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An Intelligent, Two-Stage, In-Vehicle Diagnostic-Based Secured Framework
Open accessAbstract
Recent research interests have been directed to study the security of vehicles due to the advancement of their technologies. Due to the rapid growth and accelerated development of electronic control units (ECUs), they are countered to be exploited by external attacks. As a result, recent research efforts have been focused on investigating alternative countermeasures that might be implemented by introducing different intrusion detection systems (IDSs). The problem with some of IDSs is the location of their deployment because of the ECU limitations and constraints. Other introduced IDSs require severe changes in the in-vehicle network, which is not preferred by vehicle manufacturers. In this research, we introduce a novel design of a framework to check the state of the vehicle and capture possible attacks by detecting any malicious data in the diagnostic parameters of the vehicle. The framework is divided into two phases: the specific-based detection phase and the anomaly-based detection phase. The proposed system employs the extreme gradient boosting (XGBoost) algorithm to detect anomalies in diagnostic data and it is optimized by a non-dominated sorting genetic algorithm II (NSGA-II). The model is verified against two datasets collected from real vehicles. To generate anomalies in datasets, an attack generation algorithm is introduced. The model is trained on a dataset that contains different attack types and verified blindly against various attacks that have not been seen before. The framework’s experimental results show that it can detect abnormalities with accuracy 97.00% for the Seat Leon 2018 dataset and 97.49% for the KIA SOUL dataset.
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“Because they focus on physical and logical feasibility rather than on specific attack signatures, they can flag previously unseen attacks that nevertheless violate process interlocks, causal relationships, or power-balance constraints [113,114,117,118].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [114]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“First, unlike purely data-driven prediction-residual or reconstruction-error approaches, these methods explicitly encode process constraints, control logic, and admissible state transitions, and treat violations of such rules as primary anomaly cues [111-118].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [114]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“By checking whether actuator commands, sensor responses, and event sequences are physically feasible and logically consistent. they can detect cyber-physical attacks and stealthy manipulations that only slightly perturb statistical patterns but induce clear inconsistencies with process and control laws-such as false data injection, command and control tampering, replay, ramp-style stealth attacks,”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [114]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Several studies therefore emphasize the importance of automatic rule extraction, adaptive updating of operational profiles, and careful coordination between rule-based and ML-based components [113,114,117].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [114]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Finally, the dataset distribution shows that operational-consistency-oriented methods are evaluated across a relatively diverse set of CPS domains, including water-treatment testbeds (SWaT, WADI), vehicle OBD-II datasets, and power-system simulations [111-118].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [114]
The cited source says what the claim attributes to it.
In the citing paper
“On the other side, hybrid frameworks combine these rule-based consistency checks with supervised learners such as decision trees, XGBoost, CNN-LSTM, and DNNs, using operational rules either as a first-stage filter or as additional features to improve the robustness of data-driven classifiers [112-114,117,118].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [114]
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References 32 · Cited by 8
Research works printed but not linked: 7
- Miller, Remote exploitation of an unaltered passenger vehicle, 1
- Wolf, Wanna Drive? Feasible attack paths and effective protection against ransomware in modern vehicles
- 10.3403/30268833u · DOI
- Zhang, A two-stage deep learning approach for can intrusion detection, 1
- 2022, Zonal Architecture: The Foundation for Next Generation Vehicles
- Kwak, 2016, Driving Dataset
- 10.3403/30268833u · DOI
Metadata
- Authors
- Awaad, T. A., El-Kharashi, M. W., Taher, M., & Ammar, K. A.
- Affiliations
- Ain Shams University (EG)
- Ajman University (AE)
- Venue
- IEEE Access
- Published
- 2022
- Volume
- 10
- Pages
- 88907-88919
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4292347966
- Cited by
- 8
- References
- 32
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 2 · 2023: 1
- FWCI
- 0.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Hardware and Architecture › Security and Verification in Computing
- Keywords
- automotive cybersecurity, automotive network security, intrusion detection, electronic control units, anomaly detection, XGBoost, NSGA-II, attack generation, malicious data detection, two-stage detection framework
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- CoChat. (2026). Citation check: An Intelligent, Two-Stage, In-Vehicle Diagnostic-Based Secured Framework. https://app.cochat.ai/papers/10.1109/access.2022.3200375
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- Awaad, T. A., El-Kharashi, M. W., Taher, M., & Ammar, K. A. (2022). An Intelligent, Two-Stage, In-Vehicle Diagnostic-Based Secured Framework. IEEE Access. https://doi.org/10.1109/access.2022.3200375
