International Journal of Computer Networks and Applications

Open Access Open Access  Restricted Access Subscription Access

Ensemble Miscellaneous Classifiers Based Misbehavior Detection Model for Vehicular Ad-Hoc Network Security


Affiliations
1 Department of Information Technology, Bharathiar University, Coimbatore, Tamil Nadu, India
 

Vehicular Ad-Hoc Network is an emerging technology, mainly developed for road safety applications, entertainment applications, and effective traffic conditions. VANET applications work based on the accurate mobility information shared among the vehicles. Sometimes attackers manipulate the mobility information shared by the adjacent vehicle or neighboring vehicle, which results in terrible consequences. To deal with the illusion-based type of attacks, researchers have proposed enormous solutions. Unfortunately, those solutions could not deal with the dynamic vehicle conditions and variable cyber malfunctions, which reduces the misbehavior detection accurateness and increases the false-positive rate. In this paper, the dynamic vehicle context is taken into account to propose a two solutions such as Miscellaneous VANET Classifiers based Misbehavior Detection Model (MVC-MDM) and Ensemble Miscellaneous VANET Classifiers based Misbehavior Detection Model (EMVC-MDM). This model is constructed based on the Mobility Data Gathering phase, Mobility Context Feature Extraction phase, Mobility Context Feature Level Fixing phase, Hampel Filter based Context Reference Building phase, Constructing Miscellaneous VANET Classifiers based Misbehavior Detection model and Ensemble Miscellaneous VANET Classifiers based Misbehavior Detection phase. Vehicle context is prepared using the data-centric features and the behavior-based features of the vehicles. The Nonparametric Hampel filter and Kalman filter are used to building the context reference model. These filters discover the temporal and spatial correlation of the uniformity in the current mobility information. Vehicle features are extracted locally according to the stability, likelihood, and performance of the vehicles' mobility information. A random forest based learning algorithm is used to train and test the classifiers. The proposed MVC-MDM and EMVC-MDM has been simulated in various context scenarios and the presence of misbehaving vehicles. NGSIM dataset has been used for extensive simulation. The results prove that the effectiveness and the reliability of the proposed MVC-MDM and EMVC-MDM are higher than the existing misbehavior detection systems.

Keywords

Stability, Likelihood, Performance, Hampel Filter, Kalman Filter.
User
Notifications
Font Size

  • Lim, Kiho, Kastuv M. Tuladhar, and Hyunbum Kim. Detecting location spoofing using ADAS sensors in VANETs, in 2019 16th IEEE annual consumer communications & networking conference (CCNC), IEEE, 2019, pp. 1-4.

  • H. Vahdat-Nejad, A. Ramazani, T. Mohammadi, and W. Mansoor, A survey on context-aware vehicular network applications, Vehicular Communication, vol. 3, Jan. 2016, pp. 43-57.

  • O. A. Wahab, A. Mourad, H. Otrok, and J. Bentahar, CEAP: SVM based intelligent detection model for clustered vehicular ad hoc networks, Expert System Applications, vol. 50, May 2016, pp. 40-54.

  • R.W. van der Heijden, S. Dietzel, T. Leinmüller, and F. Kargl, Survey on misbehavior detection in cooperative intelligent transportation systems, IEEE Commun. Surveys Tuts., vol. 21, no. 1, 4th Quart, 2018, pp. 779-811.

  • F. Sakiz and S. Sen, A survey of attacks and detection mechanisms on intelligent transportation systems: VANETs and IoV, Ad Hoc Network., vol. 61, Jun. 2017, pp. 33-50.

  • M. N. Mejri, J. Ben-Othman, and M. Hamdi, Survey on VANET security challenges and possible cryptographic solutions, Vehicular Communication, vol. 1, no. 2, Apr. 2014, pp. 53-66.

  • S. Sumithra and R. Vadivel, An overview of various trust models for VANET security establishment, 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, 2018, pp. 1-7.

  • J. Wang, Y. Shao, Y. Ge, and R. Yu, A survey of vehicle to everything (V2X) testing, Sensors, vol. 19, Jan. 2019, pp. 334.

  • R. K. Pearson, Y. Neuvo, J. Astola, and M. Gabbouj, Generalized Hampel filters, EURASIP J. Adv. Signal Process., vol. 2016, pp. 87.

