Third-party Infrastructure Research Articles

Security frameworks without the third-party infrastructure in VANETs with detailed security strength and overhead analyses

Two cluster-based security frameworks without a third-party infrastructure in VANETs are proposed and analyzed in this paper. Utilizing some concepts of PCREF [1] with suitable modification reaches our first proposed framework for serving as a benchmark. By further associating trust values with vehicles and taking key updates into account based on the first proposed framework, our second proposed framework is devised and serves as the most recommended framework. Through a theoretical approach, the security strength (regarding confidentiality, data authenticity, data availability, and the expected filtering position of the bogus report) and the overheads of storage, computation, and communication are thoroughly analyzed for both our proposed frameworks. Illustrated by numerical examples, the security strength and overhead are checked in detail for the two proposed frameworks to reach a constructive recommendation.

RESCU-SQL: Oblivious Querying for the Zero Trust Cloud

Cloud service providers offer robust infrastructure for rent to organizations of all kinds. High stakes applications, such as the ones in defense and healthcare, are turning to the public cloud for a cost-effective, geographically distributed, always available solution to their hosting needs. Many such users are unwilling or unable to delegate their data to this third-party infrastructure. In this demonstration, we introduce RESCU-SQL, a zero-trust platform for resilient and secure SQL querying outsourced to one or more cloud service providers. RESCU-SQL users can query their DBMS using cloud infrastructure alone without revealing their private records to anyone. It does so by executing the query over secure multiparty computation. We call this system zero trust because it can tolerate any number of malicious servers provided one of them remains honest. Our demo will offer an interactive dashboard with which attendees can observe the performance of RESCU-SQL deployed on several in-cloud nodes for the TPC-H benchmark. Attendees can select a computing party and inject messages from it to explore how quickly it detects and reacts to a malicious party. This is the first SQL system to support all-but-one maliciously secure querying over a semi-honest coordinator for efficiency.

Analysis of Cyber Security Aspects of Data Transmission in Large-Scale Networks Based on the LoRaWAN Protocol Intended for Monitoring Critical Infrastructure Sensors

Cyber security is nowadays synonymous with the reliability of elements connected to the internet. Better control of factories, security systems or even individual sensors is possible through the use of Internet of Things technology. The security of the aforementioned structures and the data they transmit has been a major concern in the development of IoT solutions for wireless data transmission. If we add to this prospect of low-cost end devices, we can seriously consider implementing such solutions in critical infrastructure areas. This article aims to assess the state of the art and experience and identify the main risks and directions for further development in order to improve the cyber security situation of LoRaWAN-based networks. LoRaWAN meets the three key requirements of IoT applications (low cost, large-scale deployability, high energy efficiency) through an open standard and the construction of autonomous networks without third-party infrastructure. However, many research issues remain to be solved/improved such as resource allocation, link coordination, transmission reliability, performance and, above all, security. Thus, we have defined a research gap in the area of LoRaWAN security. The contribution of this work is to structure the knowledge in the field of LoRaWAN security, based on previous publications and our own experience, in order to identify challenges and their potential solutions. This will help move LoRaWAN security research to the next stage.

Towards Oblivious Guidance Systems for Autonomous Vehicles

We consider encrypted guidance systems for straight-line path following. With cloud-computing technology, we can outsource computations in guidance systems to third-party providers, increasing scalability and enabling guidance-as-a-service. However, by remotely hosting a conventional guidance system on third-party infrastructure, we leak information such as the path of the vehicle. Potential customers may consider these leaks a serious breach of confidentiality, which limits the practical use of such systems. Therefore, to make cloud-based guidance systems viable, we would like to design guidance systems that we can host remotely without revealing confidential information to the host. To this end, we show that we can construct guidance systems that operate on encrypted position measurements, encrypted waypoints, and the bearing between each waypoint by using homomorphic encryption, effectively preventing the cloud host from identifying the vehicle's position. We show that the proposed guidance laws are locally exponentially stable and that the induced computational latency is appropriate for the real-time guidance of autonomous vehicles. Through field experiments, we demonstrate that an encrypted guidance system is practical and allows an unmanned surface vehicle to follow an encrypted path. The originality of this work lies in conceptualizing, designing, and experimentally validating an encrypted guidance system, unlike other studies that considered encrypted control systems.

