacy of the user profile is also ensured in each server, as they are not trustable.

The Internet of Things (IoT) has revolutionized the way of how pervasive computing devices communicate and disseminate information over the global network. A plethora of user data is collected and logged daily into cloud-based servers. Such data can be analyzed by the IoT infrastructure to capture users' behaviors (e.g. users' location, tagging of smart home occupancy). This brings a new set of security challenges, specifically user anonymity. Existing access control and authentication technologies failed to support user anonymity. They relied on the surrendering of the device/user authentication parameters to the trusted server, which hence could be utilized by the IoT infrastructure to track users' behavioral patterns. This paper, presents two novel configurable privacy-preserving authentication schemes. User anonymity capabilities were incorporated into our proposed authentication schemes through the implementation of two crypto-based approaches (i) Zero Knowledge Proof (ZKP) and (ii) Verifiable Common Secret Encoding (VCSE). We consider a user-oriented approach when determining user anonymity. The proposed authentication schemes are dynamically capable of supporting various levels of user privacy based on the user preferences. To validate the two schemes, they were fully implemented and deployed on an IoT testbed. We have tested the performance of each proposed schemes in terms of power consumption and computation time. Based on our performance evaluation results, the proposed ZKP-based approach provides better performance compared to the VCSE-based approach.

Context-based zero-interaction has become the trend for smart IoT device pairing. In this paper, we propose a secure and usable mechanism to authenticate devices co-located in smart home scenario, and build a secure communication channel between legitimate devices by utilizing on-board microphones to capture a common audio context. After receiving randomly generated sound signals, smart IoT device uses the time intervals between salient sound signals to derive audio fingerprint which can be matched among co-present devices and then be used to bootstrap trust of the devices. The protocol is based on the idea that devices co-located within a physical security boundary (e.g., single family house) can hear similar sounds, and the devices outside would miss parts of sound signals due to the attenuation when sounds pass through the wall. To accelerate the generation rate of audio fingerprint, an extra sound source is introduced. We implement our protocol on Android devices, and the experiment results show that the protocol can distinguish the malicious devices outside from the legitimate devices located inside a security boundary and can quickly establish a strong secret-key between legitimate devices.

The IoT industry has developed rapidly in recent years, which has attracted the attention of security researchers. However, the researchers are hampered by the wide variety of IoT device operating systems and their hardware architectures. Especially for the lightweight IoT devices, many manufacturers do not provide the device firmware images, embedded firmware source code or even the develop documents. As a result, it hinders traditional static analysis and dynamic analysis techniques. In this paper, we propose a novel dynamic analysis framework, called FIoT, which aims at finding memory corruption vulnerabilities in lightweight IoT device firmware images. The key idea is dynamically run the binary code snippets through symbolic execution with carrying out a fuzzing test. Specifically, we generate code snippets through traversing the control-flow graph (CFG) in a backward manner. We improved the CFG recovery approach and backward slice approach for better performance. To reduce the influence of the binary firmware, FIoT leverages loading address determination analysis and library function identification approach. We have implemented a prototype of FIoT and conducted experiments. Our results show that FIoT can complete the Fuzzing test within 40 seconds in average. Considering 170 seconds for static analysis, FIoT can load and analyze a lightweight IoT firmware within 210 seconds in total. Furthermore, we illustrate the effectiveness of FIoT by applying it over 115 firmware images from 17 manufacturers. We have found 35 images exist memory corruptions, which are all zero-day vulnerabilities.

A number of solutions have been proposed to tackle the user privacy-preserving issue. Most of existing schemes, however, focus on methodology and techniques from the perspective of data processing. In this paper, we propose a lightweight privacy-preserving scheme for user identity from the perspective of data user and applied cryptography. The basic idea is to break the association relationships between User identity and his behaviors and ensure that User can access data or services as usual while the real identity will not be revealed. To this end, an interactive zero-knowledge proof protocol of identity is executed between CSP and User. Besides, a trusted third-party is introduced to manage user information, help CSP to validate User identity and establish secure channel between CSP and User via random shared key. After passing identity validation, User can log into cloud platform as usual without changing existing business process using random temporary account and password generated by CSP and sent to User by the secure channel which can further obscure the association relationships between identity and behaviors. Formal security analysis and theoretic and experimental evaluations are conducted, showing that the proposal is efficient and practical.

