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Growing number of network devices and services have led to increasing demandfor protective measures as hackers launch attacks to paralyze or stealinformation from victim systems. Intrusion Detection System (IDS) is one of theessential elements of network perimeter security which detects the attacks byinspecting network traffic packets or operating system logs. While existingworks demonstrated effectiveness of various machine learning techniques, onlyfew of them utilized the time-series information of network traffic data. Also,categorical information has not been included in neural network basedapproaches. In this paper, we propose network intrusion detection models basedon sequential information using long short-term memory (LSTM) network andcategorical information using the embedding technique. We have experimented themodels with UNSW-NB15, which is a comprehensive network traffic dataset. Theexperiment results confirm that the proposed method improve the performance,observing binary classification accuracy of 99.72\%.

Network Intrusion Detection based on LSTM and Feature Embedding