Blockchain-Enabled NextGen Service Architecture for Mobile Internet Offload

The amalgamation of heterogeneous generations of mobile cellular networks around the globe has resulted in diverse data speed experiences for end users. At present, there are no defined mechanisms in place for subscribers of a mobile network operator (MNO) to use the services of third-party WiFi providers. MNOs also have no standardized procedures to securely interact with each other, and allow their subscribers to use third-party services on a pay-as-you-go basis. This paper proposes a blockchain-enabled offloading framework that allows a subscriber of a mobile operator to temporarily use another MNO or WiFi provider’s higher-speed network. A smart contract is employed to allow diverse entities, such as MNOs, brokers and WiFi providers, to automatically execute mutual agreements, to enable the utilization of third-party infrastructure in a secure and controlled manner. The proposed framework is tested using Ethereum’s testnet on the Goerli network using Alchemy and Hardhat. The analysis of the results obtained shows that the proposed technique helps mobile operators to offer improved user experience in the form of average speed and latency. The experiments show that the average time taken to deliver a 500 MB file is reduced from 10.23 s to 0.91 s for the global average scenario, from 6.09 s to 0.50 s for 5G, from 13.50 s to 0.50 s for 4G-LTE, from 41.11 s to 0.49 s for 4G, and from 339.11 s to 0.49 s for the 3G scenario. The results also show that, with WiFi offloading, users from all cellular generations can enjoy a similar quality of services, because delivery time ranges from 0.49 s to 0.91 s for offloaded experiments whereas for the non-offloaded scenario it ranges from 6.09 s to 339.11 s.

BIoMT: A State-of-the-Art Consortium Serverless Network Architecture for Healthcare System Using Blockchain Smart Contracts

Owing to the sensitive nature of healthcare data, the aforementioned approach to transferring patient data to central servers creates serious security and privacy issues. In addition, blockchain distributed ledger technology has introduced immutable storage and decentralized data management capability, which handles a large number of distributed nodes of E-Healthcare transactions via a serverless network, but in a limited manner because of blockchain-enabled resources. In this scenario, the medical industries are concerned about constituting an innovation in health information preservation and exchanging service delivery protocols without the connectivity of an untrusted third-party infrastructure. In this study, we proposed a blockchain hyperledger fabric-enabled consortium architecture called BIoMT, which provides security, integrity, transparency, and provenance to health-related transactions and exchanges sensitive clinical information in a serverless peer-to-peer (P2P) secure network environment. A consensus is designed and created to reduce the rate of blockchain resource constraints on the Internet of Medical Things (IoMT). The privacy of individual health transactions before sharing is protected using the NuCypher Re-Encryption mechanism, which increases security and provides medical ledger integrity and transparency. Smart contracts are created and deployed to automate device registration, exchange transactions, and ledger preservation in immutable storage (filecoin) after cross verification and validation. The experimental results show that the proposed BIoMT reduces the computational cost by 26.13%, and the robust medical node generation increases to 60.37%. Thus, only 31.79% and 74.21% of IoMT-related information and serverless P2P network usage are maintained and saved, respectively.

FO-Sketch: A Fast Oblivious Sketch for Secure Network Measurement Service in the Cloud

