arXiv subjects

Spyridon Mastorakis

Publications and source records attributed to Spyridon Mastorakis.

44 records · Page 3Linked to original sources

arXiv2020

Named Data Networking for Content Delivery Network Workflows

In this work we investigate Named Data Networking's (NDN's) architectural properties and features, such as content caching and intelligent packet forwarding, in the context of a Content Delivery Network (CDN) workflows. More specifically, we evaluate NDN's properties for PoP (Point of Presence) to PoP and PoP to device connectivity. We use the Apache Traffic Server (ATS) platform to create an HTTP, CDN-like caching hierarchy in order to compare NDN with HTTP-based content delivery. Overall, our work demonstrates that properties inherent to NDN can benefit content providers and users alike. Our experimental results demonstrate that HTTP is faster under stable conditions due to a mature software stack. However, NDN performs better in the presence of packet loss, even for a loss rate as low as 0.1%, due to packet-level caching in the network and fast retransmissions from close upstreams and fast retransmissions from close upstreams. We further show that the Time To First Byte (TTFB) in NDN is consistently lower than HTTP (~100ms in HTTP vs ~50ms in NDN), a vital requirement for CDNs, in addition to supporting transparent failover to another upstream when a failure occurs in the network. Moreover, we examine implementation agnostic (implementation choices can be Software Defined Networking, Information Centric Networking, or something else) network properties that can benefit CDN workflows.

cs.NI↗

arXiv2020

DLWIoT: Deep Learning-based Watermarking for Authorized IoT Onboarding

The onboarding of IoT devices by authorized users constitutes both a challenge and a necessity in a world, where the number of IoT devices and the tampering attacks against them continuously increase. Commonly used onboarding techniques today include the use of QR codes, pin codes, or serial numbers. These techniques typically do not protect against unauthorized device access-a QR code is physically printed on the device, while a pin code may be included in the device packaging. As a result, any entity that has physical access to a device can onboard it onto their network and, potentially, tamper it (e.g.,install malware on the device). To address this problem, in this paper, we present a framework, called Deep Learning-based Watermarking for authorized IoT onboarding (DLWIoT), featuring a robust and fully automated image watermarking scheme based on deep neural networks. DLWIoT embeds user credentials into carrier images (e.g., QR codes printed on IoT devices), thus enables IoT onboarding only by authorized users. Our experimental results demonstrate the feasibility of DLWIoT, indicating that authorized users can onboard IoT devices with DLWIoT within 2.5-3sec.

cs.CR↗

arXiv2020

NDNTP: A Named Data Networking Time Protocol

Named Data Networking (NDN) architectural features, including multicast data delivery, stateful forwarding, and in-network data caching, have shown promise for applications such as video streaming and file sharing. However, collaborative applications, requiring a multi-producer participation introduce new NDN design challenges. In this paper, we highlight these challenges in the context of the Network Time Protocol (NTP) and one of its most widely-used deployments for NTP server discovery, the NTP pool project. We discuss the design requirements for the support of NTP and NTP pool and present general directions for the design of a time synchronization protocol over NDN, coined Named Data Networking Time Protocol (NDNTP).

cs.NI↗

arXiv2020

An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks

Digital image watermarking is the process of embedding and extracting a watermark covertly on a cover-image. To dynamically adapt image watermarking algorithms, deep learning-based image watermarking schemes have attracted increased attention during recent years. However, existing deep learning-based watermarking methods neither fully apply the fitting ability to learn and automate the embedding and extracting algorithms, nor achieve the properties of robustness and blindness simultaneously. In this paper, a robust and blind image watermarking scheme based on deep learning neural networks is proposed. To minimize the requirement of domain knowledge, the fitting ability of deep neural networks is exploited to learn and generalize an automated image watermarking algorithm. A deep learning architecture is specially designed for image watermarking tasks, which will be trained in an unsupervised manner to avoid human intervention and annotation. To facilitate flexible applications, the robustness of the proposed scheme is achieved without requiring any prior knowledge or adversarial examples of possible attacks. A challenging case of watermark extraction from phone camera-captured images demonstrates the robustness and practicality of the proposal. The experiments, evaluation, and application cases confirm the superiority of the proposed scheme.

