  With the rapid technological advancements, organizations need to rapidly
scale up their information technology (IT) infrastructure viz. hardware,
software, and services, at a low cost. However, the dynamic growth in the
network services and applications creates security vulnerabilities and new
risks that can be exploited by various attacks. For example, User to Root (U2R)
and Remote to Local (R2L) attack categories can cause a significant damage and
paralyze the entire network system. Such attacks are not easy to detect due to
the high degree of similarity to normal traffic. While network anomaly
detection systems are being widely used to classify and detect malicious
traffic, there are many challenges to discover and identify the minority
attacks in imbalanced datasets. In this paper, we provide a detailed and
systematic analysis of the existing Machine Learning (ML) approaches that can
tackle most of these attacks. Furthermore, we propose a Deep Learning (DL)
based framework using Long Short Term Memory (LSTM) autoencoder that can
accurately detect malicious traffics in network traffic. We perform our
experiments in a publicly available dataset of Intrusion Detection Systems
(IDSs). We obtain a significant improvement in attack detection, as compared to
other benchmarking methods. Hence, our method provides great confidence in
securing these networks from malicious traffic.