Network measurements are the foundation for network applications. The metrics generated by those measurements help applications improve their performance of the monitored network and harden their security. As severe network attacks using leaked information from a public cloud exist, it raises privacy and security concerns if directly deployed in network measurement services in a third-party public cloud infrastructure. Recent studies, most notably OblivSketch, demonstrated the feasibility of alleviating those concerns by using trusted hardware and Oblivious RAM (ORAM). As their performance is not good enough, and there are certain limitations, they are not suitable for broad deployment. In this paper, we propose FO-Sketch, a more efficient and general network measurement service that meets the most stringent security requirements, especially for a large-scale network with heavy traffic volume and burst traffic. Let a mergeable sketch update the local flow statistics in each local switch; FO-Sketch merges (in an Intel SGX-created enclave) these sketches obliviously to form a global “one big sketch” in the cloud. With the help of Oblivious Shuffle, Divide and Conquer, and SIMD speedup, we optimize all of the critical routines in our FO-Sketch to make it 17.3x faster than a trivial oblivious solution. While keeping the same level of accuracy and packet processing throughput as non-oblivious Elastic Sketch, our FO-Sketch needs only ∼4.5 MB enclave memory space in total to record metrics and for PORAM to store the global sketch in the cloud. Extensive experiments demonstrate that, for the recommended setting, it takes only ∼ 0.6 s in total to rebuild those data during each measurement interval.

NN-EMD: Efficiently Training Neural Networks Using Encrypted Multi-Sourced Datasets

Training complex neural network models using third-party cloud-based infrastructure among multiple data sources is a promising approach among existing machine learning solutions. However, privacy concerns of large-scale data collections and recent regulations have restricted the availability and use of privacy sensitive data in the third-party infrastructure. To address such privacy issues, a promising emerging approach is to train a neural network model over an encrypted dataset. Specifically, the model training process can be outsourced to a third party such as a cloud service that is backed by significant computing power, while the encrypted training data keeps the data confidential from the third party. Compared to training a traditional machine learning model over encrypted data, however, it is extremely challenging to train a deep neural network (DNN) model over encrypted data for two reasons: first, it requires large-scale computation over huge datasets; second, the existing solutions for computation over encrypted data, such as using homomorphic encryption, is inefficient. Further, for enhanced performance of a DNN model, we also need to use huge training datasets composed of data from multiple data sources that may not have pre-established trust relationships among each other. We propose a novel framework, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NN-EMD</i> , to train DNN over <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">encrypted multiple datasets</i> collected from multiple sources. Toward this, we propose a set of secure computation protocols using hybrid functional encryption schemes. We evaluate our framework for performance with regards to the training time and model accuracy on the MNIST datasets. We show that compared to other existing frameworks, our proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NN-EMD</i> framework can significantly reduce the training time, while providing comparable model accuracy and privacy guarantees as well as supporting multiple data sources. Furthermore, the depth and complexity of neural networks do not affect the training time despite introducing a privacy-preserving <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NN-EMD</i> setting.

A Study on Identification of Development Status of MASS Technologies and Directions of Improvement

The introduction of the maritime autonomous surface ship (MASS) to the maritime industry will open up a new era and bring about a new paradigm shift in terms of cost efficiency, maritime accidents, and human resources. Various studies are currently being conducted to realize the MASS. Understanding the scope and direction of these studies will be of great help for future MASS research. In this study, the current development status of technologies for autonomous ships is identified, and considerations and directions of improvement are suggested for six major research fields that cover all technological issues of MASS. Firstly, the results of the regulatory scoping exercise (RSE) on the International Maritime Organization (IMO) conventions to accept MASSs are identified; in particular, human elements are identified as vital issues to be considered for the design and operation of MASSs. Secondly, various studies on the decision-making system are identified, and the future direction is suggested. Thirdly, in terms of ship design and propulsion system, design changes for autonomous cargo ships are investigated, with their potential impacts to be considered. Fourthly, the communication system will need to be robust and supported by multiple systems to minimize potential risk with third-party infrastructures, and suitable protection of systems, networks, and data will be required as an integral part of the safety system for cybersecurity. Fifthly, issues of maintenance and repair are identified, with a maintenance strategy to be considered. Lastly, hazard analysis of the autonomous ship is explored, and system-theoretic process analysis (STPA) and the functional resonance analysis method (FRAM) are identified as the most representative new methods that can be used for hazard analysis of autonomous ships.

