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PreprintCited by 1

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Detection of Sybil & DDoS Attacks in VANET using Intelligent Technique

Bhanja, Urmila, Majhi, Ankit, Sahoo, Smrutirekha, Parida, Debapriya·2023
DOI 10.21203/rs.3.rs-3187938/v1
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Abstract

Vehicular Adhoc network (VANET) are more prone to various types of attacks. Sybil attack is the most dangerous attack in vehicular adhoc network as it creates multiple fake identities and creates traffic congestion.Fake identities are used to enter the network illegally.On the other hand, distributed denial of service (DDoS) attack intentionally blocks the users from accessing any online services. It temporarily disrupts or interrupts the service of the hosting server. These types of attacks in VANET cause severe damage to vehicles, passengers travelling in the vehicles by inducing traffic congestion, and may also cause minor or fatal accidents.Hence, it is highly essential to early detect such attacks in VANET to protect the vehicles and human kind. In this work, a novel model is proposed using fuzzy logic controllers (FLCs) to detect both the Sybil and the DDoS attacks in VANET. Furthermore, performance of attack detection is also analysed and compared with the existing techniques. The proposed model yields better accuracy, sensitivity, and recall value compared to the existing techniques. Margin of error for the attack detection is also estimated for 95% of the confidence interval.

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Crossref · 10.21203/rs.3.rs-3187938/v1

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Statistics

Figures

Figure 1 Flow chart of Sybil and DDoS attack detection using Fuzzy Logic Controller in VANET
Figure 1 Flow chart of Sybil and DDoS attack detection using Fuzzy Logic Controller in VANETCC-BY · From the PDF
Figure 2 Flow chart of Trust value Evaluation in VANET
Figure 2 Flow chart of Trust value Evaluation in VANETCC-BY · From the PDF
Figure 3 Membership Function of "Packet Loss Ratio"
Figure 3 Membership Function of "Packet Loss Ratio"CC-BY · From the PDF
Figure 4 Membership Function of "SNIR"
Figure 4 Membership Function of "SNIR"CC-BY · From the PDF
Figure 5 Membership Function of "Total Busy Time"
Figure 5 Membership Function of "Total Busy Time"CC-BY · From the PDF
Figure 6 Membership Function of " DDoS Attack detection Status"
Figure 6 Membership Function of " DDoS Attack detection Status"CC-BY · From the PDF
Figure 7 Membership Function of "End to End Delay"
Figure 7 Membership Function of "End to End Delay"CC-BY · From the PDF
Figure 8 Membership Function of "Packet Delivery Ratio"
Figure 8 Membership Function of "Packet Delivery Ratio"CC-BY · From the PDF
Figure 9 Membership Function of "Throughput"
Figure 9 Membership Function of "Throughput"CC-BY · From the PDF
Figure 10 Membership Function of "Sybil Attack detection Status"
Figure 10 Membership Function of "Sybil Attack detection Status"CC-BY · From the PDF
Figure 11 "Fuzzy ruler view of DDoS attack Status for High condition"
Figure 11 "Fuzzy ruler view of DDoS attack Status for High condition"CC-BY · From the PDF
Figure 12 "Fuzzy ruler view of DDoS attack Status for Medium condition"
Figure 12 "Fuzzy ruler view of DDoS attack Status for Medium condition"CC-BY · From the PDF
Figure 13 "Fuzzy ruler view of DDoS attack Status for Low condition"
Figure 13 "Fuzzy ruler view of DDoS attack Status for Low condition"CC-BY · From the PDF
Figure 14 "Fuzzy ruler view of Sybil attack Status for High condition"
Figure 14 "Fuzzy ruler view of Sybil attack Status for High condition"CC-BY · From the PDF
Figure 15 "Fuzzy ruler view of Sybil attack Status for Medium condition"
Figure 15 "Fuzzy ruler view of Sybil attack Status for Medium condition"CC-BY · From the PDF
Figure 16 "Fuzzy ruler view of Sybil attack Status for Low condition"
Figure 16 "Fuzzy ruler view of Sybil attack Status for Low condition"CC-BY · From the PDF
Figure 17 Simulation Scenario using OMNET++ Software
Figure 17 Simulation Scenario using OMNET++ SoftwareCC-BY · From the PDF
Figure 18 Simulation Scenario using OMNET++ Software (zoomed version)
Figure 18 Simulation Scenario using OMNET++ Software (zoomed version)CC-BY · From the PDF
Figure 19 Scatter Plot & Pearson Value for 4 Attackers
Figure 19 Scatter Plot & Pearson Value for 4 AttackersCC-BY · From the PDF
Figure 20 Correlation matrix while keeping receive broadcast constant
Figure 20 Correlation matrix while keeping receive broadcast constantCC-BY · From the PDF
Figure 21 SNIR Lost Packets under 4,6,8,10 DDoS Attackers
Figure 21 SNIR Lost Packets under 4,6,8,10 DDoS AttackersCC-BY · From the PDF
Figure 22 Total Busy Time under under 4,6,8,10 DDoS Attackers
Figure 22 Total Busy Time under under 4,6,8,10 DDoS AttackersCC-BY · From the PDF
Figure 23 Packet Loss Ratio under 4,6,8,10 DDoS Attackers
Figure 23 Packet Loss Ratio under 4,6,8,10 DDoS AttackersCC-BY · From the PDF
Figure 24 End to End Delay for Sybil Attacks [20]
Figure 24 End to End Delay for Sybil Attacks [20]CC-BY · From the PDF
Image 25
CC-BY · From the PDF
Image 26
CC-BY · From the PDF
Figure 27 Performance comparison of DDoS Attack Detection
Figure 27 Performance comparison of DDoS Attack DetectionCC-BY · From the PDF
Figure 29 Comparison of Detection rate for both the DDoS and Sybil Attack
Figure 29 Comparison of Detection rate for both the DDoS and Sybil AttackCC-BY · From the PDF

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Citations

Cites

Show all 1 references

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Metadata

Authors
Bhanja, U., Majhi, A., Sahoo, S., & Parida, D.
Affiliations
  • Professor of ETC, Indira Gandhi Institute of Technology
  • Indira Gandhi Institute of Technology
Publisher
Research Square Platform LLC
Published
2023-07-27
DOI
10.21203/rs.3.rs-3187938/v1
Cited by
0
References
1
Type
Preprint
Licence
creativecommons.org/licenses/by/4.0/ (unspecified)

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CoChat. (2026). Citation check: Detection of Sybil & DDoS Attacks in VANET using Intelligent Technique. https://app.cochat.ai/papers/10.21203/rs.3.rs-3187938/v1
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Bhanja, U., Majhi, A., Sahoo, S., & Parida, D. (2023). Detection of Sybil & DDoS Attacks in VANET using Intelligent Technique. https://doi.org/10.21203/rs.3.rs-3187938/v1

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