| Title | Network Anomaly Detection with Payload-based Analysis |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Özdel, Süleyman, Damla Ateş, Pelin, Ateş, Çağatay, Koca, Mutlu, Anarım, Emin |
| Conference Name | 2022 30th Signal Processing and Communications Applications Conference (SIU) |
| Keywords | anomaly detection, attack detection, deep packet inspection, Entropy, feature extraction, Inspection, n-gram analysis, Payload, Payloads, pubcrawl, resilience, Resiliency, Scalability, Signal processing, statistical analysis |
| Abstract | Network attacks become more complicated with the improvement of technology. Traditional statistical methods may be insufficient in detecting constantly evolving network attack. For this reason, the usage of payload-based deep packet inspection methods is very significant in detecting attack flows before they damage the system. In the proposed method, features are extracted from the byte distributions in the payload and these features are provided to characterize the flows more deeply by using N-Gram analysis methods. The proposed procedure has been tested on IDS 2012 and 2017 datasets, which are widely used in the literature. |
| Notes | ISSN: 2165-0608 |
| DOI | 10.1109/SIU55565.2022.9864866 |
| Citation Key | ozdel_network_2022 |
Network Anomaly Detection with Payload-based Analysis