research-article

Hybrid Algorithm to Detect DDoS Attacks in VANETs

Authors:

Kaushik Adhikary,

Shashi Bhushan,

Kamlesh DuttaAuthors Info & Claims

Wireless Personal Communications, Volume 114, Issue 4

Pages 3613 - 3634

https://doi.org/10.1007/s11277-020-07549-y

Published: 01 October 2020 Publication History

Abstract

Security and safety are fundamental issues in any wireless network. The problem becomes serious when the specified network is Vehicular Adhoc Network (VANET). VANET faces Distributed Denial of Service (DDoS) attacks, when several vehicles carry out various types of Denial of Service (DoS) attacks to disrupt the normal functioning of network, thereby endangering human lives. A highly efficient and reliable algorithm is required to be developed to detect and prevent DDoS attacks in VANET. This paper presents a hybrid detection algorithm based on the SVM kernel methods of AnovaDot and RBFDot for detecting DDoS attacks in VANETs. In this hybrid algorithm, features like collisions, packet drop, jitter etc. have been used to simulate real time network communication scenario where the network is operating under normal conditions, as well as under DDoS attacks. These features are used both for training and for testing the model based on the proposed hybrid algorithm. The performance of the model based on the proposed hybrid algorithm is compared with the models based on single SVM kernel algorithms AnovaDot and RBFDot based on Accuracy, Gini, KS, MER and H. The experimental results show that the model based on the proposed hybrid algorithm is superior to detect DDoS attacks as compared to the models based on single SVM kernel algorithms AnovaDot and RBFDot. The results also prove that by combining the the SVM kernel algorithms, an efficient and effective hybrid algorithm can be developed.

References

[1]

Liang W, Li Z, Zhang H, Wang S, and Bie R Vehicular ad hoc networks: Architectures, research issues, methodologies, challenges, and trends International Journal of Distributed Sensor Networks 2015 2015 745303

[2]

Ayyappan B and Mohan Kumar P Vehicular ad hoc networks (VANET): Architectures, methodologies and design issues 2016 Second International Conference on Science Technology Engineering and Management (ICONSTEM). 2016 2016 177-180

[3]

Bariah, L., Shehada, D., Salahat, E., Yeun, C.Y. (2016). Recent advances in VANET security: A survey. 2015 IEEE 82nd Veh. Technol. Conf. VTC Fall 2015—Proc.

[4]

Al-Sultan S, Al-Doori MM, Al-Bayatti AH, and Zedan H A comprehensive survey on vehicular ad hoc network Journal of Network and Computer Applications 2014 37 1 380-392

[5]

Ho YH and Hua KA Failure-resilient vehicular networks In 35th Annual IEEE Conference on Local Computer Networks, LCN 2010 25 8 336-339

[6]

Saha S, Roy U, and Sinha DD VANET simulation in diffrent indian city scenario Advance in Electronic and Electric Engineering, ISSN 2013 3 9 1221-1228

[7]

Kait R and Chauhan RK Networks on road—challenges in securing vehicular Adhoc networks An International Journal of Engineering Sciences 2011 1 53-61

[8]

Kim S Blockchain for a trust network among intelligent vehicles Advances in Computers 2018 111 43-68

[9]

Bouk SH, Ahmed SH, and Kim D Vehicular content centric network (VCCN): a survey and research challenges Proceedings of the 30th Annual ACM Symposium on Applied Computing 2015 13–17 695-700

[10]

Kelarestaghi, K.B, Foruhandeh, M., Heaslip, K., Gerdes R. (2019). Survey on vehicular ad hoc networks and its access technologies security vulnerabilities and countermeasures (pp. 1–21). Virginia Tech University.