  • U. Khan, S. Agrawal, and S. Silakari, A detailed survey on misbehavior node detection techniques in vehicular ad Hoc networks, in Information Systems Design and Intelligent Applications (Advances in Intelligent Systems and Computing), New Delhi, India: Springer, vol. 339, 2015, pp. 11-19.

  • F. A. Ghaleb, A. Zainal, A. M. Rassam, and F. Saeed, ``Driving-situation aware adaptive broadcasting rate scheme for vehicular adhoc network, Journal of Intelligent Fuzzy Systems, vol. 35, 2018, pp. 423-438.

  • X. Y. Tian, Y. H. Liu, J. Wang, W. W. Deng, and H. Oh, Computational security for context-awareness in vehicular ad-hoc networks, IEEE Access, vol. 4, 2016, pp. 5268-5279.

  • S. Dietzel, J. Petit, G. Heijenk, and F. Kargl, Graph-based metrics for insider attack detection in VANET multihop data dissemination protocols, IEEE Transaction on Vehicular Technology, vol. 62, no. 4, May 2013, pp. 1505-1518.

  • S. Ruj, M. A. Cavenaghi, Z. Huang, A. Nayak, and I. Stojmenovic, On data-centric misbehavior detection in VANETs, in Proc. IEEE Vehicular Technology. Conf. (VTC Fall), Sep. 2011, pp. 1-5.

  • S. Sumithra and R. Vadivel, NB-FTBM model for entity trust evaluation in vehicular ad hoc network security, 2nd International Conference on Ubiquitous Communications and Network Computing, Springer, Cham, 2019, pp. 173-187.

  • N. Bissmeyer, W. Michael, and K. Frank, Misbehavior detection and attacker identification in vehicular ad-hoc networks, Tech. Univ. Darmstadt, Darmstadt, Germany, Tech. Rep., 2014.

  • A Ghaleb, Fuad, Faisal Saeed, Mohammad Al-Sarem, Bander Ali Saleh Al-rimy, Wadii Boulila, A. E. M. Eljialy, Khalid Aloufi, and Ma1111moun Alazab. Misbehavior-aware on-demand collaborative intrusion detection system using distributed ensemble learning for VANET, Electronics, vol.9, no. 9, 2020, pp.1411.

  • H. Stubing, Car-to-X communication: System architecture and applications, in Multilayered Security and Privacy Protection in Car-to-XNetworks. Wiesbaden, Germany: Springer, 2013, pp. 9-19.

  • Zhang, Chunhua, Kangqiang Chen, Xin Zeng, and Xiaoping Xue. "Misbehavior detection based on support vector machine and Dempster-Shafer theory of evidence in VANETs." IEEE Access, vol.6, 2018: 59860-59870.

  • S. Sumithra and R. Vadivel, Optimal Innovation-Based Adaptive Estimation Kalman Filter For Measuring Noise Uncertainty During Vehicle Positioning In VANET, International Journal of Applied Mathematics and Computer Science (AMCS), Vol. 31, No. 1, March 2021.

  • Kamel, Joseph, Mohammad Raashid Ansari, Jonathan Petit, Arnaud Kaiser, Ines Ben Jemaa, and Pascal Urien. "Simulation framework for misbehavior detection in vehicular networks." IEEE transactions on vehicular technology, vol.69, no. 6, 2020, pp.6631-6643.

  • K. Zaidi, M. B. Milojevic, V. Rakocevic, A. Nallanathan, and M. Rajarajan, Host-based intrusion detection for VANETs: A statistical approach to rogue node detection, IEEE Transaction on Vehicular Technology, vol. 65, no. 8, Aug. 2016, pp. 6703-6714.

  • Ghaleb, Fuad A., Anazida Zainal, Murad A. Rassam, and Fathey Mohammed, An effective misbehavior detection model using artificial neural network for vehicular ad hoc network applications, In 2017 IEEE Conference on Application, Information and Network Security (AINS), IEEE, 2017, pp. 13-18.

  • S. A. Soleymani, A. H. Abdullah,W. H. Hassan, M. H. Anisi, S. Goudarzi, and M. A. R. Baee, Trust management in vehicular ad hoc network: A systematic review, EURASIP J. Wireless Communication Network, vol. 1, Dec. 2015, pp. 146.

  • Y. Zhang, L. Lazos, and W. Kozma, AMD: Audit-based misbehavior detection in wireless ad hoc networks, IEEE Transaction on Mobile Computing., vol. 15, no. 8, Aug. 2016, pp. 1893-1907.

