Page 828 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

4 Sequence Diagram

5 References

[1] Khalil, Abizar, Haleem Farman, Moustafa M. Nasralla, Bilal Jan, and Jamil Ahmad. "Artificial
Intelligence-Based Intrusion Detection System for V2V Communication in Vehicular
Adhoc Networks." Ain Shams Engineering Journal 15, no. 4 (2024): 102616. https:// doi
j
.org/ 10 .1016/ .asej .2023 .102616.
[2] Rajapaksha, Sampath, Harsha Kalutarage, M. Omar Al-Kadri, Andrei Petrovski, Garikayi
Madzudzo, and Madeline Cheah. "AI-Based Intrusion Detection Systems for In-Vehicle
Networks: A Survey." ACM Computing Surveys 55, no. 11 (November 2023): Article 237.
https:// doi .org/ 10 .1145/ 3570954.
[3] Sowmya, T., and E. A. Mary Anita. "A Comprehensive Review of AI-Based Intrusion
Detection System." Measurement: Sensors 28 (2023): 100827. https:// doi .org/ 10 .1016/ j
.measen .2023 .100827.
[4] Guerra, Lorenzo, Linhan Xu, Paolo Bellavista, Thomas Chapuis, Guillaume Duc, Pavlo
Mozharovskyi, and Van-Tam Nguyen. "AI-Driven Intrusion Detection Systems (IDS) on the
ROAD Dataset: A Comparative Analysis for Automotive Controller Area Network (CAN)."
In Proceedings of the 2024 Cyber Security in CarS Workshop (CSCS '24), 39–49. New York:
Association for Computing Machinery, 2024. https:// doi .org/ 10 .1145/ 3689936 .3694696.
[5] AI4CE. V2X-Sim. GitHub. Accessed June 19, 2025. https:// github .com/ ai4ce/ V2X -Sim.
[6] Chethuhn. Network Intrusion Dataset. Kaggle. Accessed June 19, 2025. https:// www
.kaggle .com/ datasets/ chethuhn/ network -intrusion -dataset.

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