Mobile QR Code QR CODE

REFERENCES

1 
S. Kudva, S. Badsha, S. Sengupta, H. La, I. Khalil, M. Atiquzzaman, ``A scalable blockchain based trust management in VANET routing protocol'', Journal Of Parallel And Distributed Computing. vol. 152, pp. 144-156, Jun. 2021.DOI
2 
F.H. Shajin, B. Aruna Devi, N.B. Prakash, G.R. Sreekanth, P. Rajesh, Sailfish optimizer with Levy flight, chaotic and opposition-based multi-level thresholding for medical image segmentation. Soft Computing. pp. 1-26, Apr. 2023.DOI
3 
P. Remya krishnan, P. Arun Raj Kumar, ``Detection and Mitigation of Smart Blackhole and Gray Hole Attacks in VANET Using Dynamic Time Warping'', Wireless Personal Communications. vol. 124, pp. 931-966, May. 2021.DOI
4 
F.H. Shajin, P. Rajesh, M.R. Raja. An efficient VLSI architecture for fast motion estimation exploiting zero motion prejudgment technique and a new quadrant-based search algorithm in HEVC. Circuits, Systems, and Signal Processing. pp. 1-24, Mar. 2022.DOI
5 
G. Soni, K. Chandravanshi, ``A Novel Privacy-Preserving and Denser Traffic Management System in 6G-VANET Routing Against Black Hole Attack'', Sustainable Communication Networks And Application. pp. 649-663, Jan. 2022.DOI
6 
P. Rajesh, F. Shajin. A multi-objective hybrid algorithm for planning electrical distribution system. European Journal of Electrical Engineering. vol. 22, no. 4-5, pp. 224-509, Dec.2020.DOI
7 
P. Rajesh, R. Kannan, J. Vishnupriyan, B. Rajani. Optimally detecting and classifying the transmission line fault in power system using hybrid technique. ISA transactions. vol. 130, pp. 253-64, Nov. 2022.DOI
8 
G. Soni, K. Chandravanshi, M. Jhariya, A. Rajput, ``An IPS Approach to Secure V-RSU Communication from Blackhole and Wormhole Attacks in VANET'', Lecture Notes In Networks And Systems. pp. 57-65, Dec. 2021.DOI
9 
P. Shah, T. Kasbe, ``Detecting Sybil Attack, Black Hole Attack and DoS Attack in VANET Using RSA Algorithm'', 2021 Emerging Trends In Industry 4.0 (ETI 4.0), May. 2021.DOI
10 
A. Kumar, N. Sharma, A. Kumar, ``Improving security and privacy in VANET network using node authentication'', Journal Of Discrete Mathematical Sciences And Cryptography. vol. 24, pp. 2471-2480, Nov. 2021.DOI
11 
S. Ercan, M. Ayaida, N. Messai, ``Misbehavior Detection for Position Falsification Attacks in VANETs Using Machine Learning'', IEEE Access. vol. 10, pp. 1893-1904, Dec. 2022.DOI
12 
S. Sharma, A. Kaul, S. Ahmed, S. Sharma, ``A detailed tutorial survey on VANETs: Emerging architectures, applications, security issues, and solutions'', International Journal Of Communication Systems. vol. 34, Sep. 2021.DOI
13 
J. Shu, L. Zhou, W. Zhang, X. Du, M. Guizani, ``Collaborative Intrusion Detection for VANETs: A Deep Learning-Based Distributed SDN Approach'', IEEE Transactions On Intelligent Transportation Systems. vol. 22, pp. 4519-4530, Oct. 2021.DOI
14 
T. Alladi, B. Gera, A. Agrawal, V. Chamola, F. Yu, ``DeepADV: A Deep Neural Network Framework for Anomaly Detection in VANETs'', IEEE Transactions On Vehicular Technology. vol. 70, pp. 12013-12023, Sep. 2021.DOI
15 
H. Bangui, M. Ge, B. Buhnova, ``A hybrid machine learning model for intrusion detection in VANET'', Computing. vol. 104, pp. 503-531, Mar. 2021.DOI
16 
R. Dhanaraj, S. Islam, V. Rajasekar, ``A cryptographic paradigm to detect and mitigate blackhole attack in VANET environments'', Wireless Networks, Oct. 2022.DOI
17 
A. Kumar, V. Varadarajan, A. Kumar, P. Dadheech, S.S. Choudhary, V.A. Kumar, B. K. Panigrahi, K.C. Veluvolu, ``Black hole attack detection in vehicular ad-hoc network using secure AODV routing algorithm'', Microprocessors And Microsystems. vol. 80, pp. 103352, Feb. 2021.DOI