Abstract
For the advancement of technologies in vehicular industry, an intelligent Software Defined Vehicular Ad-hoc Network (SDVANET) which decoupled its data and control plane providing a golden opportunity to the researchers and academicians working in the related field. The limitation associated with SDVANET lies in the fact that it has problem in transferring reliable multimedia information among different users. To address this limitation, researchers of the domain tried to provide more intelligence to SDVANET by deploying Machine Learning (ML) algorithms to this network. This deployment of ML algorithms provided a safe, reliable, secure, optimal methodology for transferring multimedia information among SDVANET networks. In this work, researchers have used two datasets: small dataset and big dataset according to number of iterations used for collection. Total six supervised ML algorithms: Random Forest (RF), Decision-Tree (DT), Logistic Regression (LR), Naive-Bayes (NB), Support Vector Machine (SVM), K-Nearest Neigbour (kNN) are chosen to participate in the training and testing process. For result analysis, two simulation parameters: Receiver operating characteristic-Area under the ROC curve (ROC-AUC) and Confusion matrix are utilized. Numerical Results: Classification Accuracy (CA) (88.3%), F1-Measure (89.3%), precision (94.3%), recall (93.3%), AUC (92.3%) indicate that ML algorithms used for small dataset (LR, SVM and NB), NB is outperforming and ones used for big dataset (DT, RF and kNN), DT gives best performance based on numerical results: CA (95.4%), F1-Measure (93.4%), precision (92.3%), recall (98.2%), AUC (94.9%). So, NB and DT are the best ML algorithms for transferring the secure multimedia information, avoiding accidents among SDVANET networks.
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Data Availability
All data generated or analyzed during this study are included in this published article and will be available in future if required.
Abbreviations
- AI:
-
Artificial Intelligence
- AUC:
-
Area under the ROC curve
- DT:
-
Decision Tree
- GPS:
-
Global Positioning system
- kNN:
-
K-nearest Neigbour
- LR:
-
Logistic Regression
- ML:
-
Machine Learning
- NB:
-
Naïve-Bayes
- OSM:
-
Open Street Map
- RF:
-
Random Forest
- ROC:
-
Receiver Operating Characteristic
- RL:
-
Reinforcement Learning
- SL:
-
Supervised Learning
- SDVN:
-
Software Defined Vehicular Network
- SDVANET:
-
Software Defined Vehicular Ad-hoc Network
- SVM:
-
Support Vector Machine
- USL:
-
Un-Supervised Learning
- V-2-V:
-
Vehicle-to-Vehicle
- VANET:
-
Vehicular Ad-hoc Networking
- V-2-I:
-
Vehicle-to-Infrastructure
- V-2-X:
-
Vehicle-to-Everything
References
Abdullah DM, Abdulazeez AM (2021) Machine Learning Applications based on SVM Classification: A Review. Qubahan Acad J 1(2):81–90. https://doi.org/10.48161/qaj.v1n2a50
Adbeb T, Wu D, Ibrar M (2020) Software-defined networking (SDN) based VANET architecture: Mitigation of traffic congestion. Int J Adv Comput Sci Appl 11(3). https://doi.org/10.14569/IJACSA.2020.0110388
Ahuja N, Singal G, Mukhopadhyay D, Kumar N (2021) Automated DDOS attack detection in software defined networking. J Network Comp App 187:103108. https://doi.org/10.1016/j.jnca.2021.103108
Ali J, Roh BH, Lee B, Oh J, Adil M (2020) A machine learning framework for prevention of software-defined networking controller from DDoS attacks and dimensionality reduction of big data. In 2020 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 515–519). IEEE. https://doi.org/10.1109/ICTC49870.2020.9289504
Alsarhan A, Alauthman M, Alshdaifat E, Al-Ghuwairi, AR, Al-Dubai A (2021) Machine Learning-driven optimization for SVM-based intrusion detection system in vehicular ad hoc networks. J Amb Int Human Comput. pp. 1–10. https://doi.org/10.1007/s12652-021-02963-x
Anbalagan S, Bashir AK, Raja G, Dhanasekaran P, Vijayaraghavan G, Tariq U, Guizani M (2021) Machine-learning-based efficient and secure RSU placement mechanism for software-defined-IoV. IEEE Internet of Things Journal 8(18):13950–13957. https://doi.org/10.1109/JIOT.2021.3069642
