Table 17 Summary of ML-based Fault Detection
Ref. | ML Technique | Network | Dataset | Features | Output | Evaluation | |
|---|---|---|---|---|---|---|---|
| Â | Â | (location) | Â | Â | (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 |