Botnet Attack Detection Using A Hybrid Supervised Fast-Flux Killer System

被引:1
|
作者
Al-Nawasrah, Ahmad [1 ]
Almomani, Ammar [2 ,3 ]
Al-Issa, Huthaifa A. [4 ]
Alissa, Khalid [5 ]
Alrosan, Ayat [6 ]
Alaboudi, Abdulellah A. [7 ]
Gupta, Brij B. [8 ]
机构
[1] British Univ Bahrain, Informat & Commun Technol Coll, Saar, Bahrain
[2] Al Balqa Appl Univ, Al Huson Univ Coll, IT Dept, POB 50, Irbid, Jordan
[3] Skyline Univ Coll, Res & Innovat Dept, POB 1797, Sharjah, U Arab Emirates
[4] Al Balqa Appl Univ, Al Huson Univ Coll, Elect & Elect Engn Dept, As Salt, Jordan
[5] Shaqra Univ, Coll Comp & Informat Technol, POB 33, Riyadh, Saudi Arabia
[6] Imam Abdulrahman Bin Faisal Univ, Dept Networks & Commun, Coll Comp Sci & Informat Technol, Saudi ARAMCO Cybersecur Chair, POB 1982, Dammam 31441, Saudi Arabia
[7] Skyline Univ Coll, Sch Informat Technol, POB 1797, Sharjah, U Arab Emirates
[8] Natl Inst Technol, Dept Comp Engn, Kurukshetra, Haryana, India
来源
JOURNAL OF WEB ENGINEERING | 2022年 / 21卷 / 02期
关键词
Hybrid supervised fast-flux; botnet detection; DeSNN; DDOS ATTACKS; MITIGATION; NETWORKS; DOMAINS;
D O I
10.13052/jwe1540-9589.2123
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
A Fast Flux Service Network (FFSN) domain name system method is a technique used on botnet that bot herders used to support malicious botnet actions to rapidly change the domain name IP addresses and to increase the life of malicious servers. While several methods for the detection of FFSN domains are suggested, they are still suffering from relatively low accuracy with the zero-day domain in particular. Throughout the current research, a system that's deemed new is proposed. The latter system is called (the Fast Flux Killer System) and is abbreviated as (FFKS)). It allows one to have the FF-Domains "zero-day", via a deployment built on (ADeSNN). It is a hybrid, which consists of two stages. The online phase according to the learning outcomes from the offline phase works on detecting the zero-day domains while the offline phase helps in enhancing the classification performance of the system in the online phase. This system will be compared to a previously published work that was based on a supervised detection method using the same ADeSNN algorithm to have the FFSNs domains detected, also to show better performance in detecting malicious domains. A public data set for the impacts of the hybrid ADeSNN algorithm is employed in the experiment. When hybrid ADeSNN was used over the supervised one, the experiments showed better accuracy. The detection of zero-day fast-flux domains is highly accurate (99.54%) in a mode considered as an online one.
引用
收藏
页码:179 / 201
页数:23
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