Synthetic Attack Dataset Generation with ID2T for AI-Based Intrusion Detection in Industrial V2I Network

Prinkle Sharma; Jaiganesh Anandan; Hong Liu; Jyoti Grover

doi:10.1109/OJVT.2025.3609149

Synthetic Attack Dataset Generation with ID2T for AI-Based Intrusion Detection in Industrial V2I Network

Prinkle Sharma, Jaiganesh Anandan, Hong Liu Jyoti Grover

IEEE open journal of vehicular technology, Vol.6, pp.1-31

09/10/2025

: https://doi.org/10.1109/OJVT.2025.3609149

Artificial intelligence

Benchmark testing

Computer security

Intrusion detection

Intrusion Detection Dataset Toolkit (ID2T)

intrusion detection system

network security

Real-time systems

Security

Synthetic data

Synthetic dataset

Telecommunication traffic

Training

V2I communication

Vehicle-to-infrastructure

wireless network

Industrial Vehicle-to-Infrastructure (iV2I) networks are increasingly adopted in settings such as warehouses, construction sites, and smart factories to enhance automation and operational efficiency. However, these systems face growing cybersecurity risks that threaten safety-critical operations. This paper introduces a realistic synthetic dataset created using the ID2T framework, which injects malicious traffic, such as DDoS, PortScan, and memory corruption exploits, into benign communication traces collected from actual iV2I environments. The resulting hybrid dataset, combining synthetic and real-world traffic, enables the supervised training of a Multi-Layer Perceptron (MLP) neural network using 16 meticulously crafted flow-based features. Experimental results demonstrate high detection accuracy under both balanced and threat-specific conditions, validating the effectiveness of ID2T in modeling domain-relevant cyberattack behaviors. In addition to strong classification performance, this work demonstrates how synthetic malicious traffic generation reduces the cost and complexity of cyberattack emulation. The proposed method offers a scalable and reproducible framework for training intrusion detection systems (IDS), highlighting the critical role of Artificial Intelligence (AI) in securing next-generation industrial vehicular networks.

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url

https://doi.org/10.1109/OJVT.2025.3609149

Published (Version of record)

: Synthetic Attack Dataset Generation with ID2T for AI-Based Intrusion Detection in Industrial V2I Network
: Prinkle Sharma - University at Albany, State University of New York
Jaiganesh Anandan - University at Albany, State University of New York
Hong Liu - University of Massachusetts Dartmouth
Jyoti Grover - Malaviya National Institute of Technology Jaipur
: IEEE open journal of vehicular technology, Vol.6, pp.1-31
: IEEE; PISCATAWAY
: 31
: State University of New York - University at Albany
This work was supported by the State University of New York - University at Albany.
: Department of Electrical and Computer Engineering
: English
: Journal article
: https://doi.org/10.1109/OJVT.2025.3609149
: 9914495964501301