1,436 research outputs found
MiniCPS: A toolkit for security research on CPS Networks
In recent years, tremendous effort has been spent to modernizing
communication infrastructure in Cyber-Physical Systems (CPS) such as Industrial
Control Systems (ICS) and related Supervisory Control and Data Acquisition
(SCADA) systems. While a great amount of research has been conducted on network
security of office and home networks, recently the security of CPS and related
systems has gained a lot of attention. Unfortunately, real-world CPS are often
not open to security researchers, and as a result very few reference systems
and topologies are available. In this work, we present MiniCPS, a CPS
simulation toolbox intended to alleviate this problem. The goal of MiniCPS is
to create an extensible, reproducible research environment targeted to
communications and physical-layer interactions in CPS. MiniCPS builds on
Mininet to provide lightweight real-time network emulation, and extends Mininet
with tools to simulate typical CPS components such as programmable logic
controllers, which use industrial protocols (Ethernet/IP, Modbus/TCP). In
addition, MiniCPS defines a simple API to enable physical-layer interaction
simulation. In this work, we demonstrate applications of MiniCPS in two example
scenarios, and show how MiniCPS can be used to develop attacks and defenses
that are directly applicable to real systems.Comment: 8 pages, 6 figures, 1 code listin
JamLab: Augmenting Sensornet Testbeds with Realistic and Controlled Interference Generation
Radio interference drastically affects the performance of sensor-net communications, leading to packet loss and reduced energy-efficiency. As an increasing number of wireless devices operates on the same ISM frequencies, there is a strong need for understanding and debugging the performance of existing sensornet protocols under interference. Doing so requires a low-cost flexible testbed infrastructure that allows the repeatable generation of a wide range of interference patterns. Unfortunately, to date, existing sensornet testbeds lack such capabilities, and do not permit to study easily the coexistence problems between devices sharing the same frequencies. This paper addresses the current lack of such an infrastructure by using off-the-shelf sensor motes to record and playback interference patterns as well as to generate customizable and repeat-able interference in real-time. We propose and develop JamLab: a low-cost infrastructure to augment existing sensornet testbeds with accurate interference generation while limiting the overhead to a simple upload of the appropriate software. We explain how we tackle the hardware limitations and get an accurate measurement and regeneration of interference, and we experimentally evaluate the accuracy of JamLab with respect to time, space, and intensity. We further use JamLab to characterize the impact of interference on sensornet MAC protocols
When should I use network emulation ?
The design and development of a complex system requires an adequate methodology and efficient instrumental support in order to early detect and correct anomalies in the functional and non-functional properties of the tested protocols. Among the various tools used to provide experimental support for such developments, network emulation relies on real-time production of impairments on real traffic according to a communication model, either realistically or not. This paper aims at simply presenting to newcomers in network emulation (students, engineers, ...) basic principles and practices illustrated with a few commonly used tools. The motivation behind is to fill a gap in terms of introductory and pragmatic papers in this domain. The study particularly considers centralized approaches, allowing cheap and easy implementation in the context of research labs or industrial developments. In addition, an architectural model for emulation systems is proposed, defining three complementary levels, namely hardware, impairment and model levels. With the help of this architectural framework, various existing tools are situated and described. Various approaches for modeling the emulation actions are studied, such as impairment-based scenarios and virtual architectures, real-time discrete simulation and trace-based systems. Those modeling approaches are described and compared in terms of services and we study their ability to respond to various designer needs to assess when emulation is needed
When Should I Use Network Emulation?
The design and development of a complex system requires an adequate
methodology and efficient instrumental support in order to early detect and
correct anomalies in the functional and non-functional properties of the tested
protocols. Among the various tools used to provide experimental support for
such developments, network emulation relies on real-time production of
impairments on real traffic according to a communication model, either
realistically or not.
This paper aims at simply presenting to newcomers in network emulation
(students, engineers, ...) basic principles and practices illustrated with a
few commonly used tools. The motivation behind is to fill a gap in terms of
introductory and pragmatic papers in this domain.
The study particularly considers centralized approaches, allowing cheap and
easy implementation in the context of research labs or industrial developments.
In addition, an architectural model for emulation systems is proposed, defining
three complementary levels, namely hardware, impairment and model levels. With
the help of this architectural framework, various existing tools are situated
and described. Various approaches for modeling the emulation actions are
studied, such as impairment-based scenarios and virtual architectures,
real-time discrete simulation and trace-based systems. Those modeling
approaches are described and compared in terms of services and we study their
ability to respond to various designer needs to assess when emulation is
needed
Lightweight testbed for machine learning evaluation in 5G networks
The adoption of Software Define Networking, Network Function Virtualization and Machine Learning will play a key role in the control and management of fifth-generation (5G) networks in order to meet the specific requirements of vertical industries and the stringent requirements of 5G. Machine learning could be applied in 5G networks to deal with issues such as traffic prediction, routing optimization and resource management. To evaluate the adoption of machine learning in 5G networks, an adequate testing environment is required. In this paper, we introduce a lightweight testbed, which utilizes the benefits of container lightweight virtualization technology to create machine learning network functions over the well-known Mininet network emulator. As a use case of this testbed, we present an experimental real-time bandwidth prediction using the Long Short Term Memory recurrent neural network.Peer ReviewedPostprint (published version
The Glasgow raspberry pi cloud: a scale model for cloud computing infrastructures
Data Centers (DC) used to support Cloud services
often consist of tens of thousands of networked machines under a single roof. The significant capital outlay required to replicate such infrastructures constitutes a major obstacle to practical implementation and evaluation of research in this domain. Currently, most research into Cloud computing relies on either limited software simulation, or the use of a testbed environments
with a handful of machines. The recent introduction of the
Raspberry Pi, a low-cost, low-power single-board computer, has made the construction of a miniature Cloud DCs more affordable.
In this paper, we present the Glasgow Raspberry Pi Cloud
(PiCloud), a scale model of a DC composed of clusters of
Raspberry Pi devices. The PiCloud emulates every layer of a
Cloud stack, ranging from resource virtualisation to network
behaviour, providing a full-featured Cloud Computing research and educational environment
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