Publication: An Improved Live Anomaly Detection System (I-LADS) based on Deep Learning Algorithms

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Network Anomaly detection is an open issue that considers the problem of finding patterns in data that do not conform to expected behavior. Anomalies exhibit themselves in network statistics differently; therefore developing general models of normal network behavior and anomalies is a challenging task. This paper presents an Improved Live Anomaly Detection System (I-LADS) based on AutoEncoder (AE), a well known deep learning algorithm, to detect network traffic anomalies. I-LADS comes in two versions: (i) I-LADS-v1, that uses filters to independently model IP addresses from the NetFlow dataset, making it possible to train one model for each filtered IP address; and (ii) I-LADS-v2, that uses no filter and therefore a single algorithm is trained for all IP addresses. Experiments have been conducted using a valid dataset containing over two million connections to build a model with multiple features in order to identify the approach that most accurately detects traffic anomalies in the target network. Preliminary results show a promising solution with 99% and 94% of accuracy for the supervised and unsupervised learning approaches respectively.