Pengembangan Perangkat Lunak Untuk Deteksi DDoS Berbasis Neural Network
Abstract views: 455
,
PDF downloads: 680
Abstract
System security issues are a vital factor that needs to be considered in the operation of systems and networks, which will later be used for disaster mitigation and preventing attacks on the network. Distributed Denial of Services (DDoS) is a form of attack carried out by individuals or groups to damage data through servers or malware in the form of flooding packets, therefore it can paralyze the network system used. Network security is a factor that must be maintained and considered in an information system. DDoS can take the form of Ping of Death, flood, Remote control attack, User Data Protocol (UDP) flood, and Smurf Attack. This study aims to develop software to detect DDoS attacks based on network traffic logs. The software has been tested and run according to the neural network algorithm. This software was developed with an interface that makes it easier for users to detect the source IP whether the IP is carrying out a DDoS attack or normal.
References
M. N. Faiz, O. Somantri, A. R. Supriyono, and A. W. Muhammad, “Impact of Feature Selection Methods on Machine Learning-based for Detecting DDoS Attacks : Literature Review,” J. Informatics Telecommun. Eng., vol. 5, no. 2, pp. 305–314, 2022, doi: 10.31289/jite.v5i2.6112.
Kaspersky, “DDoS attacks hit a record high in Q4 2021,” 2022. [Online]. Available: https://www.kaspersky.com/about/press-releases/2022_ddos-attacks-hit-a-record-high-in-q4-2021
A. D. Lopez, “Network Traffic Behavioral Analytics for Detection of DDoS Attacks,” SMU Data Sci. Rev., vol. 2, no. 1, p. 25, 2019.
A. Banitalebi Dehkordi, M. R. Soltanaghaei, and F. Z. Boroujeni, The DDoS attacks detection through machine learning and statistical methods in SDN, vol. 77, no. 3. Springer US, 2021. doi: 10.1007/s11227-020-03323-w.
A. W. Muhammad, I. Riadi, and S. Sunardi, “Deteksi Serangan DDoS Menggunakan Neural Network dengan Fungsi Fixed Moving Average Window,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 1, no. 3, p. 115, 2017, doi: 10.14421/jiska.2017.13-03.
A. Yudhana, I. Riadi, and F. Ridho, “DDoS classification using neural network and naïve bayes methods for network forensics,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 11, 2018.
M. Odusami, S. Misra, O. Abayomi-Alli, A. Abayomi-Alli, and L. Fernandez-Sanz, “A survey and meta-analysis of application-layer distributed denial-of-service attack,” Int. J. Commun. Syst., vol. 33, no. 18, pp. 1–24, 2020, doi: 10.1002/dac.4603.
M. A. Ridho and M. Arman, “Analisis Serangan DDoS Menggunakan Metode Jaringan Saraf Tiruan,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 9, no. 3, pp. 373–379, 2020, doi: 10.32736/sisfokom.v9i3.945.
A. Wirawan, C. Feresa, M. Foozy, and A. Azhari, “Machine Learning-Based Distributed Denial of Service Attack Detection on Intrusion Detection System Regarding to Feature Selection,” Int. J. Artif. Intelegence Res., vol. 4, no. 1, pp. 1–8, 2020, doi: 10.29099/ijair.v4i1.156.
P. Kaur, M. Kumar, and A. Bhandari, “A review of detection approaches for distributed denial of service attacks,” Syst. Sci. Control Eng., vol. 5, no. 1, pp. 301–320, 2017, doi: 10.1080/21642583.2017.1331768.
M. Aamir and S. M. A. Zaidi, “DDoS attack detection with feature engineering and machine learning: the framework and performance evaluation,” Int. J. Inf. Secur., vol. 18, no. 6, pp. 761–785, 2019, doi: 10.1007/s10207-019-00434-1.
M. F. Mridha et al., “A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis,” Cancers (Basel)., vol. 13, no. 23, p. 6116, Dec. 2021, doi: 10.3390/cancers13236116.
L. V. Fausset, Fundamental of Neural Networks Architectures, Algorithms, and Application. Englewood Cliffs, New York: Prentice-Hall, 1994.
S. Sambangi and L. Gondi, “A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression,” Proceedings, vol. 63, no. 1, p. 51, 2020, doi: 10.3390/proceedings2020063051.
L. F. Eliyan and R. Di Pietro, “DoS and DDoS attacks in Software Defined Networks: A survey of existing solutions and research challenges,” Futur. Gener. Comput. Syst., vol. 122, pp. 149–171, Sep. 2021, doi: 10.1016/j.future.2021.03.011.
E. M. Bårli, A. Yazidi, E. H. Viedma, and H. Haugerud, “DoS and DDoS mitigation using Variational Autoencoders,” Comput. Networks, vol. 199, no. June, p. 108399, 2021, doi: 10.1016/j.comnet.2021.108399.
S. S. Priya, M. Sivaram, D. Yuvaraj, and A. Jayanthiladevi, “Machine Learning based DDOS Detection,” in 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), Mar. 2020, pp. 234–237. doi: 10.1109/ESCI48226.2020.9167642.
Ö. Tonkal, H. Polat, E. Başaran, Z. Cömert, and R. Kocaoğlu, “Machine Learning Approach Equipped with Neighbourhood Component Analysis for DDoS Attack Detection in Software-Defined Networking,” Electronics, vol. 10, no. 11, p. 1227, May 2021, doi: 10.3390/electronics10111227.
A. Maslan, K. M. Bin Mohamad, and F. B. Mohd Foozy, “Feature selection for DDoS detection using classification machine learning techniques,” IAES Int. J. Artif. Intell., vol. 9, no. 1, pp. 137–145, 2020, doi: 10.11591/ijai.v9.i1.pp137-145.
M. Myint Oo, S. Kamolphiwong, T. Kamolphiwong, and S. Vasupongayya, “Advanced Support Vector Machine-(ASVM-) based detection for Distributed Denial of Service (DDoS) attack on Software Defined Networking (SDN),” J. Comput. Networks Commun., vol. 2019, 2019, doi: 10.1155/2019/8012568.
M. Taufan Asri Zaen, A. Tantoni, M. Ashari, P. Studi Studi Sistem Informasi, and S. Lombok, “DDoS ATTACK MITIGATION WITH INTRUSION DETECTION SYSTEM (IDS) USING TELEGRAM BOTS,” JISA (Jurnal Inform. dan Sains), vol. 04, no. 02, pp. 149–154, 2021.
A. Saied, R. E. Overill, and T. Radzik, “Detection of known and unknown DDoS attacks using Artificial Neural Networks,” Neurocomputing, vol. 172, pp. 385–393, 2015, doi: 10.1016/j.neucom.2015.04.101.
C. J. Hsieh and T. Y. Chan, “Detection DDoS attacks based on neural-network using Apache Spark,” 2016 Int. Conf. Appl. Syst. Innov. IEEE ICASI 2016, pp. 1–4, 2016, doi: 10.1109/ICASI.2016.7539833.
J. Schmidt-Hieber, “The Kolmogorov–Arnold representation theorem revisited,” Neural Networks, vol. 137, pp. 119–126, May 2021, doi: 10.1016/j.neunet.2021.01.020.
M. Aslam, “Introducing Kolmogorov-Smirnov Tests under Uncertainty: An Application to Radioactive Data,” ACS Omega, vol. 5, no. 1, pp. 914–917, 2020, doi: 10.1021/acsomega.9b03940.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
