BotNet Intrusion Detection System in Internet of Things with Developed Deep Learning

The main issue of distributed system is obtaining consensus on some data volume to reach to reliability in unreliable system data. There are no available system in perfect mode in real life. It is because of some hardware and software errors, slow networks, packet drops errors, bit error rate and noise and etc. This article deal to two other criteria in terms of quality of services and energy consumption in muti-agent systems connected to networks to optimizing consensus scaled modeling. The types of network supposed to be Internet of Things (IoT). IoT requires the creation of new structures that use edge and fog computing environments due to the lack of optimal quality of services and bandwidth requirements. This new paradigm can accomplish many of internet tasks on the edge and fog which is capable to confronting a wide range of technologies in the IoT. For example, edge and fog computing can be used in smart home, smart city, smart driving and other things that are planned based on IoT mechanisms. Since resource management along with quality of services in the IoT has been challenging in recent years as the technology has grown, providing integrated structures is essential and connecting to link quality due to more internet speed. For this purpose, Block Chain with a minimum computational complexity and high level of flexibility are provided which are capable to create gateways for the IoT-based edge and fog computation with a regular and optimal access mechanism for link quality based on deep markove model. This research aims to provide a secure routing schema with the maximum authentication of data in the connected of link quality internet environment with multiple edge and fog computing for DDoS attack detection. The use of encryption mechanisms including DTLS and FSK is considered as the main approach used in the Block Chain structure for transmitting and receiving data in variety of application. The proposed approach is called the EQoSRIoT. Improving the quality of services includes delays, throughput, probability of data detection, probability of miss detection, and data lose rate to manage the resources of the network to create a new schema of authentication in routing is necessary. The results represented that the proposed approach has better performance in comparison to others.

کلیدواژه ها:

Internet of Things (IoT) ، Consensus Scaled ، Edge and Fog Computing ، Routing ، Energy ، Quality of Services (QoS) ، Deep Learning ، Markov Model ، DDoS Attack Detection