Phishing websites have long been a serious threat to cyber security. For decades, many researchers have been devoted to developing novel techniques to detect phishing websites automatically. While state-of-the-art solutions can achieve superior performances, they require substantial manual feature engineering and are not adept at detecting newly emerging phishing attacks. Therefore, developing techniques that can detect phishing websites automatically and handle zero-day phishing attacks swiftly is still an open challenge in this area. In this work, we propose PhishingNet, a deep learning-based approach for timely detection of phishing Uniform Resource Locators (URLs). Specifically, we use a Convolutional Neural Network (CNN) module to extract character-level spatial feature representations of URLs; meanwhile, we employ an attention-based hierarchical Recurrent Neural Network(RNN) module to extract word-level temporal feature representations of URLs. We then fuse these feature representations via a three-layer CNN to build accurate feature representations of URLs, on which we train a phishing URL classifier. Extensive experiments on a verified dataset collected from the Internet demonstrate that the feature representations extracted automatically are conducive to the improvement of the generalization ability of our approach on newly emerging URLs, which makes our approach achieve competitive performance against other state-of-the-art approaches.

A key challenge of the embedded era is to ensure trust in reuse of intellectual properties (IPs), which facilitates reduction of design cost and meeting of stringent marketing deadlines. Determining source of the IPs or their authenticity is a key metric to facilitate safe reuse of IPs. Though physical unclonable functions solves this problem for application specific integrated circuit (ASIC) IPs, authentication strategies for reconfigurable IPs (RIPs) or IPs of reconfigurable hardware platforms like field programmable gate arrays (FPGAs) are still in their infancy. Existing authentication techniques for RIPs that relies on verification of proof of authentication (PoA) mark embedded in the RIP by the RIP producers, leak useful clues about the PoA mark. This results in replication and implantation of the PoA mark in fake RIPs. This not only causes loss to authorized second hand RIP users, but also poses risk to the reputation of the RIP producers. We propose a zero knowledge authentication strategy for safe reusing of RIPs. The PoA of an RIP producer is kept secret and verification is carried out based on traversal times from the initial point to several intermediate points of the embedded PoA when the RIPs configure an FPGA. Such delays are user specific and cannot be replicated as these depend on intrinsic properties of the base semiconductor material of the FPGA, which is unique and never same as that of another FPGA. Experimental results validate our proposed mechanism. High strength even for low overhead ISCAS benchmarks, considered as PoA for experimentation depict the prospects of our proposed methodology.

Performing a fair exchange without a Trusted Third Party (TTP) was considered to be impossible. With multi party computation and practices like Proof-of-Work (PoW), blockchain accomplishes a fair exchange in a trustless network. Data confidentiality is a key challenge that has to be resolved before adopting blockchain for enterprise applications where tokenized assets will be transferred. Protocols like Zcash are already providing the same for financial transactions but lacks flexibility required to apply in most of the potential use cases of blockchain. Most of the real world application work in a way where a transaction is carried out when a particular action is performed. Also, the zero knowledge proof method used in Zcash, ZKSNARK has certain weaknesses restricting its adoption. One of the major drawbacks of ZKSNARK is that it requires an initial trust setup phase which is difficult to achieve in blockchain ecosystem. ZKSTARK, an interactive zero knowledge proof does not require this phase and also provides security against post quantum attacks. We propose a system that uses two indistinguishable hash functions along with ZKSTARK to improve the flexibility of blockchain platforms. The two indistinguishable hash functions are chosen from SHA3-finalists based on their security, performance and inner designs.

Controllers of security-critical cyber-physical systems, like the power grid, are a very important class of computer systems. Attacks against the control code of a power-grid system, especially zero-day attacks, can be catastrophic. Earlier detection of the anomalies can prevent further damage. However, detecting zero-day attacks is extremely challenging because they have no known code and have unknown behavior. Furthermore, if data collected from the controller is transferred to a server through networks for analysis and detection of anomalous behavior, this creates a very large attack surface and also delays detection. In order to address this problem, we propose Reconstruction Error Distribution (RED) of Hardware Performance Counters (HPCs), and a data-driven defense system based on it. Specifically, we first train a temporal deep learning model, using only normal HPC readings from legitimate processes that run daily in these power-grid systems, to model the normal behavior of the power-grid controller. Then, we run this model using real-time data from commonly available HPCs. We use the proposed RED to enhance the temporal deep learning detection of anomalous behavior, by estimating distribution deviations from the normal behavior with an effective statistical test. Experimental results on a real power-grid controller show that we can detect anomalous behavior with high accuracy (\textbackslashtextgreater99.9%), nearly zero false positives and short (\textbackslashtextless; 360ms) latency.

Edge caching has received much attention as a promising technique to overcome the stringent latency and data hungry challenges in the future generation wireless networks. Meanwhile, full-duplex (FD) transmission can potentially double the spectral efficiency by allowing a node to receive and transmit simultaneously. In this paper, we study a cache-aided FD system via delivery time analysis and optimization. In the considered system, an edge node (EN) operates in FD mode and serves users via wireless channels. Two optimization problems are formulated to minimize the largest delivery time based on the two popular linear beamforming zero-forcing and minimum mean square error designs. Since the formulated problems are non-convex due to the self-interference at the EN, we propose two iterative optimization algorithms based on the inner approximation method. The convergence of the proposed iterative algorithms is analytically guaranteed. Finally, the impacts of caching and the advantages of the FD system over the half-duplex (HD) counterpart are demonstrated via numerical results.