[11]

Kumar S and Dutta K Trust based intrusion detection technique to detect selfish nodes in mobile ad hoc networks Wireless Personal Communications 2018 101 4 2029-2052

[12]

Islabudeen M and Kavitha Devi MK A smart approach for intrusion detection and prevention system in mobile ad hoc networks against security attacks Wireless Personal Communications 2020 112 1 193-224

[13]

Ghazy RA, El-Rabaie ESM, Dessouky MI, El-Fishawy NA, and El-Samie FEA Feature selection ranking and subset-based techniques with different classifiers for intrusion detection Wireless Personal Communications 2020 111 1 375-393

[14]

Ahmed HI, Elfeshawy NA, Elzoghdy SF, El-sayed HS, and Faragallah OS A neural network-based learning algorithm for intrusion detection systems Wireless Personal Communications 2017 97 2 3097-3112

[15]

Verma K, Hasbullah H, and Kumar A Prevention of DoS attacks in VANET Wireless Personal Communications. 2013 73 1 95-126

[16]

Kaur P, Kaur D, and Mahajan R Wormhole attack detection technique in mobile ad hoc networks Wireless Personal Communications 2017 97 2 2939-2950

[17]

Rampaul D, Kumar Patial R, and Kumar D Detection of DoS attack in VANETs Indian Journal of Science and Technology 2016 9 47 1-6

[18]

Shabbir M, Khan MA, Khan US, and Saqib NA Detection and prevention of distributed denial of service attacks in VANETs In 2016 International Conference on Computational Science and Computational Intelligence (CSCI) 2017 2016 970-974

[19]

Khalimonenko O.K.A., Badovskaya, E. DDoS attacks in Q1 2018. Kaspersky

[20]

Zeadally S, Hunt R, Chen YS, Irwin A, and Hassan A Vehicular ad hoc networks (VANETS): Status, results, and challenges Telecommunication Systems 2012 50 4 217-241

[21]

Raya M, Papadimitratos P, Aad I, Jungels D, and Hubaux JP Eviction of misbehaving and faulty nodes in vehicular networks IEEE Journal on Selected Areas in Communications 2007 25 8 1557-1568

[22]

Pathre A, Agrawal C, and Jain A Identification of malicious vehicle in vanet environment from Ddos attack Journal of Global Research in Computer Science 2013 4 6 1-5

[23]

Gandhi, U.D., Keerthana, R.V.S.M. (2014) Request response detection algorithm for detecting DoS attack in VANET. In 2014 International Conference on Reliability Optimization and Information Technology (ICROIT) 192–194.

[24]

Zhou T, Choudhury RR, Ning P, and Chakrabarty K P2DAP & #x2014; sybil attacks detection in vehicular ad hoc networks IEEE Journal on Selected Areas in Communications 2011 29 3 582-594

[25]

Adhikary K and Bhushan S “Recent techniques used for preventing DOS attacks in VANETs Proceeding—IEEE International Conference on Computing, Communication and Automation ICCCA 2017 2017 564-569

[26]

Singh PK, Nandi SK, and Nandi S A tutorial survey on vehicular communication state of the art, and future research directions Vehicular Communications 2019 18 100164

[27]

Sharma S and Kaushik B A survey on internet of vehicles: Applications, security issues and solutions Vehicular Communications 2019 20 100182

[28]

Loukas G, Karapistoli E, Panaousis E, Sarigiannidis P, Bezemskij A, and Vuong T A taxonomy and survey of cyber-physical intrusion detection approaches for vehicles Ad Hoc Networks 2019 84 124-147

[29]

Adhikary K, Bhushan S, and Kumar S Evaluating the performance of various machine learning algorithms for detecting DDOS attacks in vanets International Journal of Control Automation 2019 12 5 478-486

[30]

Agrawal PK, Gupta BB, and Jain S SVM based scheme for predicting number of zombies in a DDoS attack Proceeding 2011 European Intelligence and Security Informatics Conference EISIC 2011 2011 178-182

[31]

De Farias, G.P.M., De Oliveira, A.L.I., Cabral, G.G., Extreme learning machines for intrusion detection systems. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7666 LNCS, no. PART 4, pp. 535–543, 2012.