Anyanwu GO, Nwakanma CI, Lee JM, Kim DS (2022) Optimization of RBF-SVM Kernel using Grid Search Algorithm for DDoS Attack Detection in SDN-based VANET. IEEE Int Things J. https://doi.org/10.1109/JIOT.2022.3199712
Balkus SV, Wang H, Cornet BD, Mahabal C, Ngo H, Fang H (2022) A Survey of Collaborative Machine Learning Using 5G Vehicular Communications. IEEE Commun Surv Tutor 24(2):1280–1303. https://doi.org/10.1109/COMST.2022.3149714
Bangui H, Ge M, Buhnova B (2022) A hybrid machine learning model for intrusion detection in VANET. Comput 104(3):503–531. https://doi.org/10.1007/s00607-021-01001-0
Bonaccorso G (2017) Machine learning algorithms. Packt Publishing Ltd, Birmingham
Chen J, Huang H, Tian S, Qu Y (2009) Feature selection for text classification with Naïve Bayes. Exp Syst Appli 36(3):5432–5435. https://doi.org/10.1016/j.eswa.2008.06.054
Ghonge MM (2022) Software-defined network-based vehicular Ad Hoc Networks: A comprehensive review. Software Defined Networking for Ad Hoc Networks. pp. 33–53. https://doi.org/10.1007/978-3-030-91149-2_2
Gupta BB, Gaurav A, Marín EC, Alhalabi W (2022) Novel Graph-Based Machine Learning Technique to Secure Smart Vehicles in Intelligent Transportation Systems. IEEE Trans Int Trans Syst. https://doi.org/10.1109/TITS.2022.3174333
Huang J, Ling CX (2005) Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng 17(3):299–310. https://doi.org/10.1109/TKDE.2005.50
Islam MM, Khan MTR, Saad MM, Kim D (2021) Software-defined vehicular network (SDVN): A survey on architecture and routing. J Syst Archit 114:101961.https://doi.org/10.1016/j.sysarc.2020.101961
Jaballah WB, Conti M, Lal C (2019) A survey on software-defined VANETs: benefits, challenges, and future directions. arXiv preprint arXiv:1904.04577. https://doi.org/10.48550/arXiv.1904.04577
Javaheri D, Gorgin S, Lee JA, Masdari M (2023) Fuzzy logic-based DDoS attacks and network traffic anomaly detection methods: classification, overview, and future perspectives. Infor Sci. https://doi.org/10.1016/j.ins.2023.01.067
Jiang T, Gradus JL, Rosellini AJ (2020) Supervised machine learning: a brief primer. Behavior Therap 51(5):675–687. https://doi.org/10.1016/j.beth.2020.05.002
Jordan S, Chandak Y, Cohen D, Zhang M, Thomas P (2020) Evaluating the performance of reinforcement learning algorithms. In: International Conference on Machine Learning (pp. 4962–4973). PMLR. https://doi.org/10.48550/arXiv.2111.06978
Khatri S, Vachhani H, Shah S, Bhatia J, Chaturvedi M, Tanwar S, Kumar N (2021) Machine learning models and techniques for VANET based traffic management: Implementation issues and challenges. Peer-to-Peer Netw Appl 14(3):1778–1805. https://doi.org/10.1007/s12083-020-00993-4
Lee W-M (2019) Python® machine learning. John Wiley & Sons, Hoboken, pp 269–284. https://doi.org/10.1002/9781119557500.ch12
Liang L, Ye H, Li GY (2018) Toward intelligent vehicular networks: A machine learning framework. IEEE Int Things J 6(1):124–135. https://doi.org/10.1109/JIOT.2018.2872122
Liu Y, Wang Y, Zhang J (2012) New machine learning algorithm: Random forest. In International Conference on Information Computing and Applications (pp. 246–252). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34062-8_32
Mahalakshmi G, Uma E (2020) Machine learning based feature selection for intrusion detection system in VANET. In International Conference on Artificial Intelligence, Network Security and Data Science (IeCAN). https://doi-ds.org/doilink/11.2021-73936339/IRJHIS2111023
Nayak RP, Sethi S, Bhoi SK, Sahoo KS, Nayyar A (2022) ML-MDS: Machine Learning based Misbehavior Detection System for Cognitive Software-defined Multimedia VANETs (CSDMV) in smart cities. Multimed Tools Appli. pp. 1–21. https://doi.org/10.1007/s11042-022-13440-8
Osisanwo FY, Akinsola JET, Awodele O, Hinmikaiye JO, Olakanmi O, Akinjobi J (2017) Supervised machine learning algorithms: classification and comparison. Int J Comput Trends Technol (IJCTT) 48(3):128–138
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Duchesnay E (2011) Scikit-learn: Machine learning in Python. J Mach Lear Res 12:2825–2830. https://doi.org/10.48550/arXiv.1308.4214