In this paper, we aim at the automated unit coverage-based testing for embedded software. To achieve the goal, by analyzing the industrial requirements and our previous work on automated unit testing tool CAUT, we rebuild a new tool, SmartUnit, to solve the engineering requirements that take place in our partner companies. SmartUnit is a dynamic symbolic execution implementation, which supports statement, branch, boundary value and MC/DC coverage. SmartUnit has been used to test more than one million lines of code in real projects. For confidentiality motives, we select three in-house real projects for the empirical evaluations. We also carry out our evaluations on two open source database projects, SQLite and PostgreSQL, to test the scalability of our tool since the scale of the embedded software project is mostly not large, 5K-50K lines of code on average. From our experimental results, in general, more than 90% of functions in commercial embedded software achieve 100% statement, branch, MC/DC coverage, more than 80% of functions in SQLite achieve 100% MC/DC coverage, and more than 60% of functions in PostgreSQL achieve 100% MC/DC coverage. Moreover, SmartUnit is able to find the runtime exceptions at the unit testing level. We also have reported exceptions like array index out of bounds and divided-by-zero in SQLite. Furthermore, we analyze the reasons of low coverage in automated unit testing in our setting and give a survey on the situation of manual unit testing with respect to automated unit testing in industry.

Modern personal computers have embraced increasingly powerful Graphics Processing Units (GPUs). Recently, GPU-based graphics acceleration in web apps (i.e., applications running inside a web browser) has become popular. WebGL is the main effort to provide OpenGL-like graphics for web apps and it is currently used in 53% of the top-100 websites. Unfortunately, WebGL has posed serious security concerns as several attack vectors have been demonstrated through WebGL. Web browsers\guillemotright solutions to these attacks have been reactive: discovered vulnerabilities have been patched and new runtime security checks have been added. Unfortunately, this approach leaves the system vulnerable to zero-day vulnerability exploits, especially given the large size of the Trusted Computing Base of the graphics plane. We present Sugar, a novel operating system solution that enhances the security of GPU acceleration for web apps by design. The key idea behind Sugar is using a dedicated virtual graphics plane for a web app by leveraging modern GPU virtualization solutions. A virtual graphics plane consists of a dedicated virtual GPU (or vGPU) as well as all the software graphics stack (including the device driver). Sugar enhances the system security since a virtual graphics plane is fully isolated from the rest of the system. Despite GPU virtualization overhead, we show that Sugar achieves high performance. Moreover, unlike current systems, Sugar is able to use two underlying physical GPUs, when available, to co-render the User Interface (UI): one GPU is used to provide virtual graphics planes for web apps and the other to provide the primary graphics plane for the rest of the system. Such a design not only provides strong security guarantees, it also provides enhanced performance isolation.

Query authentication has been extensively studied to ensure the integrity of query results for outsourced databases, which are often not fully trusted. However, access control, another important security concern, is largely ignored by existing works. Notably, recent breakthroughs in cryptography have enabled fine-grained access control over outsourced data. In this paper, we take the first step toward studying the problem of authenticating relational queries with fine-grained access control. The key challenge is how to protect information confidentiality during query authentication, which is essential to many critical applications. To address this challenge, we propose a novel access-policy-preserving (APP) signature as the primitive authenticated data structure. A useful property of the APP signature is that it can be used to derive customized signatures for unauthorized users to prove the inaccessibility while achieving the zero-knowledge confidentiality. We also propose a grid-index-based tree structure that can aggregate APP signatures for efficient range and join query authentication. In addition to this, a number of optimization techniques are proposed to further improve the authentication performance. Security analysis and performance evaluation show that the proposed solutions and techniques are robust and efficient under various system settings.

Zero-knowledge SNARKs (zk-SNARKs) are non-interactive proof systems with short and efficiently verifiable proofs. They elegantly resolve the juxtaposition of individual privacy and public trust, by providing an efficient way of demonstrating knowledge of secret information without actually revealing it. To this day, zk-SNARKs are being used for delegating computation, electronic cryptocurrencies, and anonymous credentials. However, all current SNARKs implementations rely on pre-quantum assumptions and, for this reason, are not expected to withstand cryptanalitic efforts over the next few decades. In this work, we introduce the first designated-verifier zk-SNARK based on lattice assumptions, which are believed to be post-quantum secure. We provide a generalization in the spirit of Gennaro et al. (Eurocrypt'13) to the SNARK of Danezis et al. (Asiacrypt'14) that is based on Square Span Programs (SSPs) and relies on weaker computational assumptions. We focus on designated-verifier proofs and propose a protocol in which a proof consists of just 5 LWE encodings. We provide a concrete choice of parameters as well as extensive benchmarks on a C implementation, showing that our construction is practically instantiable.

