AI-POWERED INTRUSION RESPONSE FOR INTELLIGENT VEHICULAR ECOSYSTEMS

Implementing strict cybersecurity measures to protect against cyber-attacks is absolutely necessary, given the increasing number of intelligent vehicles on the road. This study aims to learn more about the potential for creating an intelligent vehicle-specific autonomous intrusion response system (IRS). The proposed IRS system can instantly assess the consequences of intrusions and ascertain the best methods of response depending on the situation. Among the most significant contributions are a thorough analysis of different response techniques, a system for evaluating costs and impacts dynamically, and the application of various selection algorithms including Simple Additive Weighting (SAW), Linear Programming (LP), game theory, and AI-based procedures. Research has shown that the system works well in terms of response quality, efficiency of time, and consumption of resources. This proves that the technology has the ability to greatly enhance car safety. The findings of this study lay the groundwork for future framework improvements and adaptations by the Internal Revenue Service.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-31
DOI
https://doi.org/10.5281/zenodo.18110689
Primary Topic
IoT and GPS-based Vehicle Safety Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI-POWERED INTRUSION RESPONSE FOR INTELLIGENT VEHICULAR ECOSYSTEMS

Journal of Theoretical and Applied Information Technology
Zenodo (CERN European Organization for Nuclear Research)
IoT and GPS-based Vehicle Safety Systems
article

AI-POWERED INTRUSION RESPONSE FOR INTELLIGENT VEHICULAR ECOSYSTEMS

Journal of Theoretical and Applied Information Technology
article en

Abstract

Implementing strict cybersecurity measures to protect against cyber-attacks is absolutely necessary, given the increasing number of intelligent vehicles on the road. This study aims to learn more about the potential for creating an intelligent vehicle-specific autonomous intrusion response system (IRS). The proposed IRS system can instantly assess the consequences of intrusions and ascertain the best methods of response depending on the situation. Among the most significant contributions are a thorough analysis of different response techniques, a system for evaluating costs and impacts dynamically, and the application of various selection algorithms including Simple Additive Weighting (SAW), Linear Programming (LP), game theory, and AI-based procedures. Research has shown that the system works well in terms of response quality, efficiency of time, and consumption of resources. This proves that the technology has the ability to greatly enhance car safety. The findings of this study lay the groundwork for future framework improvements and adaptations by the Internal Revenue Service.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 43%
IoT and GPS-based Vehicle Safety Systems
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.

AI-POWERED INTRUSION RESPONSE FOR INTELLIGENT VEHICULAR ECOSYSTEMS — Journal of Theoretical and Applied Information Technology · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS