Random Under Sampling for Performance Improvement in Attack Detection on Internet of Vehicles Using Machine Learning

Muhammad Alden Nayef Anargya; Wildanil Ghozi; Fauzi Adi Rafrastara

doi:10.30591/jpit.v10i1.8034

Random Under Sampling for Performance Improvement in Attack Detection on Internet of Vehicles Using Machine Learning

Muhammad Alden Nayef Anargya, Wildanil Ghozi, Fauzi Adi Rafrastara

Abstract

The Internet of Vehicles (IoV) technology is one of the advancements derived from the Internet of Things (IoT) in the transportation sector, benefiting its users. However, the development of this technology cannot be separated from various security threats, particularly Denial of Service (DoS) and spoofing attacks. Given these threats, it is crucial to continuously develop methods used for detecting attacks on IoV systems. Several researchers have conducted research related to attacks and threats on IoV systems, and one such study resulted in a dataset called CICIoV2024. This dataset has an imbalanced class distribution. This study aims to examine the implementation of Random Under-Sampling to improve the performance of classification algorithms in detecting attacks on IoV systems. The algorithms used in this study include Decision Tree, K-Nearest Neighbors (KNN), and Random Forest. The test results show that the Random Forest algorithm achieved the best results with an accuracy of 98.5% and an F1-Score of 98.5%.

Keywords

Attack Detection, Internet of Vehicles, Machine Learning, Random Under-Sampling

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References

W. Apriyanti et al., “SOSIALISASI PENGGUNAAN INTERNET YANG SEHAT BAGI ANAK – ANAK DI YAYASAN DOMYADHU,” Abdi Jurnal Publikasi, vol. 1, no. 1, pp. 13–17, Sep. 2022.

F. Trisnawati, “SEMMUDIK : Selamat Mudik Menggunakan Helm Berbasis Internet of Things (IoT,” JICTEE, vol. 1, no. 1, Sep. 2020, doi: 10.33365/jictee.v1i1.696.

S. Kumar and J. Singh, “INTERNET OF VEHICLES (IOV) OVER VANETS: SMART AND SECURE COMMUNICATION USING IOT,” Scalable Computing: Practice and Experience, vol. 21, no. 3, pp. 425–440, 2020.

N. Anwar, D. R. Adhy, N. Widiyasono, R. Hermawan, M. A. Hadi, and M. Tarigan, “Internet of Things ; Model Moda Layanan Sistem Transportasi Internet of Vehicle,” Seminar Nasional Pengaplikasian Telematika (SINAPTIKA), vol. 2, no. 1, pp. 51–61, 2022.

S. S. Kanumalli, A. Ch, and P. Sri, “Secure V2V Communication in IOV using IBE and PKI based Hybrid Approach,” IJACSA, vol. 11, no. 1, pp. 466–472, 2020, doi: 10.14569/IJACSA.2020.0110157.

M. S. Korium, M. Saber, A. Beattie, A. Narayanan, S. Sahoo, and P. H. J. Nardelli, “Intrusion detection system for cyberattacks in the Internet of Vehicles environment,” Ad Hoc Networks, vol. 153, p. 103330, Feb. 2024, doi: 10.1016/j.adhoc.2023.103330.

E. Zadobrischi and M. Dimian, “Vehicular Communications Utility in Road Safety Applications: A Step toward Self-Aware Intelligent Traffic Systems,” Symmetry, vol. 13, no. 3, p. 438, Mar. 2021, doi: 10.3390/sym13030438.

S. M. Karim, A. Habbal, S. A. Chaudhry, and A. Irshad, “Architecture, Protocols, and Security in IoV: Taxonomy, Analysis, Challenges, and Solutions,” Security and Communication Networks, vol. 2022, pp. 1–19, Oct. 2022, doi: 10.1155/2022/1131479.

E. Alalwany and I. Mahgoub, “Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions,” Sensors, vol. 24, no. 2, p. 368, Jan. 2024, doi: 10.3390/s24020368.

N. Tabassum and C. R. K. Reddyy, “Review on QoS and security challenges associated with the internet of vehicles in cloud computing,” Measurement: Sensors, vol. 27, p. 100562, Jun. 2023, doi: 10.1016/j.measen.2022.100562.

