Computer networks for remote laboratories in physics and engineering

This paper addresses a relatively new approach to scientific research, telescience, which is the conduct of scientific operations in locations remote from the site of central experimental activity. A testbed based on the concepts of telescience is being developed to ultimately enable scientific researchers on earth to conduct experiments onboard the Space Station. This system along with background materials are discussed

Internet Protocol (IP) Multicast: Final Report

Report presents the results of the Institute for Simulation and Training\u27s implementation and integration of new protocols into the Testbed for Research in Distributed Interactive Simulation (TRDIS) testbed, especially the Internet Protocol (IP) Multicast (IPmc) and Internet Group Management Protocol network protocols, into a simulation application

Framework for Industrial Control System Honeypot Network Traffic Generation

Defending critical infrastructure assets is an important but extremely difficult and expensive task. Historically, decoys have been used very effectively to distract attackers and in some cases convince an attacker to reveal their attack strategy. Several researchers have proposed the use of honeypots to protect programmable logic controllers, specifically those used to support critical infrastructure. However, most of these honeypot designs are static systems that wait for a would-be attacker. To be effective, honeypot decoys need to be as realistic as possible. This paper introduces a proof-of-concept honeypot network traffic generator that mimics genuine control systems. Experiments are conducted using a Siemens APOGEE building automation system for single and dual subnet instantiations. Results indicate that the proposed traffic generator is capable of honeypot integration, traffic matching and routing within the decoy building automation network

Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale

Neural Architecture Search (NAS) has demonstrated its efficacy in computer vision and potential for ranking systems. However, prior work focused on academic problems, which are evaluated at small scale under well-controlled fixed baselines. In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle. In this paper, we present Rankitect, a NAS software framework for ranking systems at Meta. Rankitect seeks to build brand new architectures by composing low level building blocks from scratch. Rankitect implements and improves state-of-the-art (SOTA) NAS methods for comprehensive and fair comparison under the same search space, including sampling-based NAS, one-shot NAS, and Differentiable NAS (DNAS). We evaluate Rankitect by comparing to multiple production ranking models at Meta. We find that Rankitect can discover new models from scratch achieving competitive tradeoff between Normalized Entropy loss and FLOPs. When utilizing search space designed by engineers, Rankitect can generate better models than engineers, achieving positive offline evaluation and online A/B test at Meta scale.Comment: Wei Wen and Kuang-Hung Liu contribute equall

Volunteer Computing Simulation Using Repast And Mason

Volunteer environments usually consist of a large number of computing nodes,with highly dynamic characteristics, therefore reliable models for a planning ofthe whole computing are highly desired. An easy to implement approach to mo-delling and simulation of such environments may employ agent-based universalsimulation frameworks, such as RePast or MASON. In the course of the paperthe above-mentioned simulation frameworks are adapted to support simulationof volunteer computing. After giving implementation details, selected resultsconcerning computing time and speedup are given and are compared with theones obtained from an actual volunteer environment

Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems

Designing an e-commerce recommender system that serves hundreds of millions of active users is a daunting challenge. From a human vision perspective, there're two key factors that affect users' behaviors: items' attractiveness and their matching degree with users' interests. This paper proposes Telepath, a vision-based bionic recommender system model, which understands users from such perspective. Telepath is a combination of a convolutional neural network (CNN), a recurrent neural network (RNN) and deep neural networks (DNNs). Its CNN subnetwork simulates the human vision system to extract key visual signals of items' attractiveness and generate corresponding activations. Its RNN and DNN subnetworks simulate cerebral cortex to understand users' interest based on the activations generated from browsed items. In practice, the Telepath model has been launched to JD's recommender system and advertising system. For one of the major item recommendation blocks on the JD app, click-through rate (CTR), gross merchandise value (GMV) and orders have increased 1.59%, 8.16% and 8.71% respectively. For several major ads publishers of JD demand-side platform, CTR, GMV and return on investment have increased 6.58%, 61.72% and 65.57% respectively by the first launch, and further increased 2.95%, 41.75% and 41.37% respectively by the second launch.Comment: 8 pages, 11 figures, 1 tabl

Prediction assisted fast handovers for seamless IP mobility

Word processed copy.Includes bibliographical references (leaves 94-98).This research investigates the techniques used to improve the standard Mobile IP handover process and provide proactivity in network mobility management. Numerous fast handover proposals in the literature have recently adopted a cross-layer approach to enhance movement detection functionality and make terminal mobility more seamless. Such fast handover protocols are dependent on an anticipated link-layer trigger or pre-trigger to perform pre-handover service establishment operations. This research identifies the practical difficulties involved in implementing this type of trigger and proposes an alternative solution that integrates the concept of mobility prediction into a reactive fast handover scheme

ML-driven provisioning and management of vertical services in automated cellular networks

One of the main tasks of new-generation cellular networks is the support of the wide range of virtual services that may be requested by vertical industries, while fulfilling their diverse performance requirements. Such task is made even more challenging by the time-varying service and traffic demands, and the need for a fully-automated network orchestration and management to reduce the service operational costs incurred by the network provider. In this paper, we address these issues by proposing a softwarized 5G network architecture that realizes the concept of ML-as-a-Service (MLaaS) in a flexible and efficient manner. The designed MLaaS platform can provide the different entities of a MANO architecture with already-trained ML models, ready to be used for decision making. In particular, we show how our MLaaS platform enables the development of two ML-driven algorithms for, respectively, network slice subnet sharing and run-time service scaling. The proposed approach and solutions are implemented and validated through an experimental testbed in the case of three different services in the automotive domain, while their performance is assessed through simulation in a large-scale, real-world scenario. In-testbed validation shows that the use of the MLaaS platform within the designed architecture and the ML-driven decision-making processes entail a very limited time overhead, while simulation results highlight remarkable savings in operational costs, e.g., up to 40% reduction in CPU consumption and up to 30% reduction in the OPEX.This work was supported by the EU Commission through the 5GROWTH project (Grant Agreement No. 856709), Spanish MINECO 5G-REFINE project (TEC2017-88373-R), and Generalitat de Catalunya 2017 SGR 1195.Publicad

Detecting Denial of Service Attacks in Internet of Things Using Software-Defined Networking and Ensemble Learning

The Internet of Things (IoT) is a novel approach to automate connections between smart devices without involving humans. The utilization of this structure is growing, and its application range is continually expanding. We confront additional issues as the usage of these networks grows, such as the presence of attackers and combating their attacks. These networks' performance may be improved, and their development can be accelerated, with new solutions to these difficulties. A new method for improving IoT security is proposed in this research, which is based on software-based network and collaborative learning. The suggested solution divides the network domain into numerous subdomains, each with its own controller for exchanging security rules with other subdomains. All of a subnet's node traffic is routed through the subnet's control node in this topology. As a result, each controller node employs an integrated learning model to continually evaluate network traffic data and detect assaults. This learning model incorporates an artificial neural network, a decision tree, and a New Biz model that uses statistical information gathered from each data stream to identify the likely existence of assaults. NSL-KDD database data was utilised to assess the proposed method's performance, and its accuracy in identifying denial of service attacks was compared to earlier approaches