Support Vector Machine Prediction a Man in the Middle Attack on Traffic Networking
DOI:
https://doi.org/10.29194/NJES.28030330Keywords:
Computer Network, Clustering, Support Vector Machine, Man in the Middle attackAbstract
The goal of the study is to predict the Man in the Middle attack in the packets of Wireshark program by using Support Vector Machines (SVM).In the time of using the internet, it has become a tool targeted by attackers and hackers; it is a serious threat to the devices. A uniqueness of an attack that appears in multiple identities for legitimate agencies. It is very necessary to know the behavior attack and predict the possible actions of an attacker. In this research a detection of Man in the Middle attack by monitoring the Wireshark program and recording any changes can be recognized in packet information. The classification of packets is divided into two categories (normal and abnormal). The proposed model is designed in many stages: loading data, processing data, training data, and testing data. The detection of SVM based on abnormal network packet through movement packets in the Wireshark program that needs to deal with current packets to recognize a new attack that one does not have prior knowledge of its detection, and there is a need for an intelligent way to separate network packets that represent normal. The proposed approach achieved an accuracy of 97.34% in detecting attacks. The results show that the proposed model effectively visualizes attacker behavior from data that represents abnormal network attackers. Research achieves successful accuracy in predicting abnormalities.
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S. Nabilah, S. Novita, N. Isra', A. Maria, and S. Dahlan, “Computer networking,” Int. J. Comput. Netw. Inf. Secur., Jan. 2023.
A. Sumit and A. Anshul, “An introduction to computer networking,” Int. J. Comput. Sci. Inf. Technol. Res., vol. 2, no. 2, pp. 373–377, Apr.–Jun. 2014.
M. Mimi and L. Satishkumar, “Mitigation of DDOS and MITM attacks using belief based secure correlation approach in SDN-based IoT networks,” Int. J. Comput. Netw. Inf. Secur., vol. 1, pp. 52–68, 2022. DOI: https://doi.org/10.5815/ijcnis.2022.01.05
K. Ruzaina and H. Mohammad, “Network threats attacks and security measures: A review,” Int. J. Adv. Res. Comput. Sci., vol. 8, no. 8, Sep.–Oct. 2017. DOI: https://doi.org/10.26483/ijarcs.v8i8.4641
J. Tanya, “Artificial intelligence in computer networks,” Period. Eng. Nat. Sci., vol. 10, no. 1, pp. 309–322, Jan. 2022. DOI: https://doi.org/10.21533/pen.v10.i1.539
N. Evgenii and V. Vitalii, “Application of network traffic using machine learning methods,” Int. Sci. Pract. Conf., Dec. 2023.
U. Pranjal and S. Perina, “Classification network attack detection using machine learning,” Int. Res. J. Eng. Technol., vol. 8, Apr. 2021.
A. Mahmood, Al-Shareeda, M. Selvakumar, and S. Murtaja, “DDoS attacks detection using machine learning and deep learning techniques: Analysis and comparison,” Bull. Electr. Eng. Inform., vol. 12, no. 2, pp. 930–939, Apr. 2023. DOI: https://doi.org/10.11591/eei.v12i2.4466
A. Mohammad, H. Russul, T. Shatha, A. Ahmed, M. Mostafa, and S. Tole, “Distributed denial of service attack defense system-based auto machine learning algorithm,” Bull. Electr. Eng. Inform., vol. 12, no. 1, pp. 544–551, Feb. 2023. DOI: https://doi.org/10.11591/eei.v12i1.4537
G. Toufik, B. Mohamed, and P. Purnendu, “Automated diagnosis of attacks in internet of things using machine learning and frequency distribution techniques,” Bull. Electr. Eng. Inform., vol. 10, no. 2, pp. 950–961, Apr. 2021. DOI: https://doi.org/10.11591/eei.v10i2.2766
J. Weston, S. Mukherjee, and L. Pontil, “Feature selection for SVMs,” Adv. Neural Inf. Process. Syst., vol. 13, pp. 668–674, 2000.
