论文标题
NFDLM：用于IoT域中DDOS攻击检测的基于轻巧的网络流量的深度学习模型
NFDLM: A Lightweight Network Flow based Deep Learning Model for DDoS Attack Detection in IoT Domains
论文作者
论文摘要
近年来，分布式拒绝服务（DDOS）对物联网（IoT）设备的攻击已成为全球互联网用户的主要关注点之一。对物联网生态系统攻击的来源之一是僵尸网络。 Intruders在短时间内发送大量消息，迫使IoT设备无法为其合法用户提供。这项研究提出了NFDLM，这是一种基于轻巧的人工神经网络（ANN）的分布式拒绝服务（DDOS）攻击检测框架，具有相互关联为特征选择方法，与长期短期记忆（LSTM）相比，它会产生优越的结果。总体而言，检测性能可实现大约99 \％的准确性，以检测僵尸网络的攻击。在这项工作中，我们设计并比较了四个不同的模型，其中两个模型基于ANN，另外两个基于LSTM来检测DDOS的攻击类型。
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.
