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Table 17 Summary of ML-based Fault Detection

From: A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Ref. ML Technique Network Dataset Features Output Evaluation
   (location)    (training) Settings Results
Rao [382] Statistical learning Cellular network Data collected from real cellular networks Mobile user call load profile Detect faults at ·Base station level ·Sector level ·Carrier level ·Channel level Not provided Bounded probability of false alarm
Baras et al. [37] A combination of NN (radial basis functions) Cellular network (X.25 protocol) Simulation with OPNET For each fault scenario ·Blocking of packets ·Queue sizes ·Packet throughput ·Utilization on links connecting subnetworks ·Packet end-to-end delays Detect one of the fault scenarios ·Reduced switch capacity ·Increased packet generation rate of a certain application ·Disabled switch ·Disabled links Varying number of hidden nodes between 175 and 230 Different rates of errors
Adda et al. [5] Supervised: ·k-Means ·FCM ·EM IP network of a school campus Obtained from a network with heavy and light traffic scenarios 12 variables of interface (IF) category collected through SNMP Fault classes: · Normal traffic ·Link failure traffic ·Server crash ·Broadcast storm ·Protocol error Not provided Precision for heavy scenario in router dataset ·k-Means = 40 · FCM = 85 · EM = 40
Moustapha and Selmic [324] Supervised: · RNN Wireless sensor network Collected from a simulated sensor network ·Previous outputs of sensor nodes · Current and previous output samples of neighboring sensor nodes Approximation of the output of the sensor node 8-10-1a Constant error smaller than state-of-the-art
Hajji [178] Unsupervised change detection method Local area networks Collected from a real network using remote monitoring agents Baseline random variable An alarm as soon as an anomaly occurs Time to detect : · 50 s to 17 min Accuracy: 100% · Low alarm rate: 0.12 alarms per hour
Hashmi et al. [181] Supervised: ·k-Means · FCM · SOM Broadband service provider network 1 million NFL data points from 5 service regions ·Fault occurrence date ·Time of the day ·Geographical region ·Fault cause ·Resolution time Identify the spatio-temporal patterns linked with high fault resolution times SOM on a 15x15 network grid for 154 epochs Sum of squared errors: ·k-Means = 2156788 · FCM = 2822823 · SOM = 1136
  1. aNumber of neurons at the input layer, hidden, and output layers, respectively