Negative Resistance Converter Traction Power System for Reducing Rail Potential and Stray Current in the Urban Rail Transit

In the urban rail transit, the rail current leaks into the earth inevitably due to the incomplete rail-to-earth insulation. It then forms rail potential and stray current issues, which endanger the personal safety and cause metallic corrosions on running rails and the third-party infrastructures. For mitigating the rail potential and stray current, this article proposes a negative resistance converter traction power system (NRC-TPS) to reduce their generating sources by providing zero-resistance loops to the rail current. NRC-TPS is constructed by installing power electronics equipment directly in the conventional traction power system (CON-TPS) without modifying trains, rails, or tunnels, and so on. The operation principles, control strategies, and mitigation performance of NRC-TPS are discussed in detail. The modeling, simulation, and experimental results indicate that the rail potential and stray current are reduced in NRC-TPS compared with those of CON-TPS. The feasibility and practicability of NRC-TPS are also proved through these results.

A Survey on LoRa Networking: Research Problems, Current Solutions, and Open Issues

Wireless networks have been widely deployed for many Internet-of-Things (IoT) applications, like smart cities and precision agriculture. Low Power Wide Area Networking (LPWAN) is an emerging IoT networking paradigm to meet three key requirements of IoT applications, i.e., low cost, large scale deployment and high energy efficiency. Among all available LPWAN technologies, LoRa networking has attracted much attention from both academia and industry, since it specifies an open standard and allows us to build autonomous LPWAN networks without any third-party infrastructure. Many LoRa networks have been developed recently, e.g., managing solar plants in Carson City, Nevada, USA and power monitoring in Lyon and Grenoble, France. However, there are still many research challenges to develop practical LoRa networks, e.g., link coordination, resource allocation, reliable transmissions and security. This article provides a comprehensive survey on LoRa networks, including the technical challenges of deploying LoRa networks and recent solutions. Based on our detailed analysis of current solutions, some open issues of LoRa networking are discussed. The goal of this survey paper is to inspire more works on improving the performance of LoRa networks and enabling more practical deployments.

DC Interference Modeling for Assessing the Impact of Sustained DC Ground Faults of Photovoltaic Systems on Third-Party Infrastructure

In grounded photovoltaic (PV) systems, dc leakage to earth is formed by the contribution of the PV modules, inverters, and dc circuitry. Ground faults in such systems occur when there is an unintentional connection between any dc-carrying conductor with a grounded surface or earth. On the occurrence of ground faults, dc leakage will be intensified and will flow through the earth or other conductive paths, before returning back to the source. Thus, it is highly probable that some portion of the dc leakage will attempt to return to the source through any low-resistance paths that exist in the nearby vicinity, for example, through nearby buried pipelines. Therefore, unattended dc leakage may induce stray current corrosion concerns on these pipelines. To this extent, this paper presents a detailed dc interference modeling for assessing the dc leakage activity in PV systems—particularly under undetected fault conditions. First, the modeling carried out embraces the topologically accurate consideration of all sources of dc leakage that are associated with the grounded PV systems. A second feature presented in the paper is the modeling of blind-spot dc faults and their impact on third-party infrastructure.

Worst-case bounds and optimized cache on [formula omitted]th request cache insertion policies under elastic conditions

Cloud services and other shared third-party infrastructures allow individual content providers to easily scale their services based on current resource demands. In this paper, we consider an individual content provider that wants to minimize its delivery costs under the assumptions that the storage and bandwidth resources it requires are elastic, the content provider only pays for the resources that it consumes, and costs are proportional to the resource usage. Within this context, we (i) derive worst-case bounds for the optimal cost and competitive cost ratios of different classes of cache onMth request cache insertion policies, (ii) derive explicit average cost expressions and bounds under arbitrary inter-request distributions, (iii) derive explicit average cost expressions and bounds for short-tailed (deterministic, Erlang, and exponential) and heavy-tailed (Pareto) inter-request distributions, and (iv) present numeric and trace-based evaluations that reveal insights into the relative cost performance of the policies. Our results show that a window-based cache on 2nd request policy using a single threshold optimized to minimize worst-case costs provides good average performance across the different distributions and the full parameter ranges of each considered distribution, making it an attractive choice for a wide range of practical conditions where request rates of individual file objects typically are not known and can change quickly.