M. Zerina, K. Dino, D. Nejdet, and Kemal, “Flood attack detection in cloud computing using support vector machine,” TEM J., vol. 6, pp. 752–759, Nov. 2017. DOI: https://doi.org/10.18421/TEM64-15
S. Muhammad, N. Shah, and Y. Xiangzhan, “Identification of attack traffic using machine learning in smart IoT networks,” Secur. Commun. Netw., Apr. 2022, doi: 10.1155/2022/9804596. DOI: https://doi.org/10.1155/2022/9804596
N. Vivine and M. Zuriani, “Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm,” Bull. Electr. Eng. Inform., vol. 12, no. 3, pp. 1817–1824, Jun. 2023, doi: 10.11591/eei.v12i3.4830. DOI: https://doi.org/10.11591/eei.v12i3.4830
N. Mukrimah, A. Amiza, Y. Naimah, and L. Ong, “Effective and efficient network anomaly detection system using machine learning algorithm,” Bull. Electr. Eng. Inform., vol. 8, no. 1, pp. 46–51, Mar. 2019, doi: 10.11591/eei.v8i1.1387. DOI: https://doi.org/10.11591/eei.v8i1.1387
A. Raveendra and G. Gurumoorthi, “Cloud-based machine learning algorithms for anomalies detection,” Indones. J. Electr. Eng. Comput. Sci., vol. 35, no. 1, pp. 156–164, Jul. 2024, doi: 10.11591/ijeecs.v35.i1.pp156-164. DOI: https://doi.org/10.11591/ijeecs.v35.i1.pp156-164
Vazquez-Saavedra, J. Jimenez, Loureiro-Acuna, Fernandez-Veiga, and Pedrouzo-Ulloa, “Homomorphic SVM inference for fraud detection,” Ongoing Research, 2019.
T. Mohammed, “Advanced support vector machine (ASVM) based detection for distributed denial of service (DDoS) attack,” Softw. Defined Netw., 2022, doi: 10.56294/dm202272. DOI: https://doi.org/10.56294/dm202272
J. Vinit, Wireshark Fundamentals: A Network Engineer’s Handbook to Analyzing Network Traffic, Apress, 2022, doi: 10.1007/978-1-4842-8002-7. DOI: https://doi.org/10.1007/978-1-4842-8002-7
B. Andreea, “Cyber-attacks – trends, patterns and security countermeasures,” Procedia Econ. Finance, vol. 28, pp. 24–31, Dec. 2015, doi: 10.1016/S2212-5671(15)01077-1. DOI: https://doi.org/10.1016/S2212-5671(15)01077-1
B. Bhushanm, G. Sahoo, and A. Raj, “Man-in-the-middle attack in wireless and computer networking: A review,” Int. Conf. Adv. Comput. Commun. Autom., Sep. 2017. DOI: https://doi.org/10.1109/ICACCAF.2017.8344724
P. Silvana, S. Phil, Z. Valentina, and P. Aleksandar, “Predicting bidding price in construction using support vector machine,” TEM J., vol. 5, no. 2, pp. 143–151, May 2016, doi: 10.18421/TEM52-04. DOI: https://doi.org/10.18421/TEM52-04
S. Pongsametrey and T. Nguonly, “Support vector machine (SVM) based classifier for Khmer printed character-set recognition,” APSIPA Annu. Summit Conf., 2014, doi: 10.1109/APSIPA.2014.7041823. DOI: https://doi.org/10.1109/APSIPA.2014.7041823
S. Anton, U. Wendi, B. Arif, and H. Khalid, “Content based image retrieval and support vector machine methods for face recognition,” TEM J., vol. 8, no. 2, pp. 389–395, May 2019, doi: 10.18421/TEM82-10. DOI: https://doi.org/10.18421/TEM82-10
C. Yange, M. Qinyu, W. Baocang, P. Duan, Z. Benyu, and H. Zhiyong, “Privacy-preserving multi-class support vector machine model on medical diagnosis,” IEEE J. Biomed. Health Inform., vol. 26, no. 7, pp. 3342–3353, Jul. 2022, doi: 10.1109/JBHI.2022.3157592. DOI: https://doi.org/10.1109/JBHI.2022.3157592
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