Info: Zenodo’s user support line is staffed on regular business days between Dec 23 and Jan 5. Response times may be slightly longer than normal.

Published February 19, 2022 | Version v1
Dataset Open

Dataset for Anomaly Detection in a Production Wireless Mesh Community Network

  • 1. Universitat Politècnica de Catalunya

Description

CSV dataset generated gathering data from a production wireless mesh community network. Data is gathered every 5 minutes during the interval 2021-04-13 00:00:00 to 2021-04-16 00:00:00. During the interval 2021-04-14 02:00:00 2021-04-14 17:50:00 (both included) there is the failure of a gateway in the mesh (nodeid 24). 

Live mesh network monitoring link: http://dsg.ac.upc.edu/qmpsu

The dataset consists of single gzip compressed CSV file. The first line of the file is a header describing the features. The first column is a GMT timestamp of the sample in the format as "2021-03-16 00:00:00".  The rest of the columns provide the comma-separated values of the features collected from each node in the corresponding capture.

A suffix with the nodeid is added to each feature. For instance, the feature having the number of processes of node with nodeid 24 is named as "processes-24". In total, 63 different nodes showed up during the samples, each being assigned a different nodeid.


Features are of two types: (i) absolute values, for instance, the CPU 1-minute load average, and (ii) counters that are monotonically increased, for instance the number of transmitted packets. We have converted counter-type kernel variables to rates, by dividing the difference between two consecutive samples, over the difference of the corresponding timestamps in seconds, as shown in the following pseudo-code:
  feature.rate are columns computed from feature as
    feature.rate <- (feature[2:n]-feature[1:(n-1)])/(epoch[2:n]-epoch[1:(n-1)])
    feature.rate <- feature.rate[feature.rate >= 0] # discard samples where the counter is restarted
   where n is the number of samples

features
- processes     number of processes
- loadavg.m1     1 minute load average
- softirq.rate     servicing softirqs
- iowait.rate     waiting for I/O to complete
- intr.rate      
- system.rate     processes executing in kernel mode
- idle.rate     twiddling thumbs
- user.rate     normal processes executing in user mode
- irq.rate     servicing interrupts
- ctxt.rate     total number of context switches across all CPUs
- nice.rate     niced processes executing in user mode
- nr_slab_unreclaimable     The part of the Slab that can't be reclaimed under memory pressure
- nr_anon_pages     anonymous memory pages
- swap_cache     Memory that once was swapped out, is swapped back in but still also is in the swapfile
- page_tables     Memory used to map between virtual and physical memory addresses
- swap      
- eth.txe.rate     tx errors over all ethernet interfaces
- eth.rxe.rate     rx errors over all ethernet interfaces
- eth.txb.rate     tx bytes over all ethernet interfaces
- eth.rxb.rate     rx bytes over all ethernet interfaces
- eth.txp.rate     tx packets over all ethernet interfaces
- eth.rxp.rate     rx packets over all ethernet interfaces
- wifi.txe.rate     tx errors over all wireless interfaces
- wifi.rxe.rate     rx errors over all wireless interfaces
- wifi.txb.rate     tx bytes over all wireless interfaces
- wifi.rxb.rate     rx bytes over all wireless interfaces
- wifi.txp.rate     tx packets over all wireless interfaces
- wifi.rxp.rate     rx packets over all wireless interfaces
- txb.rate        tx bytes over all ethernet and wifi interfaces
- txp.rate        tx packets over all ethernet and wifi interfaces
- rxb.rate        rx bytes over all ethernet and wifi interfaces
- rxp.rate        rx packets over all ethernet and wifi interfaces
- sum.xb.rate    tx+rx bytes over all ethernet and wifi interfaces
- sum.xp.rate    tx+rx packets over all ethernet and wifi interfaces
- diff.xb.rate      tx-rx bytes over all ethernet and wifi interfaces
- diff.xp.rate      tx-rx packets over all ethernet and wifi interfaces

Files

Files (41.6 MB)

Name Size Download all
md5:3c5f42c9fda8c715645243c2f525c985
41.6 MB Download

Additional details

References

  • L. Cerdà-Alabern and Iuhasz, G., "Anomaly Detection in Wireless Community Networks using PCA", in Jornadas de Concurrencia y Sistemas Distribuidos 2020+1, Barcelona, Spain, 2021. https://dsg.ac.upc.edu/sites/default/files/dsg/jcsd-wmn-ad-a4.pdf
  • L. Cerdà-Alabern, Iuhasz, G., and Gemmi, G., "Anomaly detection for fault detection in wireless community networks using machine learning", Computer Communications, vol. 202, pp. 191-203, 2023.