Experience with SPLUNK for archiving and visualisation of operational data in ATLAS TDAQ system

Kazarov, A.; Avolio, G.; Chitan, A.; Mineev, M.

doi:10.1088/1742-6596/1085/3/032052

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…The URL address of the destination of the communication of our users can also help us to identify possible threats to our network. 14 For our scenario of behaviour analysis of users, we will use the following subset of presented information from the record of the web access log file:…”

Section: Log File Datamentioning

confidence: 99%

Network User Behaviour Analysis by Machine Learning Methods

Michal¹

2021

ISIJ

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Cyber security is one of the prominent global challenges due to the significant increase in the number of cyberattacks over the last few decades. The amount of transferred data is growing, and a quick reaction to cyber incidents is needed. The paper is a contribution to this effort. There is a possibility to save time and resources by concentrating only on a subgroup of potential threats caused by a specific group of users. The main source of information about a selected group of users is the web access log file, where all the necessary data is stored. The contribution also presents the concept of preprocessing data from the log files to a form useful for clustering. In the next step, a densitybased spatial clustering algorithm is applied to create the clusters. Clustering algorithms have been applied to many fields (marketing, business, etc.), but not for the purposes of cyber defence. The created clusters were analysed according to our definition of risky behaviour. After analysis of the clustering results, it was possible to select a potentially dangerous group of users in the specific cluster. The presented method has potential use in different areas of cyber defence and other applications where intelligent classification is required.

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Section: Log File Datamentioning

confidence: 99%

Network User Behaviour Analysis by Machine Learning Methods

Michal¹

2021

ISIJ

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“…Splunk, a commercial solution by Splunk Inc [7], was another candidate for PBEAST implementation [8]. It allows non-relational structured time-series textual information to be inserted into a database, with search, analysis and Web-based reporting facilities.…”

Section: Splunk Prototypementioning

confidence: 99%

ATLAS Operational Monitoring Data Archival and Visualization

et al. 2020

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The Information Service (IS) is an integral part of the Trigger and Data Acquisition (TDAQ) system of the ATLAS experiment at the Large Hadron Collider (LHC) at CERN. The IS allows online publication of operational monitoring data, and it is used by all sub-systems and sub-detectors of the experiment to constantly monitor their hardware and software components including more than 25000 applications running on more than 3000 computers. The Persistent Back-End for the ATLAS Information System (PBEAST) service stores all raw operational monitoring data for the lifetime of the experiment and provides programming and graphical interfaces to access them including Grafana dashboards and notebooks based on the CERN SWAN platform. During the ATLAS data taking sessions (for the full LHC Run 2 period) PBEAST acquired data at an average information update rate of 200 kHz and stored 20 TB of highly compacted and compressed data per year. This paper reports how over six years PBEAST became an essential piece of the experiment operations including details of the challenging requirements, the failures and successes of the various attempted implementations, the new types of monitoring data and the results of the time-series database technology evaluations for the improvements towards LHC Run 3.

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Behaviour Analysis of Web Users by Mean Shift Clustering

Turčaník¹

2021

2021 International Conference on Military Technologies (ICMT)

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Experience with SPLUNK for archiving and visualisation of operational data in ATLAS TDAQ system

Cited by 6 publications

References 3 publications

Network User Behaviour Analysis by Machine Learning Methods

Network User Behaviour Analysis by Machine Learning Methods

ATLAS Operational Monitoring Data Archival and Visualization

Behaviour Analysis of Web Users by Mean Shift Clustering

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