A Cutting-Edge Deep Learning Method For Enhancing IoT Security

TL;DR

This paper presents a deep learning-based IoT intrusion detection system using CNN and LSTM, achieving high accuracy and real-time processing to enhance IoT network security.

Contribution

It introduces an innovative deep learning model combining CNN and LSTM for IoT intrusion detection, demonstrating superior performance over traditional methods.

Findings

Achieved 99.52% accuracy on CICIDS2017 dataset

Outperformed traditional IDS in real-time processing and false alarm rate

Proven effective for scalable IoT network security

Abstract

There have been significant issues given the IoT, with heterogeneity of billions of devices and with a large amount of data. This paper proposed an innovative design of the Internet of Things (IoT) Environment Intrusion Detection System (or IDS) using Deep Learning-integrated Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Our model, based on the CICIDS2017 dataset, achieved an accuracy of 99.52% in classifying network traffic as either benign or malicious. The real-time processing capability, scalability, and low false alarm rate in our model surpass some traditional IDS approaches and, therefore, prove successful for application in today's IoT networks. The development and the performance of the model, with possible applications that may extend to other related fields of adaptive learning techniques and cross-domain applicability, are discussed. The research involving deep learning for IoT cybersecurity offers a potent solution for significantly improving network security.