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
相关论文
共 50 条
  • [31] Hybrid Machine Learning Model for Efficient Botnet Attack Detection in IoT Environment
    Ali, Mudasir
    Shahroz, Mobeen
    Mushtaq, Muhammad Faheem
    Alfarhood, Sultan
    Safran, Mejdl
    Ashraf, Imran
    IEEE ACCESS, 2024, 12 : 40682 - 40699
  • [32] Hybrid Deep Learning for Botnet Attack Detection in the Internet-of-Things Networks
    Popoola, Segun, I
    Adebisi, Bamidele
    Hammoudeh, Mohammad
    Gui, Guan
    Gacanin, Haris
    IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (06) : 4944 - 4956
  • [33] Fast attack detection system using log analysis and attack tree generation
    Kim, Duhoe
    Kim, Yong-Hyun
    Shin, Dongil
    Shin, Dongkyoo
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2019, 22 (Suppl 1): : 1827 - 1835
  • [34] Fast attack detection system using log analysis and attack tree generation
    Duhoe Kim
    Yong-Hyun Kim
    Dongil Shin
    Dongkyoo Shin
    Cluster Computing, 2019, 22 : 1827 - 1835
  • [35] A genomic rule-based KNN model for fast flux botnet detection
    Ayo, Femi Emmanuel
    Awotunde, Joseph Bamidele
    Folorunso, Sakinat Oluwabukonla
    Adigun, Matthew O.
    Ajagbe, Sunday Adeola
    EGYPTIAN INFORMATICS JOURNAL, 2023, 24 (02) : 313 - 325
  • [36] Detecting Malicious Fast-Flux Domains Using Feature-based Classification Techniques
    Truong, Dinh-Tu
    Tran, Dac-Tot
    Huynh, Bao
    JOURNAL OF INTERNET TECHNOLOGY, 2020, 21 (04): : 1061 - 1072
  • [37] Performance evaluation of Botnet DDoS attack detection using machine learning
    Tuan, Tong Anh
    Long, Hoang Viet
    Son, Le Hoang
    Kumar, Raghvendra
    Priyadarshini, Ishaani
    Son, Nguyen Thi Kim
    EVOLUTIONARY INTELLIGENCE, 2020, 13 (02) : 283 - 294
  • [38] Performance evaluation of Botnet DDoS attack detection using machine learning
    Tong Anh Tuan
    Hoang Viet Long
    Le Hoang Son
    Raghvendra Kumar
    Ishaani Priyadarshini
    Nguyen Thi Kim Son
    Evolutionary Intelligence, 2020, 13 : 283 - 294
  • [39] Feature selection and hybrid CNNF deep stacked autoencoder for botnet attack detection in IoT
    Kalidindi, Archana
    Arrama, Mahesh Babu
    COMPUTERS & ELECTRICAL ENGINEERING, 2025, 122
  • [40] Parallel Botnet Detection System by Using GPU
    Hung, Che-Lun
    Wang, Hsiao-Hsi
    2014 IEEE/ACIS 13TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE (ICIS), 2014, : 65 - 70