Mitigating Cybersecurity Risks in Automotive Embedded Systems Through AI-Driven Intrusion Detection and Secure Communication Protocols

The rapid digitization of the automotive industry has ushered in advanced connectivity features, autonomous functionalities, and data-driven decision-making processes.However, this technological transformation has also introduced significant cybersecurity vulnerabilities within automotive embedded systems, where traditional security frameworks struggle to address evolving threats.Cyberattacks targeting vehicle control units, in-vehicle networks, and external communication interfaces can compromise passenger safety, disrupt mobility services, and undermine consumer trust.A broader understanding of these risks highlights the urgent need for robust defense mechanisms capable of adapting to sophisticated intrusion attempts.Artificial intelligence (AI)-driven intrusion detection systems (IDS) present a promising approach, leveraging machine learning and deep learning techniques to identify anomalous patterns in vehicular data streams in real time.These systems not only enhance detection accuracy but also enable adaptive responses to previously unseen attack vectors.Narrowing the focus further, secure communication protocols, including cryptographic authentication and lightweight encryption schemes, play a pivotal role in safeguarding data exchange across in-vehicle networks and vehicle-to-everything (V2X) infrastructures.Integrating AI-driven IDS with resilient communication frameworks offers a layered security model that strengthens embedded system resilience against cyber threats while ensuring compliance with stringent automotive safety standards such as ISO/SAE 21434.This paper emphasizes the synergy of AI-based threat detection with secure communication architectures as a comprehensive strategy to mitigate cybersecurity risks.By aligning technological innovations with regulatory and industry practices, the automotive sector can address emerging challenges proactively, ensuring the safe deployment of connected and autonomous vehicles in an increasingly hostile cyber environment.

Publication Details

Journal
International Journal of Science and Engineering Applications
Published
2025-09-22
DOI
https://doi.org/10.7753/ijsea1410.1003
Primary Topic
Vehicular Ad Hoc Networks (VANETs)
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mitigating Cybersecurity Risks in Automotive Embedded Systems Through AI-Driven Intrusion Detection and Secure Communication Protocols

International Journal of Science and Engineering Applications
Vehicular Ad Hoc Networks (VANETs)
article

Mitigating Cybersecurity Risks in Automotive Embedded Systems Through AI-Driven Intrusion Detection and Secure Communication Protocols

article en

Abstract

The rapid digitization of the automotive industry has ushered in advanced connectivity features, autonomous functionalities, and data-driven decision-making processes.However, this technological transformation has also introduced significant cybersecurity vulnerabilities within automotive embedded systems, where traditional security frameworks struggle to address evolving threats.Cyberattacks targeting vehicle control units, in-vehicle networks, and external communication interfaces can compromise passenger safety, disrupt mobility services, and undermine consumer trust.A broader understanding of these risks highlights the urgent need for robust defense mechanisms capable of adapting to sophisticated intrusion attempts.Artificial intelligence (AI)-driven intrusion detection systems (IDS) present a promising approach, leveraging machine learning and deep learning techniques to identify anomalous patterns in vehicular data streams in real time.These systems not only enhance detection accuracy but also enable adaptive responses to previously unseen attack vectors.Narrowing the focus further, secure communication protocols, including cryptographic authentication and lightweight encryption schemes, play a pivotal role in safeguarding data exchange across in-vehicle networks and vehicle-to-everything (V2X) infrastructures.Integrating AI-driven IDS with resilient communication frameworks offers a layered security model that strengthens embedded system resilience against cyber threats while ensuring compliance with stringent automotive safety standards such as ISO/SAE 21434.This paper emphasizes the synergy of AI-based threat detection with secure communication architectures as a comprehensive strategy to mitigate cybersecurity risks.By aligning technological innovations with regulatory and industry practices, the automotive sector can address emerging challenges proactively, ensuring the safe deployment of connected and autonomous vehicles in an increasingly hostile cyber environment.

International Journal of Science and Engineering Applications
Openalex Percentile: Top 75%
Vehicular Ad Hoc Networks (VANETs)
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Mitigating Cybersecurity Risks in Automotive Embedded Systems Through AI-Driven Intrusion Detection and Secure Communication Protocols · International Journal of Science and Engineering Applications (2025) | TGRS Research Map | TGRS