  In the recent years, Distributed Denial of Service (DDoS) attacks on Internet
of Things (IoT) devices have become one of the prime concerns to Internet users
around the world. One of the sources of the attacks on IoT ecosystems are
botnets. Intruders force IoT devices to become unavailable for its legitimate
users by sending large number of messages within a short interval. This study
proposes NFDLM, a lightweight and optimised Artificial Neural Network (ANN)
based Distributed Denial of Services (DDoS) attack detection framework with
mutual correlation as feature selection method which produces a superior result
when compared with Long Short Term Memory (LSTM) and simple ANN. Overall, the
detection performance achieves approximately 99\% accuracy for the detection of
attacks from botnets. In this work, we have designed and compared four
different models where two are based on ANN and the other two are based on LSTM
to detect the attack types of DDoS.
