Which algorithms can be used for Misuse Detection and anomaly detection?
In the following, we discuss some examples of machine learning methods applied in misuse detection systems.
- 3.1 Classification using association rules.
- 3.2 Artificial neural networks.
- 3.3 Support vector machines.
- 3.4 Decision tree and classification and regression tree.
- 3.5 Bayesian network classifier.
- 3.6 Naïve Bayes.
Which type of machine learning algorithm is used for anomaly detection in telecom networks?
An example of a machine learning approach to network anomaly detection is the time-based inductive learning machine (TIM) of Teng et al. [12]. Their algorithm constructs a set of rules based upon usage patterns.
How to detect anomaly in time series data?
You can have a look here, where many open-source algorithms specifically for anomaly detection on time-series data (e.g. metrics) are collected, both for online of offline settings. Almost all of them are unsupervised approaches that require no labels to detect the anomalies.
Where does the data for anomaly detection come from?
More in details, data are coming from some sensors/meters which record and collect data on boilers or other equipments. As I said before, the data which I have to work with, are timeseries, so a timestamp and the relative value detected by sensor; a value is anomalous when it’s bigger or smaller than the others near it; basically a peak.
What is the alert threshold for anomaly detection?
Alert Threshold: Specify a value above which the anomaly score generates an alert. The default value is 3.25, meaning that an alert is generated for every row containing a score of 3.25 or more. Y
What is the default martingale value in ML studio?
Length of Martingale and Strangeness Values: Specify the size of the history window, which is used to compute martingale values over the look-back history. The default value is 500, but you can specify any integer between 0 and 5000. For large time series, the default value should work well.