24,305 research outputs found
SSHCure: a flow-based SSH intrusion detection system
SSH attacks are a main area of concern for network managers, due to the danger associated with a successful compromise. Detecting these attacks, and possibly compromised victims, is therefore a crucial activity. Most existing network intrusion detection systems designed for this purpose rely on the inspection of individual packets and, hence, do not scale to today's high-speed networks. To overcome this issue, this paper proposes SSHCure, a flow-based intrusion detection system for SSH attacks. It employs an efficient algorithm for the real-time detection of ongoing attacks and allows identification of compromised attack targets. A prototype implementation of the algorithm, including a graphical user interface, is implemented as a plugin for the popular NfSen monitoring tool. Finally, the detection performance of the system is validated with empirical traffic data
Visual Execution and Data Visualisation in Natural Language Processing
We describe GGI, a visual system that allows the user to execute an automatically generated data flow graph containing code modules that perform natural language processing tasks. These code modules operate on text documents. GGI has a suite of text visualisation tools that allows the user useful views of the annotation data that is produced by the modules in the executable graph. GGI forms part of the GATE natural language engineering system
Enabling adaptive scientific workflows via trigger detection
Next generation architectures necessitate a shift away from traditional
workflows in which the simulation state is saved at prescribed frequencies for
post-processing analysis. While the need to shift to in~situ workflows has been
acknowledged for some time, much of the current research is focused on static
workflows, where the analysis that would have been done as a post-process is
performed concurrently with the simulation at user-prescribed frequencies.
Recently, research efforts are striving to enable adaptive workflows, in which
the frequency, composition, and execution of computational and data
manipulation steps dynamically depend on the state of the simulation. Adapting
the workflow to the state of simulation in such a data-driven fashion puts
extremely strict efficiency requirements on the analysis capabilities that are
used to identify the transitions in the workflow. In this paper we build upon
earlier work on trigger detection using sublinear techniques to drive adaptive
workflows. Here we propose a methodology to detect the time when sudden heat
release occurs in simulations of turbulent combustion. Our proposed method
provides an alternative metric that can be used along with our former metric to
increase the robustness of trigger detection. We show the effectiveness of our
metric empirically for predicting heat release for two use cases.Comment: arXiv admin note: substantial text overlap with arXiv:1506.0825
Cavitation Inception - A Selective Review
This paper reviews recent developments in selected cavitation research areas which have been active mainly within the past two years. The new understanding resulting from this work is summarized. Research topics discussed are cavitation inception on smooth surfaces, on vortex cavitation and scaling, on the measurement of cavitation nuclei, and on the effects of polymer additives. Because of the selective nature of the review, a fairly comprehensive listing of recent contributions to the literature on these and related aspects of cavitation research is an essential part of the exposition
Dynamic 3D Network Data Visualization
Monitoring network traffic has always been an arduous and tedious task because of the complexity and sheer volume of network data that is being consistently generated. In addition, network growth and new technologies are rapidly increasing these levels of complexity and volume. An effective technique in understanding and managing a large dataset, such as network traffic, is data visualization. There are several tools that attempt to turn network traffic into visual stimuli. Many of these do so in 2D space and those that are 3D lack the ability to display network patterns effectively. Existing 3D network visualization tools lack user interaction, dynamic generation, and intuitiveness. This project proposes a user-friendly 3D network visualization application that creates both dynamic and interactive visuals. This application was built using the Bablyon.js graphics framework and uses anonymized data collected from a campus network
Intelligent Management and Efficient Operation of Big Data
This chapter details how Big Data can be used and implemented in networking
and computing infrastructures. Specifically, it addresses three main aspects:
the timely extraction of relevant knowledge from heterogeneous, and very often
unstructured large data sources, the enhancement on the performance of
processing and networking (cloud) infrastructures that are the most important
foundational pillars of Big Data applications or services, and novel ways to
efficiently manage network infrastructures with high-level composed policies
for supporting the transmission of large amounts of data with distinct
requisites (video vs. non-video). A case study involving an intelligent
management solution to route data traffic with diverse requirements in a wide
area Internet Exchange Point is presented, discussed in the context of Big
Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big
Data and Web Intelligence, IGI Global, 201
A new method for interacting with multi-window applications on large, high resolution displays
Physically large display walls can now be constructed using off-the-shelf computer hardware. The high resolution
of these displays (e.g., 50 million pixels) means that a large quantity of data can be presented to users, so the
displays are well suited to visualization applications. However, current methods of interacting with display walls
are somewhat time consuming. We have analyzed how users solve real visualization problems using three desktop
applications (XmdvTool, Iris Explorer and Arc View), and used a new taxonomy to classify usersâ actions and
illustrate the deficiencies of current display wall interaction methods. Following this we designed a novel methodfor interacting with display walls, which aims to let users interact as quickly as when a visualization application is used on a desktop system. Informal feedback gathered from our working prototype shows that interaction is both fast and fluid
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