ADDITIONAL MENU
Fuzzy Tsukamoto-Based Detection of Ping of Death Attacks: Advancing Network Security with Precise Classification
Abstract
Internet services have the potential to be targeted by hackers using various DDoS (Distributed Denial of Service) attack techniques, including the ping of death attack. This attack involves multiple machines launching simultaneous attacks on the database server and File Transfer Protocol (FTP), resulting in severe consequences for computer networks. To effectively classify such attacks, the Fuzzy Tsukamoto method is employed, which represents each IF-THEN rule as a Fuzzy set with a corresponding membership function. Fuzzy logic offers great flexibility, tolerance for imprecise data, and the ability to model highly complex and nonlinear functions. By implementing this classification technique, it becomes easier to differentiate and analyze network traffic captured by Wireshark, enabling the detection of ping of death attacks against the server with maximum accuracy through the Fuzzy Tsukamoto method in the classification process.
Keywords
DDOS; Ping of Death; Fuzzy Tsukamoto; Classification
References
P. Singh, Y. K. Dwivedi, K. S. Kahlon, A. Pathania, and R. S. Sawhney, “Can twitter analytics predict election outcome? An insight from 2017 Punjab assembly elections,” Gov Inf Q, vol. 37, no. 2, 2020, doi: 10.1016/j.giq.2019.101444.
L. Ni and J. Liu, “A Framework for Domain-Specific Natural Language Information Brokerage,” J Syst Sci Syst Eng, vol. 27, no. 5, 2018, doi: 10.1007/s11518-018-5389-1.
E. Beulen, A. Plugge, and J. van Hillegersberg, “Formal and relational governance of artificial intelligence outsourcing,” Information Systems and e-Business Management, vol. 20, no. 4, 2022, doi: 10.1007/s10257-022-00562-7.
S. Hajiheidari, K. Wakil, M. Badri, and N. J. Navimipour, “Intrusion detection systems in the Internet of things: A comprehensive investigation,” Computer Networks, vol. 160. 2019. doi: 10.1016/j.comnet.2019.05.014.
Z. Shah, I. Ullah, H. Li, A. Levula, and K. Khurshid, “Blockchain Based Solutions to Mitigate Distributed Denial of Service (DDoS) Attacks in the Internet of Things (IoT): A Survey,” Sensors, vol. 22, no. 3. 2022. doi: 10.3390/s22031094.
F. Yihunie, E. Abdelfattah, and A. Odeh, “Analysis of ping of death DoS and DDoS attacks,” in 2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018, 2018. doi: 10.1109/LISAT.2018.8378010.
Z. Chen, F. Han, J. Cao, X. Jiang, and S. Chen, “Cloud computing-based forensic analysis for collaborative network security management system,” Tsinghua Sci Technol, vol. 18, no. 1, 2013, doi: 10.1109/TST.2013.6449406.
R. Sharma and A. Thakral, “Identifying botnets: Classification and detection,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 9 Special Issue, 2019, doi: 10.35940/ijitee.I1021.0789S19.
Y. Cui and Q. Qian, “MIND: Message classification based controller scheduling method for resisting DDoS Attack in Software-Defined Networking,” in 2020 5th International Conference on Computer and Communication Systems, ICCCS 2020, 2020. doi: 10.1109/ICCCS49078.2020.9118597.
F. Mukhametzyanov, A. S. Katasev, A. M. Akhmetvaleev, and D. V. Kataseva, “The neural network model of DDoS attacks identification for information management fail,” International Journal of Supply Chain Management, vol. 8, no. 5, 2019.
L. Brikh, O. Guenounou, and T. Bakir, “Selection of Minimum Rules from a Fuzzy TSK Model Using a PSO–FCM Combination,” Journal of Control, Automation and Electrical Systems, vol. 34, no. 2, 2023, doi: 10.1007/s40313-022-00975-2.
