Woo, S.,
Jo, H.J.,
Lee, D.H.,
A Practical Wireless Attack on the Connected Car and Security
Protocol for In-Vehicle CAN,
ITS(16), No. 2, April 2015, pp. 993-1006.
IEEE DOI
1504
Automotive engineering
BibRef
Woo, S.,
Jo, H.J.,
Kim, I.S.,
Lee, D.H.,
A Practical Security Architecture for In-Vehicle CAN-FD,
ITS(17), No. 8, August 2016, pp. 2248-2261.
IEEE DOI
1608
Automotive engineering
BibRef
Wu, W.,
Li, R.,
Xie, G.,
An, J.,
Bai, Y.,
Zhou, J.,
Li, K.,
A Survey of Intrusion Detection for In-Vehicle Networks,
ITS(21), No. 3, March 2020, pp. 919-933.
IEEE DOI
2003
Controller area network (CAN), cybersecurity,
in-vehicle network (IVN), intrusion detection system (IDS),
machine learning
BibRef
Aliwa, E.[Emad],
Rana, O.[Omer],
Perera, C.[Charith],
Burnap, P.[Peter],
Cyberattacks and Countermeasures for In-Vehicle Networks,
Surveys(54), No. 1, March 2021, pp. xx-yy.
DOI Link
2104
cybersecurity, CAN bus, intrusion detection systems
BibRef
Xie, G.Q.[Guo-Qi],
Yang, L.T.[Laurence T.],
Wu, W.[Wei],
Zeng, K.Y.[Ke-Yu],
Xiao, X.Z.[Xiang-Zhen],
Li, R.[Renfa],
Security Enhancement for Real-Time Parallel In-Vehicle Applications
by CAN FD Message Authentication,
ITS(22), No. 8, August 2021, pp. 5038-5049.
IEEE DOI
2108
Task analysis, Real-time systems, Bandwidth, Authentication,
Automotive engineering, Automobiles, Authentication,
security
BibRef
He, Y.C.[Yu-Chu],
Jia, Z.J.[Zhi-Juan],
Hu, M.S.[Ming-Sheng],
Cui, C.[Chi],
Cheng, Y.[Yage],
Yang, Y.Y.[Yan-Yan],
The Hybrid Similar Neighborhood Robust Factorization Machine Model
for Can Bus Intrusion Detection in the In-Vehicle Network,
ITS(23), No. 9, September 2022, pp. 16833-16841.
IEEE DOI
2209
Mathematical models, Data models, Intrusion detection,
Predictive models, Robustness, Computational modeling,
factorization machine
BibRef
Duan, X.[Xuting],
Yan, H.[Huiwen],
Tian, D.X.[Da-Xin],
Zhou, J.[Jianshan],
Su, J.[Jian],
Hao, W.[Wei],
In-Vehicle CAN Bus Tampering Attacks Detection for Connected and
Autonomous Vehicles Using an Improved Isolation Forest Method,
ITS(24), No. 2, February 2023, pp. 2122-2134.
IEEE DOI
2302
Anomaly detection, Computer hacking, Support vector machines,
Safety, Encryption, Autonomous vehicles, Authentication, data mass
BibRef
Derhab, A.[Abdelouahid],
Belaoued, M.[Mohamed],
Mohiuddin, I.[Irfan],
Kurniawan, F.[Fajri],
Khan, M.K.[Muhammad Khurram],
Histogram-Based Intrusion Detection and Filtering Framework for
Secure and Safe In-Vehicle Networks,
ITS(23), No. 3, March 2022, pp. 2366-2379.
IEEE DOI
2203
Intrusion detection, Feature extraction, Histograms, Safety,
Filtering, Wireless fidelity, Vehicle-to-everything, OCSVM
BibRef
Wang, K.[Kai],
Zhang, A.[Aiheng],
Sun, H.R.[Hao-Ran],
Wang, B.L.[Bai-Ling],
Analysis of Recent Deep-Learning-Based Intrusion Detection Methods
for In-Vehicle Network,
ITS(24), No. 2, February 2023, pp. 1843-1854.
IEEE DOI
2302
Intrusion detection, Biological system modeling,
Periodic structures, Deep learning, Adaptation models, Security,
vehicular networks
BibRef
Rajapaksha, S.[Sampath],
Kalutarage, H.[Harsha],
Al-Kadri, M.O.[M. Omar],
Petrovski, A.[Andrei],
Madzudzo, G.[Garikayi],
Cheah, M.[Madeline],
AI-Based Intrusion Detection Systems for In-Vehicle Networks:
A Survey,
Surveys(55), No. 11, February 2023, pp. xx-yy.
DOI Link
2303
machine learning, Controller Area Network (CAN),
Intrusion Detection System (IDS), automotive cybersecurity, in-vehicle network
BibRef
Zhang, J.[Jiangjiang],
Gong, B.[Bei],
Waqas, M.[Muhammad],
Tu, S.S.[Shan-Shan],
Chen, S.[Sheng],
Many-Objective Optimization Based Intrusion Detection for in-Vehicle
Network Security,
ITS(24), No. 12, December 2023, pp. 15051-15065.
IEEE DOI
2312
BibRef
Jeong, Y.[Yeonseon],
Kim, H.[Hyunghoon],
Lee, S.[Seyoung],
Choi, W.[Wonsuk],
Lee, D.H.[Dong Hoon],
Jo, H.J.[Hyo Jin],
In-Vehicle Network Intrusion Detection System Using CAN Frame-Aware
Features,
ITS(25), No. 5, May 2024, pp. 3843-3853.
IEEE DOI
2405
Feature extraction, Random forests, Decision trees, Standards,
Fuzzing, Boosting, Vehicles, Controller area network,
machine learning
BibRef
Ali, Z.[Zulfiqar],
Hussain, T.[Tahir],
Su, C.L.[Chun-Lien],
Khan, I.[Irfan],
Jurcut, A.D.[Anca Delia],
Tsao, S.H.[Shao-Hang],
Hu, C.H.[Cho-Han],
Elsisi, M.[Mahmoud],
Deep Learning-Driven Cyber Attack Detection Framework in DC Shipboard
Microgrids System for Enhancing Maritime Transportation Security,
ITS(26), No. 11, November 2025, pp. 20122-20142.
IEEE DOI
2511
Real-time systems, Microgrids, Power system stability,
Computer security, Adaptation models, Vectors, ship microgrid
BibRef
Chapter on New Unsorted Entries, and Other Miscellaneous Papers continues in
Direction of Arrival, DoA, Analysis .