[32]

Singh R, Kumar H, and Singla RK An intrusion detection system using network traffic profiling and online sequential extreme learning machine Expert Systems with Applications 2015 42 22 8609-8624

[33]

Abusitta A, Bellaiche M, and Dagenais M An SVM-based framework for detecting DoS attacks in virtualized clouds under changing environment Journal of Cloud Computing 2018 7 1 1-18

[34]

Sharma S and Kaul A A survey on Intrusion detection systems and honeypot based proactive security mechanisms in VANETs and VANET Cloud Vehicular Communications 2018 12 April 138-164

[35]

Sakiz F and Sen S A survey of attacks and detection mechanisms on intelligent transportation systems: VANETs and IoV Ad Hoc Networks 2017 61 33-50

[36]

Van Der Heijden RW, Dietzel S, Leinmüller T, and Kargl F Survey on misbehavior detection in cooperative intelligent transportation systems IEEE Communications Surveys and Tutorials 2019 21 1 779-811

[37]

Hui Yang M and Chuan Wang R DDoS detection based on wavelet kernel support vector machine The Journal of China Universities of Posts and Telecommunications 2008 15 3 59-63

[38]

Kale M and Choudhari DM DDOS attack detection based on an ensemble of neural classifier International Journal of Computer Science and Network Security (IJCSNS) 2014 14 7 122

[39]

Sarkar BK and Sana SSA hybrid approach to design efficient learning classifiersComputers and Mathematics with Applications200958165-7325359671189.68099

[40]

Sivatha Sindhu SS, Geetha S, and Kannan A Decision tree based light weight intrusion detection using a wrapper approach Expert Systems with applications 2012 39 1 129-141

[41]

Adhikary K, Bhushan S, Kumar S, and Dutta K Decision tree and neural network based hybrid algorithm for detecting and preventing Ddos attacks in VANETS International Journal of Innovative Technology and Exploring Engineering 2020 5 669-675

[42]

Hosseini S and Azizi M The hybrid technique for DDoS detection with supervised learning algorithms Computer Networks 2019 158 35-45

[43]

Sinha, S., Paul, A. (2020) Neuro-fuzzy based intrusion detection system for wireless sensor network. Wireless Personal Communications 1–17

[44]

Ravale U, Marathe N, and Padiya P Feature selection based hybrid anomaly intrusion detection system using K Means and RBF kernel function Procedia Computer Science 2015 45 428-435

[45]

Sharanya S and Karthikeyan S Classifying malicious nodes in VANETs using support vector machines with modified fading memory ARPN Journal of Engineering and Applied Sciences 2017 12 1 171-176

[46]

Hardy RL Multiquadric equations of topography and other irregular surfaces Journal of Geophysical Research 1971 76 8 1905-1915

[47]

Racine JS RSTUDIO: A platform-independent IDE for R and sweave Journal of Applied Econometrics 2012 27 1 167-172

[48]

Rana PS, Sharma H, Bhattacharya M, and Shukla A Quality assessment of modeled protein structure using physicochemical properties Journal of Bioinformatics and Computational Biology 2015 13 2 1550005

[49]

Hand DJ Measuring classifier performance: A coherent alternative to the area under the ROC curve Machine Learning 2009 77 1 103-123

Cited By

Bahramnejad SMovahhedinia NNaseri A(2024)An LSTM-Based Method for Automatic Reliability Prediction of Cognitive Radio Vehicular Ad Hoc NetworksSN Computer Science10.1007/s42979-024-02603-z5:3Online publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1007/s42979-024-02603-z
Kaur UMahajan AKumar SDutta K(2024)Jellyfish Search Chimp Optimization Enabled Routing and Attack Detection in SDN based VANETsWireless Personal Communications: An International Journal10.1007/s11277-024-11525-1138:2(819-859)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11277-024-11525-1
Wang MMao JZhao WHan XLi MLiao CSun HWang K(2024)Smart City Transportation: A VANET Edge Computing Model to Minimize Latency and Delay Utilizing 5G NetworkJournal of Grid Computing10.1007/s10723-024-09747-522:1Online publication date: 8-Feb-2024
https://dl.acm.org/doi/10.1007/s10723-024-09747-5
Show More Cited By

