Очиқ кодли операцион тизимларда шифрлаш ва архивлашни ташкил этиш

IDS nomi	TP	FP
FAR %
Snort	97.4	3.7	58.8
Suricata	94.5	9.3	62.8
Bro	94.8	6.3	54.8

Yuqorida ko‘rishimiz mumkinki, FAR ko‘rsatkichi juda yuqori bo‘lmasa ham, bu xavfsizlik administratorlari uchun yaxshigina muammo tug‘diradi. Shuning uchun ham ushbu qiymatlarni iloji boricha kamaytirish zarur hisoblanadi.
FAR ni kamaytirishning samarali usullaridan biri IDS tizimlarini machine learning algoritmlari asosida qurayotgan ma’lumotlar to‘plami ichidan to‘g‘ri xususiyatlarni tanlash, neyronlar va hiddenlayerlar sonini to‘g‘ri belgilash hisoblanadi.
Foydalanilgan adabiyotlar

1. 
   Julisch, K.: Mining Alarm Clusters to Improve Alarm Handling Effciency. In 17^th Annual Computer Security Applications Conference (ACSAC). 2001. pp.12-21
   
2. 
   Yihua, L., Vemuri, V. R.: Use of K-Nearest Neighbor classifier for intrusion detection. Computers & Security 21(5). (2002) 439-448
   
3. 
   Tjhai G., Furnell S., Papadaki M., and Clarke N., “A Preliminary Two-Stage Alarm Correlation and Filtering System Using SOM Neural Network and K-Means Algorithm,” Centre for Security, Communications and Network Research, Computers & Security, vol. 29, no. 6, pp. 712 - 723, 2010.
   
4. 
   Spathoulas G. and Katsikas S., “Reducing False Positives in Intrusion Detection Systems,” Computer & Security, vol. 29, no. 1, pp. 35 - 44, pp. 1 - 10, 2009.
   
5. 
   Anuar N. and Sallehudin H., “Identifying False Alarm for Network Intrusion Detection System Using Data Mining and Decision Tree,” in Proceedings of the 7th Conference on Data Networks, Communications, Computers, Bucharest, Romania, pp. 22 - 28, 2008.
   
6. 
   S. Bozorov, "DDoS Attack Detection via IDS: Open Challenges and Problems," 2021 International Conference on Information Science and Communications Technologies (ICISCT), 2021, pp. 1-4, doi: 10.1109/ICISCT52966.2021.9670260.