Survey on Communication and Networks for Autonomous Marine Systems

The rapid development of autonomous systems and Information and Communications Technologies (ICT) create new opportunities for maritime activities. Existing autonomous systems are becoming more powerful and utilise the capabilities of several types of devices such as Autonomous Underwater Vehicles (AUVs), Unmanned Surface Vehicles (USVs) – sometimes referred as Autonomous Surface Vehicles (ASVs) –, Unmanned Aerial Vehicles (UAVs), moored and drifting systems and, recently emerging, autonomous vessels. Their importance in providing new services in maritime environments is undeniable and the opportunity for coordinated and interconnected operations is clear. However, continuous wide integration of various technologies in maritime environments still faces many challenges. Operations may take place in remote locations, so that dependence on third-party infrastructures such as satellite communication or terrestrial communication systems must be expected. The reliability, performance, availability, and cost of such systems are important issues that need to be tackled. This work reviews the major advancements on state-of-the-art autonomous maritime vehicles and systems, which are used in several different scenarios, from scientific research to transportation. Moreover, the paper highlights how available technologies can be composed in order to efficiently and effectively operate in maritime environments. Highlights of the trade-off between autonomy and communication requirements are provided and followed by an overview of promising communication and networking technologies that could encourage the integration of autonomous systems in maritime scenarios.

Beyond the Certification Badge—How Infrastructure Sustainability Rating Tools Impact on Individual, Organizational, and Industry Practice

Sustainability consideration in designing, constructing, and operating civil infrastructure requires substantive action and yet progress is slow. This research examines the impact third-party infrastructure sustainability rating tools—specifically CEEQUAL, Envision, Greenroads, and Infrastructure Sustainability—have beyond individual project certification and considers their role in driving wider industry change. In this empirical study, engineering and sustainability professionals (n = 63) assess and describe their experience in using rating tools outside of formal certification and also the impact of tool use on their own practice and the practices of their home organizations. The study found that 77% of experienced users and 59% of infrastructure owners used the tools for purposes other than formal project certification. The research attests that rating tool use and indeed their very existence has a strong influence on sustainability awareness and practice within the infrastructure industry, providing interpretation of sustainability matters in ways that resonate with industry norms. The rating tools impact on individuals and their professional and personal practice, on the policies and practices of infrastructure-related organizations, and more widely on other industry stakeholders. The findings can be used to increase the value gained from sustainability rating tool use and to better understand the role such tools play in creating cultural change within the industry.

Privacy-preserving mobile crowd sensing in ad hoc networks

The presence of a rich set of embedded sensors on mobile devices has been propelling various sensing applications regarding individual activities and their surrounding environment, and these persuasive sensing-capable mobile devices are pushing the new paradigm of Mobile Crowd Sensing (MCS) from sketch to reality. MCS aims to outsource sensing data collection to Mobile Device Owner (MDO) and it could revolutionize the conventional ways of sensing data collection and processing. Nonetheless, the widespread deployment of MCS gives rise to the privacy concerns from both the MDOs and the Sensing Service Consumers (SSC), especially in the case where MCS relies on untrustworthy third-party infrastructures. This paper proposes three protocols to address the privacy issues of MCS in ad hoc network without depending on any third-parties. It first presents Privacy-Preserving Summation (PPS) protocol to protect the privacy of the SSCs. Next, it puts forward Privacy-Preserving Difference Rank Computation (PPDRC) protocol to ensure the privacy of the MDOs. Finally, it proposes Approximate K-Nearest Neighbor with Privacy Preservation(AKN2P2) to approximately identify the k-nearest neighbors without privacy leaks of both the MDOs and the SSCs. The performance evaluations demonstrate the computation overhead in different settings.

