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

Abstract

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.

A Decentralized Approach to Smart Home Security: Blockchain With Red-Tailed Hawk-Enabled Deep Learning

“…Low soaring: the individuals encircle the target by flying lower toward the ground in a spiral line and the mathematical modelling is shown in Eq. ( 4 Where 𝑟 denotes a control gain [1,2], the initial value ranges within [0.5−3] is 𝑅 0 , 𝐴 indicates the angel gain [515], and 𝑟𝑎𝑛𝑑 shows the random integer [0,1]. This parameter helps the hawk fly around the target with spiral movement.…”

“…A smart home is nothing but an Internet of Things (IoT) combined residence that provides users security, comfort, healthiness, enhanced regular living, etc [1]. Smart home techniques have been mainly proficient in creating people's lives more easily and improved.…”

Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models

“…While PCA has shown promising results in certain scenarios, it suffers from limitations in handling variations in lighting conditions and facial expressions. Another popular approach is the use of support vector machines (SVMs) for face recognition, as demonstrated by [ 31 ]. SVMs have been effective in capturing complex patterns in face images.…”

“…Combining anomaly detection and facial recognition technology for application in smart home security has been the subject of a limited number of studies. In [ 31 ] developed a solution to boost the security of smart homes by fusing deep-learning-based anomaly detection with face recognition software. Their security system is effective because it can recognize users precisely and report any anomalies in real time.…”