E. C. P. Neto et al., “CICIoV2024: Advancing realistic IDS approaches against DoS and spoofing attack in IoV CAN bus,” Internet of Things, vol. 26, p. 101209, Jul. 2024, doi: 10.1016/j.iot.2024.101209.

T. Garg, N. Kagalwalla, P. Churi, A. Pawar, and S. Deshmukh, “A survey on security and privacy issues in IoV,” IJECE, vol. 10, no. 5, p. 5409, Oct. 2020, doi: 10.11591/ijece.v10i5.pp5409-5419.

K. M. Sai, B. B. Gupta, F. Colace, and K. T. Chui, “A lightweight Anomaly based DDoS flood attack detection for Internet of vehicles,” in CEUR Workshop Proceedings (CEUR-WS.org), CEUR Workshop Proceedings, Dec. 2021.

F. Khan et al., “Development of a Model for Spoofing Attacks in Internet of Things,” Mathematics, vol. 10, no. 19, p. 3686, Oct. 2022, doi: 10.3390/math10193686.

M. Mehta and K. Patel, “Experimental Study of Location Spoofing and Identity Spoofing Attack in Internet of Things Network:,” International Journal of Intelligent Information Technologies, vol. 18, no. 3, pp. 1–13, Sep. 2022, doi: 10.4018/IJIIT.309587.

K. Lynch, “Ops, Security Experts Alert of GPS Spoofing Near Iran,” Aviation International News (AIN). Accessed: Oct. 01, 2024. [Online]. Available: https://www.ainonline.com/aviation-news/air-transport/2023-09-26/ops-security-experts-alert-gps-spoofing-near-iran

F. A. Rafrastara, W. Ghozi, and A. Wardoyo, “Deteksi Serangan berbasis Machine Learning pada Internet of Vehicle,” in Inovasi AI dalam Mewujudkan Kemajuan Berkelanjutan, Universitas PGRI Semarang: Prosiding Seminar Nasional Informatika, 2024.

B. R. Kikissagbe, M. Adda, P. Célicourt, I. T. Haman, and A. Najjar, “Machine Learning for DoS Attack Detection in IoT Systems,” Procedia Computer Science, vol. 241, pp. 195–202, 2024, doi: 10.1016/j.procs.2024.08.027.

M. Aljabri et al., “Machine Learning-Based Detection for Unauthorized Access to IoT Devices,” JSAN, vol. 12, no. 2, p. 27, Mar. 2023, doi: 10.3390/jsan12020027.

S. Shi, Q. Wang, P. Xu, and X. Chu, “Benchmarking State-of-the-Art Deep Learning Software Tools,” Feb. 17, 2017, arXiv: arXiv:1608.07249. Accessed: Oct. 06, 2024. [Online]. Available: http://arxiv.org/abs/1608.07249

B. Rezaeianjouybari and Y. Shang, “Deep learning for prognostics and health management: State of the art, challenges, and opportunities,” Measurement, vol. 163, no. 107929, 2020, doi: https://doi.org/10.1016/j.measurement.2020.107929.

“CIC IoV dataset 2024: Advancing Realistic IDS Approaches against DoS and Spoofing Attack in IoV CAN bus.” 2024. Accessed: Oct. 06, 2024. [Online]. Available: https://unb.ca/cic/datasets/iov-dataset-2024.html

A. Patle and G. L. Prajapati, “An Effectual Hybrid Feature Selection Method Towards the Classification Performance with Support Vector Machine,” NEUROQUANTOLOGY, vol. 20, no. 11, pp. 2602–2612, Sep. 2022.

K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Global Transitions Proceedings, vol. 3, no. 1, pp. 91–99, Jun. 2022, doi: 10.1016/j.gltp.2022.04.020.

J. Prasetya, “Penerapan Klasifikasi Naive Bayes dengan Algoritma Random Oversampling dan Random Undersampling pada Data Tidak Seimbang Cervical Cancer Risk Factors,” LBZ, vol. 2, no. 2, pp. 11–22, Jul. 2022, doi: 10.59632/leibniz.v2i2.173.

S. Susan and A. Kumar, “The balancing trick: Optimized sampling of imbalanced datasets—A brief survey of the recent State of the Art,” Engineering Reports, vol. 3, no. 4, p. e12298, Apr. 2021, doi: 10.1002/eng2.12298.

