NFDLM: A Lightweight Network Flow based Deep Learning Model for DDoS Attack Detection in IoT Domains

TL;DR

This paper introduces NFDLM, a lightweight neural network framework for detecting DDoS attacks in IoT environments, achieving around 99% accuracy and outperforming LSTM-based models.

Contribution

The study presents a novel optimized ANN-based DDoS detection model with mutual correlation feature selection, outperforming LSTM and simple ANN models in IoT security.

Findings

Achieved approximately 99% detection accuracy.

Compared four models, including ANN and LSTM variants.

Demonstrated superior performance of the proposed model.

Abstract

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.