  As IoT networks become more complex and generate massive amounts of dynamic
data, it is difficult to monitor and detect anomalies using traditional
statistical methods and machine learning methods. Deep learning algorithms can
process and learn from large amounts of data and can also be trained using
unsupervised learning techniques, meaning they don't require labelled data to
detect anomalies. This makes it possible to detect new and unknown anomalies
that may not have been detected before. Also, deep learning algorithms can be
automated and highly scalable; thereby, they can run continuously in the
backend and make it achievable to monitor large IoT networks instantly. In this
work, we conduct a literature review on the most recent works using deep
learning techniques and implement a model using ensemble techniques on the KDD
Cup 99 dataset. The experimental results showcase the impressive performance of
our deep anomaly detection model, achieving an accuracy of over 98\%.
