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<ArticleSet>
<Article>
<Journal>
				<PublisherName>Shahid Rajaee Teacher Training University</PublisherName>
				<JournalTitle>Journal of Electrical and Computer Engineering Innovations (JECEI)</JournalTitle>
				<Issn>2322-3952</Issn>
				<Volume>14</Volume>
				<Issue>1</Issue>
				<PubDate PubStatus="epublish">
					<Year>2026</Year>
					<Month>07</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Tele-operation Control of a Vehicle During a Cyber Attack</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>99</FirstPage>
			<LastPage>106</LastPage>
			<ELocationID EIdType="pii">2390</ELocationID>
			
<ELocationID EIdType="doi">10.22061/jecei.2025.11922.840</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>Marziyeh</FirstName>
					<LastName>Barootkar</LastName>
<Affiliation>Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2025</Year>
					<Month>05</Month>
					<Day>05</Day>
				</PubDate>
			</History>
		<Abstract>&lt;strong&gt;Background and Objectives: &lt;/strong&gt;While intelligent vehicle teleoperation systems prioritize operational performance, their vulnerability to cyber-physical attacks—such as sensor spoofing and latency exploitation—remains a critical unsolved challenge. Existing solutions predominantly focus on attack prevention, leaving systems defenseless during active attacks that threaten stability and collision avoidance. This study addresses the unmet need for real-time resilience by introducing an adaptive control framework that dynamically mitigates attack-induced disruptions without relying on predefined vehicle models. &lt;br /&gt;&lt;strong&gt;Methods:&lt;/strong&gt; We propose a novel adaptive LQR-based optimal controller that compensates for multi-vector attacks (e.g., false data injection, GPS spoofing) by estimating disturbed signals in real time. Unlike static models, our data-driven approach eliminates dependency on fixed dynamics. A rigorous case study evaluates performance under simultaneous command injection and DoS attacks, measuring trajectory deviation and recovery time. &lt;br /&gt;&lt;strong&gt;Results:&lt;/strong&gt; The framework achieves ≤12% trajectory deviation (35% improvement over benchmarks) and 40% faster recovery from destabilizing attacks. It outperforms conventional controllers by adapting to model uncertainties and multi-vector threats without prior knowledge of system parameters. &lt;br /&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;This work pioneers a model-agnostic, real-time resilience paradigm for teleoperated vehicles, merging human oversight with autonomous adaptability. Beyond immediate safety gains, it underscores the necessity of embedding cybersecurity-aware control mechanisms in connected vehicles, shifting from passive prevention to active threat mitigation.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Cyberattack</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Intelligent Vehicle</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Teleoperation</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Control Strategy</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://jecei.sru.ac.ir/article_2390_1d5d7794d196ba0dd4e4b0d04ef36edd.pdf</ArchiveCopySource>
</Article>
</ArticleSet>
