Intelligent Deep Learning for Anomaly-Based Intrusion Detection in IoT Smart Home Networks

被引:10
|
作者
Butt, Nazia [1 ]
Shahid, Ana [1 ]
Qureshi, Kashif Naseer [2 ]
Haider, Sajjad [1 ]
Ibrahim, Ashraf Osman [3 ]
Binzagr, Faisal [4 ]
Arshad, Noman [5 ]
机构
[1] Natl Univ Modern Languages, Fac Engn & Comp Sci, Dept Comp Sci, Islamabad 44000, Pakistan
[2] Univ Limerick, Dept Elect & Comp Engn, Limerick V94T9PX, Ireland
[3] Univ Malaysia Sabah, Fac Comp & Informat, Kota Kinabalu 88400, Malaysia
[4] King Abdulaziz Univ, Dept Comp Sci, POB 344, Rabigh 21911, Saudi Arabia
[5] Bahria Univ, Dept Comp Sci, Islamabad 44000, Pakistan
关键词
internet of things; smart homes; machine learning; intrusion; attacks; detection;
D O I
10.3390/math10234598
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
The Internet of Things (IoT) is a tremendous network based on connected smart devices. These networks sense and transmit data by using advanced communication standards and technologies. The smart home is one of the areas of IoT networks, where home appliances are connected to the internet and smart grids. However, these networks are at high risk in terms of security violations. Different kinds of attacks have been conducted on these networks where the user lost their data. Intrusion detection systems (IDSs) are used to detect and prevent cyberattacks. These systems are based on machine and deep learning techniques and still suffer from fitting or overfitting issues. This paper proposes a novel solution for anomaly-based intrusion detection for smart home networks. The proposed model addresses overfitting/underfitting issues and ensures high performance in terms of hybridization. The proposed solution uses feature selection and hyperparameter tuning and was tested with an existing dataset. The experimental results indicated a significant increase in performance while minimizing misclassification and other limitations as compared to state-of-the-art solutions.
引用
收藏
页数:19
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