A Semi-supervised Approach for Abnormal User Behaviour Detection in Network - Malviya, Nandit; Takalikar, Mukta S (International Journal of Computer Sciences and Engineering, 2018)
Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are generic. Detecting abnormal user behavior is of great significance for a secured network. The traditional detection method, which is based on machine learning, usually needs to accumulate a large amount of abnormal behaviour data from different times or even different network environments for training, so the data gathered is not in line with practical data and thus affects. There are many systems being developed which analyzes big data logs and recognizes patterns in it with already predefined classes using machine learning algorithm. The current research in this area implements algorithm like SVM (support vector machines), PCA (principal component analysis) mostly to classify data. Apart from this many are working to find different classes to classify anomalous activities. In this project, analysis of various machine learning algorithms will be carried out irrespective of user behaviour.