Nuclear weapons have re-emerged as one the main global security challenges of our time. Any further reductions in the nuclear arsenals will have to rely on robust verification mechanisms. This requires, in particular, trusted measurement systems to confirm the authenticity of nuclear warheads based on their radiation signatures. These signatures are considered extremely sensitive information, and inspection systems have to be designed to protect them. To accomplish this task, so-called information barriers" have been proposed. These devices process sensitive information acquired during an inspection, but only display results in a pass/fail manner. Traditional inspection systems rely on complex electronics both for data acquisition and processing. Several research efforts have produced prototype systems, but after almost thirty years of research and development, no viable and widely accepted system has emerged. This talk highlights recent efforts to overcome this impasse. A first approach is to avoid electronics in critical parts of the measurement process altogether and to rely instead on physical phenomena to detect radiation and to confirm a unique fingerprint of the inspected warhead using a zero-knowledge protocol. A second approach is based on a radiation detection system using vintage electronics built around a 6502 processor. Hardware designed in the distant past, at a time when its use for sensitive measurements was never envisioned, may drastically reduce concerns that another party implemented backdoors or hidden switches. Sensitive information is only stored on traditional punched cards. The talk concludes with a roadmap and highlights opportunities for researchers from the hardware security community to make critical contributions to nuclear arms control and global security in the years ahead.

With the rapid development of big data technology, the requirement of data processing capacity and efficiency result in failure of a number of legacy security technologies, especially in the data security domain. Data security risks became extremely important for big data usage. We introduced a novel method to preform big data security control, which comprises three steps, namely, user context recognition based on zero trust, fine-grained data access authentication control, and data access audit based on full network traffic to recognize and intercept risky data access in big data environment. Experiments conducted on the fine-grained big data security method based on the zero trust model of drug-related information analysis system demonstrated that this method can identify the majority of data security risks.

Internet of Things (IoT) is experiencing exponential scalability. This scalability introduces new challenges regarding management of IoT networks. The question that emerges is how we can trust the constrained infrastructure that shortly is expected to be formed by millions of 'things.' The answer is not to trust. This research introduces Amatista, a blockchain-based middleware for management in IoT. Amatista presents a novel zero-trust hierarchical mining process that allows validating the infrastructure and transactions at different levels of trust. This research evaluates Amatista on Edison Arduino Boards.

The evolution of the enterprise computing landscape towards emerging trends such as fog/edge computing and the Industrial Internet of Things (IIoT) are leading to a change of approach to securing computer networks to deal with challenges such as mobility, virtualized infrastructures, dynamic and heterogeneous user contexts and transaction-based interactions. The uncertainty introduced by such dynamicity introduces greater uncertainty into the access control process and motivates the need for risk-based access control decision making. Thus, the traditional perimeter-based security paradigm is increasingly being abandoned in favour of a so called "zero trust networking" (ZTN). In ZTN networks are partitioned into zones with different levels of trust required to access the zone resources depending on the assets protected by the zone. All accesses to sensitive information is subject to rigorous access control based on user and device profile and context. In this paper we outline a policy enforcement framework to address many of open challenges for risk-based access control for ZTN. We specify the design of required policy languages including a generic firewall policy language to express firewall rules. We design a mechanism to map these rules to specific firewall syntax and to install the rules on the firewall. We show the viability of our design with a small proof-of-concept.

Recently, attribute-based access control (ABAC) has emerged as a convenient paradigm for specifying, enforcing and maintaining rich and flexible authorization policies, leveraging attributes originated from multiple sources, e.g., operative systems, software modules, remote services, etc. However, attackers may try to bypass ABAC policies by compromising such sources to forge the attributes they provide, e.g., by deliberately manipulating the data contained within those attributes at will, in an effort to gain unintended access to sensitive resources as a result. In such a context, performing a proper risk assessment of ABAC policies, taking into account their enlisted attributes as well as their corresponding sources, becomes highly convenient to overcome zero-day security incidents or vulnerabilities, before they can be later exploited by attackers. With this in mind, we introduce RiskPol, an automated risk assessment framework for ABAC policies based on dynamically combining previously-assigned trust scores for each attribute source, such that overall scores at the policy level can be later obtained and used as a reference for performing a risk assessment on each policy. In this paper, we detail the general intuition behind our approach, its current status, as well as our plans for future work.