Biblio

In case of deploying additional network security equipment in a new location, network service providers face difficulties such as precise management of large number of network security equipment and expensive network operation costs. Accordingly, there is a need for a method for security-aware network service provisioning using the existing network security equipment. In order to solve this problem, there is an existing reinforcement learning-based routing decision method fixed for each node. This method performs repeatedly until a routing decision satisfying end-to-end security constraints is achieved. This generates a disadvantage of longer network service provisioning time. In this paper, we propose security constraints reinforcement learning based routing (SCRR) algorithm that generates routing decisions, which satisfies end-to-end security constraints by giving conditional reward values according to the agent state-action pairs when performing reinforcement learning.

Nowadays, network is one of the essential parts of life, and lots of primary activities are performed by using the network. Also, network security plays an important role in the administrator and monitors the operation of the system. The intrusion detection system (IDS) is a crucial module to detect and defend against the malicious traffics before the system is affected. This system can extract the information from the network system and quickly indicate the reaction which provides real-time protection for the protected system. However, detecting malicious traffics is very complicating because of their large quantity and variants. Also, the accuracy of detection and execution time are the challenges of some detection methods. In this paper, we propose an IDS platform based on convolutional neural network (CNN) called IDS-CNN to detect DoS attack. Experimental results show that our CNN based DoS detection obtains high accuracy at most 99.87%. Moreover, comparisons with other machine learning techniques including KNN, SVM, and Naïve Bayes demonstrate that our proposed method outperforms traditional ones.