J. Bonato, Z. Mrak, and M. Badurina, “Speed regulation in fan rotation using fuzzy inference system,” Pomorstvo, vol. 29, no. 1, 2015.
J. Singh and M. J. Nene, “A Survey on Machine Learning Techniques for Intrusion Detection Systems,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 11, 2013.
T. Raja Sree and S. Mary Saira Bhanu, “Detection of HTTP flooding attacks in cloud using fuzzy bat clustering,” Neural Comput Appl, vol. 32, no. 13, 2020, doi: 10.1007/s00521-019-04473-6.
M. Bhopale, A. Kshatriya, P. Kumar, and R. Jagdeesh Kanan, “Fuzzy based system for analysis of DDOS attacks,” International Journal of Pharmacy and Technology, vol. 8, no. 3, 2016.
V. de M. Rios, P. R. M. Inácio, D. Magoni, and M. M. Freire, “Detection of reduction-of-quality DDoS attacks using Fuzzy Logic and machine learning algorithms,” Computer Networks, vol. 186, 2021, doi: 10.1016/j.comnet.2020.107792.
A. Maslan, K. M. Bin Mohamad, and F. B. Mohd Foozy, “Feature selection for DDoS detection using classification machine learning techniques,” IAES International Journal of Artificial Intelligence, vol. 9, no. 1, 2020, doi: 10.11591/ijai.v9.i1.pp137-145.
K. J. Singh, K. Thongam, and T. De, “Detection and differentiation of application layer DDoS attack from flash events using fuzzy-GA computation,” IET Inf Secur, vol. 12, no. 6, 2018, doi: 10.1049/iet-ifs.2017.0500.
I. H. Sarker, “CyberLearning: Effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks,” Internet of Things (Netherlands), vol. 14, 2021, doi: 10.1016/j.iot.2021.100393.
V. Gaur and R. Kumar, “Analysis of Machine Learning Classifiers for Early Detection of DDoS Attacks on IoT Devices,” Arab J Sci Eng, vol. 47, no. 2, 2022, doi: 10.1007/s13369-021-05947-3.
L. Pan and Y. Deng, “A novel similarity measure in intuitionistic fuzzy sets and its applications,” Eng Appl Artif Intell, vol. 107, 2022, doi: 10.1016/j.engappai.2021.104512.
M. A. Taha and L. Ibrahim, “Traffic simulation system based on fuzzy logic,” in Procedia Computer Science, 2012. doi: 10.1016/j.procs.2012.09.084.
A. H. Agustin, G. K. Gandhiadi, and Oka Tjokorda Bagus, “Penerapan Metode Fuzzy Sugeno Untuk Menentukan Harga Jual Sepeda Motor Bekas,” E-Jurnal Matematika, vol. 5, no. 4, 2016, doi: 10.24843/mtk.2016.v05.i04.p138.
M. Ivanov et al., “Fuzzy modelling of big data of HR in the conditions of industry 4.0,” in CEUR Workshop Proceedings, 2020.
DOI: http://dx.doi.org/10.24014/ijaidm.v6i2.23858
Refbacks
- There are currently no refbacks.
Office and Secretariat:
Big Data Research Centre
Puzzle Research Data Technology (Predatech)
Laboratory Building 1st Floor of Faculty of Science and Technology
UIN Sultan Syarif Kasim Riau
Jl. HR. Soebrantas KM. 18.5 No. 155 Pekanbaru Riau – 28293
Website: http://predatech.uin-suska.ac.id/ijaidm
Email: ijaidm@uin-suska.ac.id
e-Journal: http://ejournal.uin-suska.ac.id/index.php/ijaidm
Phone: 085275359942
Journal Indexing:
Google Scholar | ROAD | PKP Index | BASE | ESJI | General Impact Factor | Garuda | Moraref | One Search | Cite Factor | Crossref | WorldCat | Neliti | SINTA | Dimensions | ICI Index Copernicus
IJAIDM Stats