Mitigating the Impact of IoT Routing Attacks on Power Consumption in IoT Healthcare Environment using Convolutional Neural Network

Abstract

IoT provides big contribution to healthcare for elderly care at home. There are many attacks in IoT healthcare network which may destroy the entire network. A propose a framework may be produced an efficient treatment for elderly care at home with low power consumption. A framework contains three phases names; medical data collection layer, routing and network layer and medical application layer. It intends to increase security performance through prediction and detection attacks in real time. Cooja simulator is used for generating real-time IoT routing datasets including normal and malicious motes based on different types of power. The generated IoT routing dataset using data augmentation (SMOTE) to increase the size of dataset. The preprocessing of the generating dataset using three methods of feature selection which are weight by rule, Chi-Squared and weight by tree importance using random forest reduce noise and over-fitting. A proposed model uses convolution neural network (CNN) to detect and predict IoT routing attacks to identify suspicious network traffic. A number of studies have been carried out in this area, but the issue of the extent of the impact of attacks on energy consumption is an interesting topic. Attacks can affect the network completely, in particular on the power consumption of smart devices. Therefore; the main target of this research is detecting and predicting different types of IoT routing attacks which have impact on power consumption and destroy the entire network. This work analyzes the impact of IoT routing attacks on different power consumption using CNN to achieve low power consumption by detecting different types of routing attacks. The experimental results show CNN can detect different types of attacks that have a bad impact on power consumption. It achieves high accuracy, precision, recall, correlation and low rate in error and logistic loss and this leads to decrease power consumption.

A Systematic Literature Review on Machine and Deep Learning Approaches for Detecting Attacks in RPL-Based 6LoWPAN of Internet of Things

“…Kamel and Elhamayed [1] proposed a mitigation approach for minimizing the effect of IoT routing attacks on PRC in the healthcare environment. The proposed work uses a Convolution Neural Network (CNN) to discover five routing attacks: HF, SF, SH, WH, and VN.…”

“…IoT plays a significant role in connecting smart objects anytime, anywhere, and any service through any network. The entire world looks forward to creating a new smart world that changes people's lifestyles and how things work in our world [1,2]. Consequently, the IoT affects everything from our lifestyle to how we live in this era of technological convergence and inter-connectivity.…”

A new smart approach of an efficient energy consumption management by using a machine-learning technique

“…The validation approach used to find and learn a new electrical model for the 𝐴 𝑝 and the input data T. The validation approach aims to satisfy efficient energy consumption which means the output results (𝐸 𝑐 ) must be without the amount of energy expenditure; therefore; if the efficient energy is satisfied, then the validation approach is finished else if the energy consumption data is choosing the different training data, the difference between the training data and the input data is the key because no validation approach can achieve the desired goal with invalid estimation data. This process is known as "generalization" [25]- [27]; however, it can occasionally lead to "overfitting" [28]- [30] which is caused by corruption of the generalizable data (output result with the amount of energy expenditure), lowered performance, and caused invalidity of the generalization process so we propose a solution for this problem in our approach. The validation contained some points to satisfy a good performance for the electrical model which has output results with the amount of energy expenditure.…”

COVID-19 Pandemic

“…There have been many studies on the impacts of COVID-19. Some have focused on how to protect the elderly from the virus using artificial intelligence, machine learning, and the Internet of things (for example, Kamel & Elhamayed, 2020). Some studies focused on the efficacy of vaccinations (for example, Hajirahimova & Aliyeva, 2022).…”