文章基本信息

标题：Intrusion Detection in IoT Networks Using Deep Learning Algorithm
本地全文：下载
作者：Bambang Susilo ; Riri Fitri Sari
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2020
卷号：11
期号：5
页码：279-289
DOI：10.3390/info11050279
出版社：MDPI Publishing
摘要：The internet has become an inseparable part of human life, and the number of devices connected to the internet is increasing sharply. In particular, Internet of Things (IoT) devices have become a part of everyday human life. However, some challenges are increasing, and their solutions are not well defined. More and more challenges related to technology security concerning the IoT are arising. Many methods have been developed to secure IoT networks, but many more can still be developed. One proposed way to improve IoT security is to use machine learning. This research discusses several machine-learning and deep-learning strategies, as well as standard datasets for improving the security performance of the IoT. We developed an algorithm for detecting denial-of-service (DoS) attacks using a deep-learning algorithm. This research used the Python programming language with packages such as scikit-learn, Tensorflow, and Seaborn. We found that a deep-learning model could increase accuracy so that the mitigation of attacks that occur on an IoT network is as effective as possible.
关键词：machine learning; deep learning; Internet of Things; distributed denial-of-service attack; intrusion detection machine learning ; deep learning ; Internet of Things ; distributed denial-of-service attack ; intrusion detection