F. A. Rafrastara, C. Supriyanto, C. Paramita, Y. P. Astuti, and F. Ahmed, “Performance Improvement of Random Forest Algorithm for Malware Detection on Imbalanced Dataset using Random Under-Sampling Method,” JPIT, vol. 8, no. 2, pp. 113–118, May 2023, doi: 10.30591/jpit.v8i2.5207.

N. Rodríguez, D. López, A. Fernández, S. García, and F. Herrera, “SOUL: Scala Oversampling and Undersampling Library for imbalance classification,” SoftwareX, vol. 15, p. 100767, Jul. 2021, doi: 10.1016/j.softx.2021.100767.

H. A. Ahmed, P. J. Muhammad Ali, A. K. Faeq, and S. M. Abdullah, “An Investigation on Disparity Responds of Machine Learning Algorithms to Data Normalization Method,” ARO, vol. 10, no. 2, pp. 29–37, Sep. 2022, doi: 10.14500/aro.10970.

V. Putri, “Normalisasi Data Dengan Menggunakan Model Min Max Untuk Klasifikasi Nasabah Potensial Pada Bidang Pembelian Properti Menggunakan Algoritma K-Nearest Neighbor,” Informasi dan Teknologi Ilmiah (INTI), vol. 11, no. 3, pp. 111–119, 2024.

A. Ambarwari, Q. J. Adrian, and Y. Herdiyeni, “Analisis Pengaruh Data Scaling Terhadap Performa Algoritme Machine Learning untuk Identifikasi Tanaman,” JURNAL RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 4, no. 1, pp. 117–122, 2020.

H. Henderi, “Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer,” IJIIS : Int. J. Inform. Inform. Systems., vol. 4, no. 1, pp. 13–20, Mar. 2021, doi: 10.47738/ijiis.v4i1.73.

F. M. Hana, “Klasifikasi Penderita Penyakit Diabetes Menggunakan Algoritma Decision Tree C4.5,” siskom-kb, vol. 4, no. 1, pp. 32–39, Oct. 2020, doi: 10.47970/siskom-kb.v4i1.173.

F. S. Pattiiha and H. Hendry, “Perbandingan Metode K-NN, Naïve Bayes, Decision Tree untuk Analisis Sentimen Tweet Twitter Terkait Opini Terhadap PT PAL Indonesia,” Jur. Ris. Kom., vol. 9, no. 2, p. 506, Apr. 2022, doi: 10.30865/jurikom.v9i2.4016.

N. K. K. Ardana et al., “Perbandingan Metode KNN, Naive Bayes, dan Regresi Logistik Binomial dalam Pengklasifikasian Status Ekonomi Negara,” Jambura J. Math, vol. 5, no. 2, Aug. 2023, doi: 10.34312/jjom.v5i2.21103.

R. Leonardo, J. Pratama, and C. Chrisnatalis, “Perbandingan Metode Random Forest Dan Naïve Bayes Dalam Prediksi Keberhasilan Klien Telemarketing,” JUTIKOMP, vol. 3, no. 2, pp. 455–459, Oct. 2020, doi: 10.34012/jutikomp.v3i2.1321.

K. Faturrahman and A. Sucipto, “OPTIMASI RANDOM FOREST DENGAN TEKNIK PRUNING UNTUK PREDIKSI DATA NASABAH BMT AL-HIKMAH PERMATA,” Jurnal Informatika Teknologi dan Sains (JINTEKS), vol. 6, no. 3, pp. 767–775, Aug. 2024.

D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” Jurnal Sains Komputer & Informatika (J-SAKTI), vol. 5, no. 2, pp. 697–711, 2021.

A. R. Purnajaya, R. Jelita, E. Tesvara, M. Nestelrody, and J. Irwansyah, “Penerapan Metode Radial Basis Function (RBF) dalam Mengklasifikasikan Penyakit Demam Berdarah,” Journal of Digital Ecosystem for Natural Sustainability (JoDENS), vol. 3, no. 1, pp. 1–4, 2023.

F. Kurniawati and D. Brahma Arianto, “Analisis Implementasi Seleksi Fitur Pada Klasifikasi Diabetes dengan Metode Corellation Matrix dan Algoritma Logistic Regression,” Informatik : Jurnal Ilmu Komputer, vol. 19, no. 3, pp. 157–164, Dec. 2023, doi: 10.52958/iftk.v19i3.6019.

DOI: https://doi.org/10.30591/jpit.v10i1.8034