cs.MM↗

arXiv2020

DAPES: Named Data for Off-the-Grid File Sharing with Peer-to-Peer Interactions

This paper introduces DAta-centric Peer-to-peer filE Sharing (DAPES), a data sharing protocol for scenarios with intermittent connectivity and user mobility. DAPES provides a set of semantically meaningful hierarchical naming abstractions that facilitate the exchange of file collections via local connectivity. This enables peers to "make the most" out of the limited connection time with other peers by maximizing the utility of individual transmissions to provide data missing by most connected peers. DAPES runs on top of Named-Data Networking (NDN) and extends NDN's data-centric network layer abstractions to achieve communication over multiple wireless hops through an adaptive hop-by-hop forwarding/suppression mechanism. We have evaluated DAPES through real-world experiments in an outdoor campus setting and extensive simulations. Our results demonstrate that DAPES achieves 50-71% lower overheads and 15-33% lower file sharing delays compared to file sharing solutions that rely on IP-based mobile ad-hoc routing.

cs.NI↗

arXiv2019

Experimenting with a Simulation Framework for Peer-to-Peer File Sharing in Named Data Networking

Peer-to-peer file sharing envisions a data-centric dissemination model, where files consisting of multiple data pieces can be shared from any peer that can offer the data or from multiple peers simultaneously. This aim, implemented at the application layer of the network architecture, matches with the objective of Named Data Networking (NDN), a proposed Internet architecture that features a data-centric communication model at the network layer. To study the impact of a data-centric network architecture on peer-to-peer file sharing, we proposed nTorrent, a peer-to-peer file sharing application on top of NDN. Since the initial nTorrent proposal in 2017, we have implemented its design in ndnSIM, the de facto NDN simulator. In this paper, we present the design of our nTorrent simulation framework, discussing various design decisions and trade-offs. We also describe our experimentation and validation process to ensure that our framework possesses the fundamental properties of nTorrent.

cs.NI↗

arXiv2018

ISA-Based Trusted Network Functions And Server Applications In The Untrusted Cloud

Nowadays, enterprises widely deploy Network Functions (NFs) and server applications in the cloud. However, processing of sensitive data and trusted execution cannot be securely deployed in the untrusted cloud. Cloud providers themselves could accidentally leak private information (e.g., due to misconfigurations) or rogue users could exploit vulnerabilities of the providers' systems to compromise execution integrity, posing a threat to the confidentiality of internal enterprise and customer data. In this paper, we identify (i) a number of NF and server application use-cases that trusted execution can be applied to, (ii) the assets and impact of compromising the private data and execution integrity of each use-case, and (iii) we leverage Intel's Software Guard Extensions (SGX) architecture to design Trusted Execution Environments (TEEs) for cloud-based NFs and server applications. We combine SGX with the Data Plane Development KIT (DPDK) to prototype and evaluate our TEEs for a number of application scenarios (Layer 2 frame and Layer 3 packet processing for plain and encrypted traffic, traffic load-balancing and back-end server processing). Our results indicate that NFs involving plain traffic can achieve almost native performance (e.g., ~22 Million Packets Per Second for Layer 3 forwarding for 64-byte frames), while NFs involving encrypted traffic and server processing can still achieve competitive performance (e.g., ~12 Million Packets Per Second for server processing for 64-byte frames).

cs.NI↗

arXiv2018

Experimentation With Fuzzy Interest Forwarding in Named Data Networking

In the current Named Data Networking implementation, forwarding a data request requires finding an exact match between the prefix of the name carried in the request and a forwarding table entry. However, consumers may not always know the exact naming, or an exact prefix, of their desired data. The current approach to this problem-establishing naming conventions and performing name lookup-can be infeasible in highly ad hoc, heterogeneous, and dynamic environments: the same data can be named using different terms or even languages, naming conventions may be minimal if they exist at all, and name lookups can be costly. In this paper, we present a fuzzy Interest forwarding approach that exploits semantic similarities between the names carried in Interest packets and the names of potentially matching data in CS and entries in FIB. We describe the fuzzy Interest forwarding approach, outline the semantic understanding function that determines the name matching, and present our simulation study along with extended evaluation results.

cs.NI↗