A Hybrid Method for Intrusion Detection in the IOT

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Faghih Aliabadi Hossein

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Journal Paper

Paper Information

Journal: INTERNATIONAL JOURNAL OF WEB RESEARCH Year:2022 | Volume:5 | Issue:2 Page(s): 54-60

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Information Journal Paper

Title

A Hybrid Method for Intrusion Detection in the IOT

Author(s)

Faghih Aliabadi Hossein | Issue Writer Certificate

Keywords

Internet of Things

Intrusion Detection System

Hybrid System

Deep Learning Introduction

Abstract

In computer networks, introducing an intrusion detection system with high precision and accuracy is considered vital. In this article, a proposed model using a deep learning algorithm is presented and its results are analyzed. To evaluate the performance of this algorithm, NSL-KDD, CIC-IDS 2018, UNSW-NB15 and MQTT datasets have been used. The evaluation criteria include precision, accuracy, F1 score, and, readability. The new approach uses a hybrid algorithm that includes a convolutional neural network (CNN) to extract general features and long-short-term memory (LSTM) to extract periodic features that are in the form of a layer. are cross-connected, it is introduced to detect penetration. This algorithm showed the highest known accuracy of 99% on the NSL-KDD dataset. It has reached 97% in all criteria in UNSW-NB15, 96% in all criteria in CIC-IDS 2018, and also, in MQTT for three abstraction levels of features, i. e. packet-based flow features, unidirectional flow, and The two-way flow has reached above 97%, which shows the superiority of this algorithm.

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APA: Copy

Faghih Aliabadi, Hossein. (2022). A Hybrid Method for Intrusion Detection in the IOT. INTERNATIONAL JOURNAL OF WEB RESEARCH, 5(2), 54-60. SID. https://sid.ir/paper/1044365/en

Vancouver: Copy

Faghih Aliabadi Hossein. A Hybrid Method for Intrusion Detection in the IOT. INTERNATIONAL JOURNAL OF WEB RESEARCH[Internet]. 2022;5(2):54-60. Available from: https://sid.ir/paper/1044365/en

IEEE: Copy

Hossein Faghih Aliabadi, “A Hybrid Method for Intrusion Detection in the IOT,” INTERNATIONAL JOURNAL OF WEB RESEARCH, vol. 5, no. 2, pp. 54–60, 2022, [Online]. Available: https://sid.ir/paper/1044365/en

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