Recommendations

DDoS attacks and defense mechanisms: classification and state-of-the-art

Denial of Service (DoS) attacks constitute one of the major threats and among the hardest security problems in today's Internet. Of particular concern are Distributed Denial of Service (DDoS) attacks, whose impact can be proportionally severe. With ...
Countering DDoS and XDoS Attacks against Web Services
EUC '08: Proceedings of the 2008 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing - Volume 01

Cyber-criminals use distributed denial-of-service attacks (DDoS) and XML denial-of-service attacks (XDoS) to extort money from online service providers. This kind of attacks is normally targeted at a particular service provider to exhaust the network ...
Simulated Raindrop Algorithm to Mitigate DDoS Attacks in Cloud Computing
ICCCT '15: Proceedings of the Sixth International Conference on Computer and Communication Technology 2015

Distributed Denial of Services (DDoS) Attacks are often the main source of cloud service disruptions. The considerable increase in DDoS attacks strength has drawn attention in detection and mitigation of DDoS attack. Due to use of more powerful botnets, ...

Comments

Information & Contributors

Information

Published In

cover image Wireless Personal Communications: An International Journal

Wireless Personal Communications: An International Journal Volume 114, Issue 4

Oct 2020

865 pages

ISSN:0929-6212

Issue’s Table of Contents

© Springer Science+Business Media, LLC, part of Springer Nature 2020.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 October 2020

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Bahramnejad SMovahhedinia NNaseri A(2024)An LSTM-Based Method for Automatic Reliability Prediction of Cognitive Radio Vehicular Ad Hoc NetworksSN Computer Science10.1007/s42979-024-02603-z5:3Online publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1007/s42979-024-02603-z
Kaur UMahajan AKumar SDutta K(2024)Jellyfish Search Chimp Optimization Enabled Routing and Attack Detection in SDN based VANETsWireless Personal Communications: An International Journal10.1007/s11277-024-11525-1138:2(819-859)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11277-024-11525-1
Wang MMao JZhao WHan XLi MLiao CSun HWang K(2024)Smart City Transportation: A VANET Edge Computing Model to Minimize Latency and Delay Utilizing 5G NetworkJournal of Grid Computing10.1007/s10723-024-09747-522:1Online publication date: 8-Feb-2024
https://dl.acm.org/doi/10.1007/s10723-024-09747-5
Li CLi Z(2024)Trajectory tracking attack for vehicular ad‐hoc networksSecurity and Privacy10.1002/spy2.4337:6Online publication date: 18-Jun-2024
Anyanwu GNwakanma CLee JKim D(2023)RBF-SVM kernel-based model for detecting DDoS attacks in SDN integrated vehicular networkAd Hoc Networks10.1016/j.adhoc.2022.103026140:COnline publication date: 13-Feb-2023
https://dl.acm.org/doi/10.1016/j.adhoc.2022.103026
Abuarqoub AAlzu'bi AHammoudeh MAhmad AAl-Shargabi B(2022)A Survey on Vehicular Ad hoc Networks Security Attacks and CountermeasuresProceedings of the 6th International Conference on Future Networks & Distributed Systems10.1145/3584202.3584309(701-707)Online publication date: 15-Dec-2022
https://dl.acm.org/doi/10.1145/3584202.3584309
Bahramnejad SMovahhedinia N(2022)A reliability estimation framework for cognitive radio V2V communications and an ANN-based model for automating estimationsComputing10.1007/s00607-022-01072-7104:8(1923-1947)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s00607-022-01072-7
Al-Mehdhara MRuan N(2021)MSOMWireless Communications & Mobile Computing10.1155/2021/88917582021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/8891758

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents