| Title |
An improved model for alleviating layer seven distributed denial of service intrusion on webserver / |
| Authors |
Odusami, M ; Misra, S ; Adetiba, E ; Abayomi-Alli, O ; Damasevicius, R ; Ahuja, R |
| DOI |
10.1088/1742-6596/1235/1/012020 |
| Full Text |
|
| Is Part of |
Journal of Physics: Conference Series: the 3rd international conference on computing and applied informatics 2018, 18–19 September 2018, Medan, Sumatera Utara, Indonesia.. Bristol : IOP Publishing. 2019, vol. 1235, iss. 1, art. no. 012020, p. 1-6.. ISSN 1742-6588. eISSN 1742-6596 |
| Keywords [eng] |
deep learning ; denial-of-service attack ; hypertext systems |
| Abstract [eng] |
Application layer or Layer Seven Distributed Denial of service (L7DDoS) intrusion is one of the greatest threats that intrusion a webserver. The hackers have different motives which could be for Extortion, Exfiltration e.t.c Researchers have employed several methods to prevent L7DDoS intrusion especially using machine learning. Although Machine learning techniques has proven to be very effective with high detection accuracy, the approach still find it difficult to detect Hyper Text Transfer Protocol (HTTP) based botnet traffic on web server with high false positive rate. The adoption of deep learning based technique using Long Short Term Memory (LSTM) will alleviate this problem. |
| Published |
Bristol : IOP Publishing |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2019 |
| CC license |
|