  • Dietzel, Stefan, Rens van der Heijden, Hendrik Decke, and Frank Kargl, A flexible, subjective logic-based framework for misbehavior detection in V2V networks, In Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, IEEE, 2014, pp.1-6.

  • N. Lyamin, A. Vinel, M. Jonsson, and B. Bellalta, ``Cooperative awareness in VANETs: On ETSI EN 302 637-2 performance,'' IEEE Transaction on Vehicular Technology, vol. 67, no. 1, Jan. 2018, pp. 17-28.

  • Sakiz, F.; Sen, S, A survey of attacks and detection mechanisms on intelligent transportation systems: VANETs and IoV, Ad Hoc Networks, vol.61, 2017, pp. 33–50.

  • Van der Heijden, R.W.; Stefan, D.; Tim, L.; Frank, K, Survey on misbehavior detection in cooperative intelligent transportation systems, IEEE Communications Surveys & Tutorials, vol.21, no.1, 2019, pp.779–811.

  • Ho, Yao-Hua, Chun-Han Lin, and Ling-Jyh Chen, On-demand misbehavior detection for vehicular ad hoc network, International Journal of Distributed Sensor Networks, vol.12, no. 10, 2016, 1550147716673928.

  • Ghaleb, Fuad A., Mohd Aizaini Maarof, Anazida Zainal, Murad A. Rassam, Faisal Saeed, and Mohammed Alsaedi, Context-aware data-centric misbehavior detection scheme for vehicular ad hoc networks using sequential analysis of the temporal and spatial correlation of the consistency between the cooperative awareness messages, Vehicular Communications, vol.20, 2019, 100186.

  • Erskine, Samuel Kofi, and Khaled M. Elleithy, Real-time detection of DoS attacks in IEEE 802.11 p using fog computing for a secure intelligent vehicular network, Electronics 8, no. 7, 2019, pp.776.


Abstract Views: 414

PDF Views: 1




  • Ensemble Miscellaneous Classifiers Based Misbehavior Detection Model for Vehicular Ad-Hoc Network Security

Abstract Views: 414  |  PDF Views: 1

Authors

S. Sumithra
Department of Information Technology, Bharathiar University, Coimbatore, Tamil Nadu, India
R. Vadivel
Department of Information Technology, Bharathiar University, Coimbatore, Tamil Nadu, India

Abstract


Vehicular Ad-Hoc Network is an emerging technology, mainly developed for road safety applications, entertainment applications, and effective traffic conditions. VANET applications work based on the accurate mobility information shared among the vehicles. Sometimes attackers manipulate the mobility information shared by the adjacent vehicle or neighboring vehicle, which results in terrible consequences. To deal with the illusion-based type of attacks, researchers have proposed enormous solutions. Unfortunately, those solutions could not deal with the dynamic vehicle conditions and variable cyber malfunctions, which reduces the misbehavior detection accurateness and increases the false-positive rate. In this paper, the dynamic vehicle context is taken into account to propose a two solutions such as Miscellaneous VANET Classifiers based Misbehavior Detection Model (MVC-MDM) and Ensemble Miscellaneous VANET Classifiers based Misbehavior Detection Model (EMVC-MDM). This model is constructed based on the Mobility Data Gathering phase, Mobility Context Feature Extraction phase, Mobility Context Feature Level Fixing phase, Hampel Filter based Context Reference Building phase, Constructing Miscellaneous VANET Classifiers based Misbehavior Detection model and Ensemble Miscellaneous VANET Classifiers based Misbehavior Detection phase. Vehicle context is prepared using the data-centric features and the behavior-based features of the vehicles. The Nonparametric Hampel filter and Kalman filter are used to building the context reference model. These filters discover the temporal and spatial correlation of the uniformity in the current mobility information. Vehicle features are extracted locally according to the stability, likelihood, and performance of the vehicles' mobility information. A random forest based learning algorithm is used to train and test the classifiers. The proposed MVC-MDM and EMVC-MDM has been simulated in various context scenarios and the presence of misbehaving vehicles. NGSIM dataset has been used for extensive simulation. The results prove that the effectiveness and the reliability of the proposed MVC-MDM and EMVC-MDM are higher than the existing misbehavior detection systems.

Keywords


Stability, Likelihood, Performance, Hampel Filter, Kalman Filter.

References





DOI: https://doi.org/10.22247/ijcna%2F2021%2F208890