Raju MA, Rajagopalan, N (2022). A survey on various architectural models using software-defined networks. In Mobile Computing and Sustainable Informatics (pp. 641–657). Springer, Singapore. https://doi.org/10.1007/978-981-19-2069-1_44
Raw RS, Kumar M, Singh N (2021). Software-defined vehicular ad-hoc network: a theoretical approach. In Cloud-Based Big Data Analytics in Vehicular Ad-Hoc Networks (pp. 141–164). IGI Global https://doi.org/10.4018/978-1-7998-2764-1.ch007
Schein AI, Ungar LH (2007) Active learning for logistic regression: an evaluation. Mach Learn 68(3):235–265. https://doi.org/10.1007/s10994-007-5019-5
Sehrawat P, Chawla M (2020) Review on vehicular communication using vehicular networks. National Conference on Medical instrumentation, Biomaterials and Signal Processing, D.C.R.U.S.T, Murthal; Patron in Chief, pp 130
Sehrawat P, Chawla M (2021. Determination of optimal topology based VANET Routing Protocol. In: 2021 International Conference on Industrial Electronics Research and Applications (ICIERA) (pp. 1–6). IEEE
Sehrawat P Chawla M (2022) Interpretation and investigations of topology based routing protocols applied in dynamic system of VANET. Wireless Personal Communications. pp. 1–27
Seth I, Guleria K, Panda SN (2022) Introducing intelligence in vehicular ad hoc networks using machine learning algorithms. ECS Trans 107(1):8395
Shobowale KO, Mukhtar Z, Yahaya B, Ibrahim Y, Momoh MO (2023) Latest Advances on Security Architecture for 5G Technology and Services. Int J Softw Eng Comput Syst 9(1):27–38
Singh A, Thakur N, Sharma A (2016). A review of supervised machine learning algorithms. In 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 1310–1315). IEEE
Somvanshi M, Chavan P, Tambade S, Shinde SV (2016) A review of machine learning techniques using decision tree and support vector machine. In: 2016 International Conference on Computing Communication Control and Automation (ICCUBEA) (pp. 1–7). IEEE https://doi.org/10.1109/ICCUBEA.2016.7860040
Sultana R, Grover J, Tripathi M (2020) A novel framework for misbehavior detection in sdn-based vanet. In 2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (pp. 1–6). IEEE. https://doi.org/10.1109/ANTS50601.2020.9342778
Teixeira D, Ferreira J, Macedo J (2022) Systematic literature review of AI/ML techniques applied to VANET routing. In: Future of Information and Communication Conference (pp. 339–361). Springer, Cham. https://doi.org/10.1007/978-3-030-98015-3_23
Usama M, Qadir J, Raza A, Arif H, Yau KLA, Elkhatib Y, Al-Fuqaha A (2019) Unsupervised machine learning for networking: Techniques, applications and research challenges. IEEE Access 7:65579–65615. https://doi.org/10.1109/ACCESS.2019.2909530
Visa S, Ramsay B, Ralescu AL, Van Der Knaap E (2011) Confusion matrix-based feature selection. MAICS 710(1):120–127
Wang J, Biljecki F (2022) Unsupervised machine learning in urban studies: A systematic review of applications. Cities 129:103925. https://doi.org/10.1016/j.cities.2022.103925
Xie J, Yu FR, Huang T, Xie R, Liu J, Wang C, Liu Y (2018) A survey of machine learning techniques applied to software defined networking (SDN): Research issues and challenges. IEEE Commun Surv Tutor 21(1):393–430. https://doi.org/10.1109/COMST.2018.2866942
Zhang S, Li X, Zong M, Zhu X, Cheng D (2017) Learning k for knn classification. ACM Trans Int Syst Technol (TIST) 8(3):1–19. https://doi.org/10.1145/2990508
Zhang S, Lagutkina M, Akpinar KO, Akpinar M (2021) Improving performance and data transmission security in VANETs. Comput Commun 180:126–133. https://doi.org/10.1016/j.comcom.2021.09.005
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Sehrawat, P., Chawla, M. Performance Evaluation of Machine Learning Algorithms applied in SD-VANET for Efficient Transmission of Multimedia Information. Multimed Tools Appl 82, 45317–45344 (2023). https://doi.org/10.1007/s11042-023-15244-w
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DOI: https://doi.org/10.1007/s11042-023-15244-w