| Title | Network Security Evaluation Using Deep Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Mahmoud, Loreen, Praveen, Raja |
| Conference Name | 2020 15th International Conference for Internet Technology and Secured Transactions (ICITST) |
| Keywords | Communication networks, computer networks, Cyber-physical systems, DNN, human factors, Internet, Metrics, Network, Network security, Neural Network Security, Neural networks, policy-based governance, pubcrawl, Resiliency, security, security evaluating, Task Analysis |
| Abstract | One of the most significant systems in computer network security assurance is the assessment of computer network security. With the goal of finding an effective method for performing the process of security evaluation in a computer network, this paper uses a deep neural network to be responsible for the task of security evaluating. The DNN will be built with python on Spyder IDE, it will be trained and tested by 17 network security indicators then the output that we get represents one of the security levels that have been already defined. The maj or purpose is to enhance the ability to determine the security level of a computer network accurately based on its selected security indicators. The method that we intend to use in this paper in order to evaluate network security is simple, reduces the human factors interferences, and can obtain the correct results of the evaluation rapidly. We will analyze the results to decide if this method will enhance the process of evaluating the security of the network in terms of accuracy. |
| DOI | 10.23919/ICITST51030.2020.9351326 |
| Citation Key | mahmoud_network_2020 |
Network Security Evaluation Using Deep Neural Network