Complete Guide to Anomaly Detection Techniques
Anomaly Detection refers to the identification of the events that don’t agree to the patterns present in a dataset leading to defects, errors or faults. Anomaly Detection with Machine Learning algorithms detects and classify the anomalies and make predictions from the data.
Two majorly classified techniques involve –
- Unsupervised Machine Learning for Anomaly Detection
- Supervised Machine Learning for Anomaly Detection
Unsupervised Machine Learning for Anomaly Detection
In unsupervised, all data is unlabeled, and the algorithms learn to integrate structure from the input data. Unsupervised Detection does not require training data assuming network connections of normal traffic, as well as malicious traffic, differs from the normal traffic. By these conditions, data of similar instances considered as error-free and of different patterns regarded as malicious. Some of unsupervised Machine Learning algorithms involve K-Means, Self-Organising Maps(SOM), Apriori algorithm.
Supervised Machine Learning for Anomaly Detection
In supervised, all data labeled and the algorithms learn to predict the output from the input data. Supervised Detection requires a labeled training set containing normal and abnormal data. Supervised detection uses a combination with statistical schemes, including the capability of encoding interdependencies between variables and predicting events. Moreover, incorporates both prior knowledge and data. Supervised Machine Learning algorithms involve Support Vector Machine Learning, Bayesian Networks, Decision Trees, K-NN.