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VMware Inc virtual machine (vm) log data
The diagram illustrates the generation of logs. Each <t>virtual</t> <t>machine</t> generates <t>log</t> files in chronological order over time. The intervals between log file generations are often inconsistent, resulting in some virtual machines generating a large number of log files within a given time frame ‘K’, while others generate fewer log files. The number of log lines in each log file also tends to vary.
Virtual Machine (Vm) Log Data, supplied by VMware Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/virtual machine (vm) log data/product/VMware Inc
Average 90 stars, based on 1 article reviews
virtual machine (vm) log data - by Bioz Stars, 2026-05
90/100 stars

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1) Product Images from "Anomaly detection in virtual machine logs against irrelevant attribute interference"

Article Title: Anomaly detection in virtual machine logs against irrelevant attribute interference

Journal: PLOS ONE

doi: 10.1371/journal.pone.0315897

The diagram illustrates the generation of logs. Each virtual machine generates log files in chronological order over time. The intervals between log file generations are often inconsistent, resulting in some virtual machines generating a large number of log files within a given time frame ‘K’, while others generate fewer log files. The number of log lines in each log file also tends to vary.
Figure Legend Snippet: The diagram illustrates the generation of logs. Each virtual machine generates log files in chronological order over time. The intervals between log file generations are often inconsistent, resulting in some virtual machines generating a large number of log files within a given time frame ‘K’, while others generate fewer log files. The number of log lines in each log file also tends to vary.

Techniques Used:

The latest log file on each virtual machine at time T is the object to be detected Discriminator is a detection system. Normal and Anormal represent the two categories into which the log files are divided. In one case , T 3 is a noisy normal log file alerted as an anomaly. In another case (Fig 5), T 3 is a noisy normal log file considered as normal.
Figure Legend Snippet: The latest log file on each virtual machine at time T is the object to be detected Discriminator is a detection system. Normal and Anormal represent the two categories into which the log files are divided. In one case , T 3 is a noisy normal log file alerted as an anomaly. In another case (Fig 5), T 3 is a noisy normal log file considered as normal.

Techniques Used:



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The diagram illustrates the generation of logs. Each virtual machine generates log files in chronological order over time. The intervals between log file generations are often inconsistent, resulting in some virtual machines generating a large number of log files within a given time frame ‘K’, while others generate fewer log files. The number of log lines in each log file also tends to vary.

Journal: PLOS ONE

Article Title: Anomaly detection in virtual machine logs against irrelevant attribute interference

doi: 10.1371/journal.pone.0315897

Figure Lengend Snippet: The diagram illustrates the generation of logs. Each virtual machine generates log files in chronological order over time. The intervals between log file generations are often inconsistent, resulting in some virtual machines generating a large number of log files within a given time frame ‘K’, while others generate fewer log files. The number of log lines in each log file also tends to vary.

Article Snippet: In this study, we conducted experiments using virtual machine (VM) log data obtained from 20 virtual machine instances managed by VMware.

Techniques:

The latest log file on each virtual machine at time T is the object to be detected Discriminator is a detection system. Normal and Anormal represent the two categories into which the log files are divided. In one case , T 3 is a noisy normal log file alerted as an anomaly. In another case (Fig 5), T 3 is a noisy normal log file considered as normal.

Journal: PLOS ONE

Article Title: Anomaly detection in virtual machine logs against irrelevant attribute interference

doi: 10.1371/journal.pone.0315897

Figure Lengend Snippet: The latest log file on each virtual machine at time T is the object to be detected Discriminator is a detection system. Normal and Anormal represent the two categories into which the log files are divided. In one case , T 3 is a noisy normal log file alerted as an anomaly. In another case (Fig 5), T 3 is a noisy normal log file considered as normal.

Article Snippet: In this study, we conducted experiments using virtual machine (VM) log data obtained from 20 virtual machine instances managed by VMware.

Techniques: