A new procedure for misbehavior detection in vehicular ad-hoc networks using machine learning

Misbehavior detection in Vehicular Ad hoc Networks (VANETs) is performed to improve the traffic safety and driving accuracy. All the nodes in the VANETs communicate to each other through message logs. Malicious nodes in the VANETs can cause inevitable situation by sending message logs with tampered values. In this work, various machine learning algorithms are used to detect the primarily five types of attacks namely, constant attack, constant offset attack, random attack, random offset attack, and eventual attack. Firstly, each attack is detected by different machine learning algorithms using binary classification. Then, the new procedure is created to do the multi classification of the attacks on best chosen algorithm from different machine learning techniques. The highest accuracy in case of binary classification is obtained with Naive bayes (100%), Decision tree (100%), and Random Forest (100%) in type1 attack, Decision Tree (100%) in type2 attack, and Random Forest (98.03%, 95.56%, and 95.55%) in Type4, Type8 and Type16 attack respectively. In case of new procedure for multi-classification, the highest accuracy is obtained with Random Forest (97.62%) technique. For this work, VeReMi dataset (a public repository for the malicious node detection in VANETs) is used.

[1]  Sherali Zeadally,et al.  Vehicular ad hoc networks (VANETS): status, results, and challenges , 2010, Telecommunication Systems.

[2]  N. Nathani,et al.  Fuzzy Based Malicious Detection Approach For Underwater Ad-Hoc Wireless Network ( UANET ) Ekta , 2018 .

[3]  Vijay Laxmi,et al.  Misbehavior Detection Based on Ensemble Learning in VANET , 2011, ADCONS.

[4]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.

[5]  M. Sarram,et al.  Misbehavior Node Detection in Vehicular ad-hoc Networks: A survey, With Special Emphasis on Multihop Broadcast Protocols , 2017 .

[6]  Vijay Laxmi,et al.  Attack models and infrastructure supported detection mechanisms for position forging attacks in vehicular ad hoc networks , 2013, CSI Transactions on ICT.

[7]  Fuad A. Ghaleb,et al.  An effective misbehavior detection model using artificial neural network for vehicular ad hoc network applications , 2017, 2017 IEEE Conference on Application, Information and Network Security (AINS).

[8]  Sidi-Mohammed Senouci,et al.  Predict and prevent from misbehaving intruders in heterogeneous vehicular networks , 2017, Veh. Commun..

[9]  Ramchandra S. Mangrulkar,et al.  Intrusion Detection System Using Random Forest on the NSL-KDD Dataset , 2019, Emerging Research in Computing, Information, Communication and Applications.

[10]  Suad Mohammed Othman,et al.  Intrusion detection model using machine learning algorithm on Big Data environment , 2018, Journal of Big Data.

[11]  B Ramamurthy,et al.  Mining the Web Data for Classifying and Predicting Users’ Requests , 2018 .

[12]  Soumya K. Ghosh,et al.  Detection of Misbehaving Nodes in Vehicular Ad Hoc Network , 2014 .

[13]  S Raghavendra,et al.  Performance evaluation of random forest with feature selection methods in prediction of diabetes , 2020 .

[14]  Frank Kargl,et al.  VeReMi: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs , 2018, SecureComm.

[15]  Morteza Analoui,et al.  Performance Evaluation of Decision Tree for Intrusion Detection Using Reduced Feature Spaces , 2008 .

[16]  Sanjeev Sharma,et al.  Fuzzy Based Detection of Malicious Activity for Security Assessment of MANET , 2018 .

[17]  I. Muthukumar.  Identifying the Misbehavior Nodes Using Trust Management in VANETs , 2014 .

[18]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[19]  Ana María Monteiro,et al.  Intrusion Detection in Computer Networks Based on KNN, K-Means++ and J48 , 2018, IntelliSys.

[20]  Shuo Shi,et al.  Naive Bayes Classifier Based Driving Habit Prediction Scheme for VANET Stable Clustering , 2020, Mobile Networks and Applications.

[21]  Vibhash Yadav,et al.  Thyroid Disease Prediction Using Machine Learning Approaches , 2020 .

[22]  Fred A. Hamprecht,et al.  End-to-End Learning of Decision Trees and Forests , 2019, International Journal of Computer Vision.

[23]  Priyanka Tiwari,et al.  Security Enhancement of Misbehavior Nodes in Vehicular Ad-Hoc Networks Using Hash Function , 2018 .

[24]  Vijay Laxmi,et al.  Machine Learning Approach for Multiple Misbehavior Detection in VANET , 2011, ACC.