scholarly journals Collection and harmonization of system logs and prototypal Analytics services with the Elastic (ELK) suite at the INFN-CNAF computing centre

2019 ◽  
Author(s):  
Tommaso Diotalevi ◽  
Daniele Bonacorsi ◽  
Antonio Falabella ◽  
Luca Giommi ◽  
Barbara Martelli ◽  
...  
Keyword(s):  
System Logs

scholarly journals ConAnomaly: Content-Based Anomaly Detection for System Logs

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6125
Author(s):  
Dan Lv ◽  
Nurbol Luktarhan ◽  
Yiyong Chen
Keyword(s):  
Anomaly Detection ◽  
Semantic Information ◽  
Short Term Memory ◽  
Weighted Average ◽  
Detection Methods ◽  
Detection Model ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
System Logs ◽  
System Maintenance

Enterprise systems typically produce a large number of logs to record runtime states and important events. Log anomaly detection is efficient for business management and system maintenance. Most existing log-based anomaly detection methods use log parser to get log event indexes or event templates and then utilize machine learning methods to detect anomalies. However, these methods cannot handle unknown log types and do not take advantage of the log semantic information. In this article, we propose ConAnomaly, a log-based anomaly detection model composed of a log sequence encoder (log2vec) and multi-layer Long Short Term Memory Network (LSTM). We designed log2vec based on the Word2vec model, which first vectorized the words in the log content, then deleted the invalid words through part of speech tagging, and finally obtained the sequence vector by the weighted average method. In this way, ConAnomaly not only captures semantic information in the log but also leverages log sequential relationships. We evaluate our proposed approach on two log datasets. Our experimental results show that ConAnomaly has good stability and can deal with unseen log types to a certain extent, and it provides better performance than most log-based anomaly detection methods.

scholarly journals Using Data Mining for Improving Education of University Students – A Survey

2018 ◽  
Vol 6 (9) ◽  
pp. 78-83 ◽  
Author(s):  
K. P. S. D. Kumarapathirana
Keyword(s):  
Data Mining ◽  
Academic Success ◽  
Student Retention ◽  
Student Performance ◽  
Poor Performance ◽  
Academic Behavior ◽  
System Logs ◽  
Using Data ◽  
Visualization Techniques ◽  
The University

Data mining combines machine learning, statistical and visualization techniques to discover and extract knowledge. Student retention is an indicator of academic performance and enrolment management of the university. Poor student retention could reflect badly on the university. Universities are facing the immense and quick growth of the volume of educational data stored in different types of databases and system logs. Moreover, the academic success of students is another major issue for the management in all professional institutes. So the early prediction to improve the student performance through counseling and extra coaching will help the management to take timely action for decrease the percentage of poor performance by the students. Data mining can be used to find relationships and patterns that exist but are hidden among the vast amount of educational data. This survey conducts a literature survey to identify data mining technologies to monitor student, analyze student academic behavior and provide a basis for efficient intervention strategies. The results can be used to develop a decision support system and help the authorities to timely actions on weak students.

scholarly journals VOLARE: Visual analysis of disease-associated microbiome-immune system interplay

2018 ◽  
Author(s):  
Janet C. Siebert ◽  
Charles Preston Neff ◽  
Jennifer M. Schneider ◽  
EmiLie H. Regner ◽  
Neha Ohri ◽  
...  
Keyword(s):  
Immune System ◽  
Web Application ◽  
Visual Analysis ◽  
System Development ◽  
Patient Specific ◽  
Domain Experts ◽  
Immune System Development ◽  
Interactive Environment ◽  
System Logs ◽  
Linear Regressions

AbstractBackgroundRelationships between specific microbes and proper immune system development, composition, and function have been reported in a number of studies. However, researchers have discovered only a fraction of the likely relationships. High-dimensional “omic” methodologies such as 16S ribosomal RNA (rRNA) sequencing and Time-of-flight mass cytometry (CyTOF) immunophenotyping generate data that support generation of hypotheses, with the potential to identify additional relationships at a level of granularity ripe for further experimentation. Pairwise linear regressions between microbial and host immune features is one approach for quantifying relationships between “omes”, and the differences in these relationships across study cohorts or arms. This approach yields a top table of candidate results. However, the top table alone lacks the detail that domain experts need to vet candidate results for follow-up experiments.ResultsTo support this vetting, we developed VOLARE (Visualization Of LineAr Regression Elements), a web application that integrates a searchable top table, small in-line graphs illustrating the fitted models, a network summarizing the top table, and on-demand detailed regression plots showing full sample-level detail. We applied VOLARE to three case studies—microbiome:cytokine data from fecal samples in HIV, microbiome:cytokine data in inflammatory bowel disease and spondyloarthritis, and microbiome:immune cell data from gut biopsies in HIV. We present both patient-specific phenomena and relationships that differ by disease state. We also analyzed interaction data from system logs to characterize usage scenarios. This log analysis revealed that, in using VOLARE, domain experts frequently generated detailed regression plots, suggesting that this detail aids the vetting of results.ConclusionsSystematically integrating microbe:immune cell readouts through pairwise linear regressions and presenting the top table in an interactive environment supports the vetting of results for scientific relevance. VOLARE allows domain experts to control the analysis of their results, screening dozens of candidate relationships with ease. This interactive environment transcends the limitations of a static top table.

Continuous Authentication in Computers

2013 ◽  
pp. 268-293
Author(s):  
Harini Jagadeesan ◽  
Michael S. Hsiao
Keyword(s):  
User Behavior ◽  
Behavioral Pattern ◽  
Security Issue ◽  
Age Identity ◽  
Internet Age ◽  
Continuous Authentication ◽  
System Logs ◽  
Computer Based ◽  
Authentication System ◽  
High Degree

In the Internet age, identity theft is a major security issue because contemporary authentication systems lack adequate mechanisms to detect and prevent masquerading. This chapter discusses the current authentication systems and identifies their limitations in combating masquerading attacks. Analysis of existing authentication systems reveals the factors to be considered and the steps necessary in building a good continuous authentication system. As an example, we present a continual, non-intrusive, fast and easily deployable user re-authentication system based on behavioral biometrics. It employs a novel heuristic based on keyboard and mouse attributes to decipher the behavioral pattern of each individual user on the system. In the re-authentication process, the current behavior of user is compared with stored “expected” behavior. If user behavior deviates from expected behavior beyond an allowed threshold, system logs the user out of the current session, thereby preventing imposters from misusing the system. Experimental results show that the proposed methodology improves the accuracy of application-based and application independent systems to 96.4% and 82.2% respectively. At the end of this chapter, the reader is expected to understand the dimensions involved in creating a computer based continuous authentication system and is able to frame a robust continual re-authentication system with a high degree of accuracy.

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