Cyberattacks targeting Internet of Things (IoT), have increased significantly, over the past decade, with the spread of internet-connected smart devices and applications. Routing Protocol for Low-Power and Lossy Network (RPL) enables messages to be routed between nodes for the Wireless Sensor Network in the network layer. RPL protocol, which is sensitive and difficult to protect, is exposed to various attacks. These attacks negatively affect data transmission and cause great destruction to the topology by consuming the resources. Hello Flooding (HF) attacks against RPL cause consumption of constrained resources (memory, processing and energy) in nodes. Therefore, in this study, a Gated Recurrent Unit network model based deep learning has been proposed to predict and prevent HF attacks on RPL protocol in IoT networks. The proposed model has been compared with Support Vector Machine and Logistic Regression methods, and different power states and total energy consumptions of the nodes have been taken into consideration and experimented with. The results confirm the promised and expected performance from the model in terms of source efficiency and IoT security. In addition, attack detection has been carried out with a much lower error rate than literature studies for HF attacks from RPL flood attacks.
Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It is a very useful clinical tool in the classification of epileptic seizures and the diagnosis of epilepsy. In this study, epilepsy diagnosis has been investigated using EEG records. For this purpose, an artificial neural network (ANN), widely used and known as an active classification technique, is applied. The particle swarm optimization (PSO) method, which does not need gradient calculation, derivative information, or any solution of differential equations, is preferred as the training algorithm for the ANN. A PSO-based neural network (PSONN) model is diversified according to PSO versions, and 7 PSO-based neural network models are described. Among these models, PSONN3 and PSONN4 are determined to be appropriate models for epilepsy diagnosis due to having better classification accuracy. The training methods-based PSO versions are compared with the backpropagation algorithm, which is a traditional method. In addition, different numbers of neurons, iterations/generations, and swarm sizes have been considered and tried. Results obtained from the models are evaluated, interpreted, and compared with the results of earlier works done with the same dataset in the literature.
Indoor air quality (IAQ) was investigated in two libraries of Bartin University. Particle matter, TVOC, and CH 2 O were measured simultaneously in different rooms of the libraries. Measurements were made by PCE-RCM 11 measuring device. They were conducted monthly between September 2019 and October 2020 for 2 weeks for each library. TVOC mean concentration was higher than the limit value reported by Seifert et al. ( 1999 ). PM 2.5 and PM 10 concentrations were higher in winter unlike TVOC and CH 2 O concentrations. The indoor seconder blowing dust was detected to be the most important source of particulate matter in the indoor environment. On the other hand, it was thought that the pollutants in the gas phase were affected by indoor and outdoor air temperature, indoor/outdoor air exchange rate, and indoor/outdoor pollutant sources. The parameters changed during the day and in different rooms of the libraries. Pollutants did not pose any hazard on human health, since the calculated HQ and HI ≤ 1 for both exposure groups. The indoor air of the libraries was detected to be slightly polluted and bad according to the IAQI and IEI, respectively. On the other hand, the indoor air quality of the libraries was good in terms of all parameters according to the IAQC. Supplementary information The online version contains supplementary material available at 10.1007/s10661-022-09818-8.
ÖzNesnelerin interneti (Internet of Things, IoT) cihazları, kablosuz algılayıcı ağlarında yaşanan gelişmelerle her geçen gün daha fazla kullanım oranına sahip olmaktadır. IoT cihazlarının tümünün birbirine bağlanması ile oluşan heterojen ağ, dışarıdan gelen saldırılara oldukça açıktır. Günümüze kadar birçok yönlendirme protokolü saldırıları ortaya atılmış olup gün geçtikçe saldırılar artmaya ve çeşitlenmeye devam etmektedir. Bununla birlikte, önerilen tespit ve önleme yöntemlerinin de günümüz şartlarına göre iyileştirilmesi ve güncel olması gerekmektedir. Sahte kimlik saldırıları, IoT' de ağ katmanında kayıplı ağlarda yönlendirme protokolünde (Routing Protocol for Low-Power and Lossy Network, RPL) yer almaktadır. Sahte kimlik saldırıları türünde düğümlerin sinyal gücüne bağlı saldırı tespitleri, en yaygın kullanılan ve önerilen yöntemlerdendir. Kaynak kısıtlı olan IoT cihazlarında, enerji korunumu ve düşük işlem yükü önemli hususların başında gelmektedir. Özellikle saldırı tespitinde kullanılan klasik yöntemler, saldırıların tespiti ve önlenmesinde yetersiz kalabilmektedir. Bu çalışmada, düğümlerin paket dağıtım oranları ve makine öğrenmesi yaklaşımlarından Naive-Bayes, Random Forest ve Lojistik Regresyon ile sahte kimlik saldırılarının tespiti önerilmiştir. Sahte kimlik saldırıları, klasik yöntemlere kıyasla daha yüksek başarım oranı (99.51% doğruluk) ile tespit edilmiştir.
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.