DOI: 10.5281/zenodo.6169917
title: Dataset for Anomaly Detection in a Production Wireless Mesh Community Network

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. <br> 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:<br> feature.rate are columns computed from feature as<br> feature.rate &lt;- (feature[2:n]-feature[1:(n-1)])/(epoch[2:n]-epoch[1:(n-1)])<br> feature.rate &lt;- feature.rate[feature.rate &gt;= 0] # discard samples where the counter is restarted<br> where n is the number of samples <strong>features</strong><br> - processes number of processes<br> - loadavg.m1 1 minute load average<br> - softirq.rate servicing softirqs<br> - iowait.rate waiting for I/O to complete<br> - intr.rate <br> - system.rate processes executing in kernel mode<br> - idle.rate twiddling thumbs<br> - user.rate normal processes executing in user mode<br> - irq.rate servicing interrupts<br> - ctxt.rate total number of context switches across all CPUs<br> - nice.rate niced processes executing in user mode<br> - nr_slab_unreclaimable The part of the Slab that can't be reclaimed under memory pressure<br> - nr_anon_pages anonymous memory pages<br> - swap_cache Memory that once was swapped out, is swapped back in but still also is in the swapfile<br> - page_tables Memory used to map between virtual and physical memory addresses<br> - swap <br> - eth.txe.rate tx errors over all ethernet interfaces<br> - eth.rxe.rate rx errors over all ethernet interfaces<br> - eth.txb.rate tx bytes over all ethernet interfaces<br> - eth.rxb.rate rx bytes over all ethernet interfaces<br> - eth.txp.rate tx packets over all ethernet interfaces<br> - eth.rxp.rate rx packets over all ethernet interfaces<br> - wifi.txe.rate tx errors over all wireless interfaces<br> - wifi.rxe.rate rx errors over all wireless interfaces<br> - wifi.txb.rate tx bytes over all wireless interfaces<br> - wifi.rxb.rate rx bytes over all wireless interfaces<br> - wifi.txp.rate tx packets over all wireless interfaces<br> - wifi.rxp.rate rx packets over all wireless interfaces<br> - txb.rate tx bytes over all ethernet and wifi interfaces<br> - txp.rate tx packets over all ethernet and wifi interfaces<br> - rxb.rate rx bytes over all ethernet and wifi interfaces<br> - rxp.rate rx packets over all ethernet and wifi interfaces<br> - sum.xb.rate tx+rx bytes over all ethernet and wifi interfaces<br> - sum.xp.rate tx+rx packets over all ethernet and wifi interfaces<br> - diff.xb.rate tx-rx bytes over all ethernet and wifi interfaces<br> - diff.xp.rate tx-rx packets over all ethernet and wifi interfaces
description: {"references": ["L. Cerd\u00e0-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. Cerd\u00e0-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."]}