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.
The internet of things (IoT) technology presents an intelligent way to improve our lives and contributes to many fields such as industry, communications, agriculture, etc. Unfortunately, IoT networks are exposed to many attacks that may destroy the entire network and consume network resources. This paper aims to propose intelligent process automation and an auto-configured intelligent automation detection model (IADM) to detect and prevent malicious network traffic and behaviors/events at distributed multi-access edge computing in an IoT-based smart city. The proposed model consists of two phases. The first phase relies on the intelligent process automation (IPA) technique and contains five modules named, specifically, dataset collection and pre-processing module, intelligent automation detection module, analysis module, detection rules and action module, and database module. In the first phase, each module composes an intelligent connecting module to give feedback reports about each module and send information to the next modules. Therefore, any change in each process can be easily detected and labeled as an intrusion. The intelligent connection module (ICM) may reduce the search time, increase the speed, and increase the security level. The second phase is the dynamic adaptation of the attack detection model based on reinforcement one-shot learning. The first phase is based on a multi-classification technique using Random Forest Trees (RFT), k-Nearest Neighbor (K-NN), J48, AdaBoost, and Bagging. The second phase can learn the new changed behaviors based on reinforced learning to detect zero-day attacks and malicious events in IoT-based smart cities. The experiments are implemented using a UNSW-NB 15 dataset. The proposed model achieves high accuracy rates using RFT, K-NN, and AdaBoost of approximately 98.8%. It is noted that the accuracy rate of the J48 classifier achieves 85.51%, which is lower than the others. Subsequently, the accuracy rates of AdaBoost and Bagging based on J48 are 98.9% and 91.41%, respectively. Additionally, the error rates of RFT, K-NN, and AdaBoost are very low. Similarly, the proposed model achieves high precision, recall, and F1-measure high rates using RFT, K-NN, AdaBoost, and Bagging. The second phase depends on creating an auto-adaptive model through the dynamic adaptation of the attack detection model based on reinforcement one-shot learning using a small number of instances to conserve the memory of any smart device in an IoT network. The proposed auto-adaptive model may reduce false rates of reporting by the intrusion detection system (IDS). It can detect any change in the behaviors of smart devices quickly and easily. The IADM can improve the performance rates for IDS by maintaining the memory consumption, time consumption, and speed of the detection process.
Spam Email messages have a big problem either for users or for the Internet service providers. The content of such messages may contain viruses and bad information. The spam messages also occupy a huge amount of space on the mail boxes. So, the process of Emails' classification is very important to be analyzed and discussed. This research work aims at classifying the email messages into either spam or non-spam. The E-mail messages or a dataset can be represented in a matrix form. The rows of the matrix are representing the instances (messages) while the columns are representing the features of such instances. K-Nearest Neighbor (KNN) and Naïve Bayes (NB) are two classifiers where they are used to classify the email messages. The proposed approach based on partitioning the dataset into segment and compared with the adopted approach. Moreover, feature selection methods are adopted to choose the significant features and eliminate the others to avoid processing overheads. The choice of the relevant features plays an important role of the classification accuracy. In this work, some feature selection methods are adopted, analyzed, and operated. The performance of such methods is compared. Moreover, a feature selection method is proposed and discussed. The performance of the proposed feature selection method is compared with the adopted ones. This work is operated on a chosen dataset taken from the Internet. The dataset contains about four-thousand messages with fifty-eight features. Moreover, the dataset is supported with a target feature representing the class labels. From the practical experiments it is shown that the performance of the proposed method is better than the adopted ones. It is also expected that the proposed method is applicable to other datasets for other application domains.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.