A Deep Neural Network Approach for Analysis of Firewall Log Data
| dc.contributor.author | Lillmond, Chandesh | |
| dc.contributor.author | Suddul, Geerish | |
| dc.date.accessioned | 2022-02-25T03:52:35Z | |
| dc.date.available | 2022-02-25T03:52:35Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | In this paper, we propose an intelligent approach for the classification of incoming and outgoing firewall traffic packets. A firewall is a quintessential tool that ensures the control of traffic over machines’ communication over a network. It uses a set of specific rules to define the traffic and thus assists in avoiding cyber-attacks which can be very costly to an organization. Our intelligent approach is mainly through the application of the Deep Neural Network (DNN) Machine Learning algorithm so that packets going through the firewall can be automatically classified as either allow, deny or drop. Our experiments demonstrate a classification accuracy of around 94%, which is higher when compared with other approaches. | en_US |
| dc.identifier.citation | Lillmond Chandesh, Suddul Geerish (2021), A Deep Neural Network Approach for Analysis of Firewall Log Data, International Conference on Advances in Computing and Technology (ICACT–2021) Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka 42-46 | en_US |
| dc.identifier.uri | http://repository.kln.ac.lk/handle/123456789/24493 | |
| dc.publisher | Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka | en_US |
| dc.subject | Firewall, DNN, Classification | en_US |
| dc.title | A Deep Neural Network Approach for Analysis of Firewall Log Data | en_US |