898 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
Don't Repeat Yourself: Seamless Execution and Analysis of Extensive Network Experiments
This paper presents MACI, the first bespoke framework for the management, the
scalable execution, and the interactive analysis of a large number of network
experiments. Driven by the desire to avoid repetitive implementation of just a
few scripts for the execution and analysis of experiments, MACI emerged as a
generic framework for network experiments that significantly increases
efficiency and ensures reproducibility. To this end, MACI incorporates and
integrates established simulators and analysis tools to foster rapid but
systematic network experiments.
We found MACI indispensable in all phases of the research and development
process of various communication systems, such as i) an extensive DASH video
streaming study, ii) the systematic development and improvement of Multipath
TCP schedulers, and iii) research on a distributed topology graph pattern
matching algorithm. With this work, we make MACI publicly available to the
research community to advance efficient and reproducible network experiments
CiTAR - Preserving Software-based Research
In contrast to books or published articles, pure digital output of research projects is more fragile and, thus, more difficult to preserve and more difficult to be made available and to be reused by a wider research community. Not only does a fast-growing format diversity in research data sets require additional software preservation but also today’s computer assisted research disciplines increasingly devote significant resources into creating new digital resources and software-based methods.
In order to adapt FAIR data principles, especially to ensure re-usability of a wide variety of research outputs, novel ways for preservation of software and additional digital resources are required as well as their integration into existing research data management strategies.
This article addresses preservation challenges and preservation options of containers and virtual machines to encapsulate software-based research methods as portable and preservable software-based research resources, provides a preservation plan as well as an implementation.
 
Gotham Testbed: a Reproducible IoT Testbed for Security Experiments and Dataset Generation
The scarcity of available Internet of Things (IoT) datasets remains a
limiting factor in developing machine learning based security systems. Static
datasets get outdated due to evolving IoT threat landscape. Meanwhile, the
testbeds used to generate them are rarely published. This paper presents the
Gotham testbed, a reproducible and flexible network security testbed,
implemented as a middleware over the GNS3 emulator, that is extendable to
accommodate new emulated devices, services or attackers. The testbed is used to
build an IoT scenario composed of 100 emulated devices communicating via MQTT,
CoAP and RTSP protocols in a topology composed of 30 switches and 10 routers.
The scenario presents three threat actors, including the entire Mirai botnet
lifecycle and additional red-teaming tools performing DoS, scanning and various
attacks targeting the MQTT and CoAP protocols. The generated network traffic
and application logs can be used to capture datasets containing legitimate and
attacking traces. We hope that researchers can leverage the testbed and adapt
it to include other types of devices and state-of-the-art attacks to generate
new datasets that reflect the current threat landscape and IoT protocols. The
source code to reproduce the scenario is publicly accessible
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Faithful reproduction of network experiments
The proliferation of cloud computing has compelled the research community to rethink fundamental aspects of network systems and architectures. However, the tools commonly used to evaluate new ideas have not kept abreast of the latest developments. Common simulation and emulation frameworks fail to provide scalability, fidelity, reproducibility and execute unmodified code, all at the same time.
We present SELENA, a Xen-based network emulation framework that offers fully reproducible experiments via its automation interface and supports the use of unmodified guest operating systems. This allows out-of-the-box compatibility with common applications and OS components, such as network stacks and filesystems. In order to faithfully emulate faster and larger networks, SELENA adopts the technique of time-dilation and transparently slows down the passage of time for guest operating systems. This technique effectively virtualizes the availability of host’s hardware resources and allows the replication of scenarios with increased I/O and computational demands. Users can directly control the tradeoff between fidelity and running-times via intuitive tuning knobs. We evaluate the ability of SELENA to faithfully replicate the behaviour of real systems and compare it against existing popular experimentation platforms. Our results suggest that SELENA can accurately model networks with aggregate link speeds of 44 Gbps or more, while improving by four times the execution time in comparison to ns3 and exhibits near-linear scaling properties.This is the author accepted manuscript. The final version is available from ACM via http://dx.doi.org/10.1145/2658260.265827
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