| Title |
Belaidžio 802.11 standarto tinklo deautentifikavimo atakų aptikimo tyrimas panaudojant mašininio mokymo algoritmą / |
| Translation of Title |
Wireless 802.11 standard network deauthentication attack detection research using machine learning algorithm. |
| Authors |
Juškevičius, Saulius |
| Full Text |
|
| Pages |
55 |
| Keywords [eng] |
Adam optimizer ; network classification ; deep learning ; IEEE 802.11 standard |
| Abstract [eng] |
These days, wireless network is a popular technology. Most do not realize that the convenience of a wireless network also leads to easier access for intruders to carry out attacks. It can go unnoticed when an intruder executes an attack and has already intercepted network traffic between the user and the access point, thus transmitting private information to the intruder. Wireless intrusion detection systems can protect against such intrusions. Such systems are based on feature recognition or anomaly detection. In this work, a prototype that detects a wireless attack is realized. The used machine learning algorithm was researched and compared with other machine learning algorithms implemented for comparison with the collected data set. The data set was collected using additional monitoring stations. The data set is normalized and the properties of the machine learning algorithm are isolated. Algorithm trained, researched and compared. |
| Dissertation Institution |
Kauno technologijos universitetas. |
| Type |
Master thesis |
| Language |
Lithuanian |
| Publication date |
2021 |
