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.

No abstract

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.

Central venous stenosis (CVS) is usually a late‐diagnosed clinical entity that is common in hemodialysis patients. It causes various problems ranging from hemodialysis difficulty to loss of the arterio–venous (A–V) fistula. In the present study, we aimed to determine the effect of drug eluting balloon while excluding the influence of other variable factors by evaluating the same individuals with plain and paclitaxel‐eluting balloons. This research was a prospective study of 18 symptomatic hemodialysis patients (age 50.9 ± 14.0 years, range 32–72 years; 11 male, 7 female) with CVS who underwent treatment by plain balloon angioplasty (PBA) and paclitaxel‐eluting balloon angioplasty (PEBA) in our hospital from January 2016 to June 2017. First, third and sixth month central vein patency rates were compared. The median patency rates of central veins were 109.0 (range: 10–324) days after PBA and 238.5 (range: 157–501) days after PEBA (p < 0.001). There was no statistically significant difference between PBA and PEBA angioplasty in one‐month patency (p ˃ 0.05). By contrast, a statistically significant difference was found between 3‐ and 6‐month patency rates (p = 0.031 and p ˂ 0.001, respectively). Kaplan–Meier analysis revealed that the primary cumulative patency rate of PEBA was significantly longer than that of PBA (p ˂ 0.001). In this prospective study, PEBA patency is superior to PBA patency in the treatment of CVS in dialysis patients.