Software Defined Wi-V2G: A V2G Network Architecture

The valley-to-peak difference in power consumption is a crucial problem in load regulation and control for a power grid. By allowing electric vehicles (EVs) to charge during off-peak hours and feed power back into the grid during peak hours, Vehicle-to-Grid (V2G) technology can help to shave the power peak. Long-distance communication is essential for data exchange between dispersed EVs and charging stations for the realization of V2G systems. However, because of the high mobility of EVs, the highvolume data transmission required and the limitations of the third-party infrastructure, it is challenging to achieve efficient and effective communication. To address these challenges, we propose a new V2G network architecture based on software-defined networking (SDN) technology. (1) We use an IEEE 802.11 WiFibased long-distance (WiLD) network with the TDMA scheme as the backhaul network, and (2) we partially replace the road side units (RSUs) with some of the WiLD nodes to provide access for, and to rapidly broadcast data to, EVs. In addition, we propose: (3) a two-stage flow table mechanism and a double roaming mechanism to address the mobility demands of V2G network terminals; and (4) a rapid data transmission scheme for communication from charging stations to EVs. A testbed was built to validate the proposed network architecture. Experimental results show that the communication time delay is in the order of milliseconds and that the reliability is higher than 99.9%.

Unikernels Everywhere

Video streaming dominates the Internet's overall traffic mix, with reports stating that it will constitute 90% of all consumer traffic by 2019. Most of this video is delivered by Content Delivery Networks (CDNs), and, while they optimize QoE metrics such as buffering ratio and start-up time, no single CDN provides optimal performance. In this paper we make the case for elastic CDNs, the ability to build virtual CDNs on-the-fly on top of shared, third-party infrastructure at a scale. To bring this idea closer to reality we begin by large-scale simulations to quantify the effects that elastic CDNs would have if deployed, and build and evaluate MiniCache, a specialized, minimalistic virtualized content cache that runs on the Xen hypervisor. MiniCache is able to serve content at rates of up to 32 Gb/s and handle up to 600K reqs/sec on a single CPU core, as well as boot in about 90 milliseconds on x86 and around 370 milliseconds on ARM32.

Automated Negotiation for Decentralised Scheduling

This paper is concerned with inter-company scheduling between manufacturers of similar products who wish to pool resources for their mutual benefit, but without sharing commercially sensitive situation such as order lists and production technology with one another and without the need for a third-party brokerage infrastructure. The ultimate objective of this work is to enable small-scale manufacturers, which may be competitors in certain markets, to achieve some of the economies of scale of their larger counterparts. To accomplish this, the paper introduces a simple agent-negotiation approach that is able to identify mutually beneficial resource transactions.

Measurement Study of Netflix, Hulu, and a Tale of Three CDNs

Netflix and Hulu are leading Over-the-Top (OTT) content service providers in the US and Canada. Netflix alone accounts for 29.7% of the peak downstream traffic in the US in 2011. Understanding the system architectures and performance of Netflix and Hulu can shed light on the design of such large-scale video streaming platforms, and help improving the design of future systems. In this paper, we perform extensive measurement study to uncover their architectures and service strategies. Netflix and Hulu bear many similarities. Both Netflix and Hulu video streaming platforms rely heavily on the third-party infrastructures, with Netflix migrating that majority of its functions to the Amazon cloud, while Hulu hosts its services out of Akamai. Both service providers employ the same set of three content distribution networks (CDNs) in delivering the video contents. Using active measurement study, we dissect several key aspects of OTT streaming platforms of Netflix and Hulu, e.g., employed streaming protocols, CDN selection strategy, user experience reporting, etc. We discover that both platforms assign the CDN to a video request without considering the network conditions and optimizing the user-perceived video quality. We further conduct the performance measurement studies of the three CDNs employed by Netflix and Hulu. We show that the available bandwidths on all three CDNs vary significantly over the time and over the geographic locations. We propose a measurement-based adaptive CDN selection strategy and a multiple-CDN-based video delivery strategy that can significantly increase users' average available bandwidth.