Scipediacontent: Scipediacontent moved page Draft Content 549311457 to 238,295hu

2021-03-22T21:15:06Z

Scipediacontent moved page Draft Content 549311457 to 238,295hu

Scipediacontent: Created page with " == Abstract == The recent trend for vehicles to be connected to unspecified devices, vehicles, and infrastructure increases the potential for external threats to vehicle cyb..."

2021-03-22T21:14:07Z

Created page with " == Abstract == The recent trend for vehicles to be connected to unspecified devices, vehicles, and infrastructure increases the potential for external threats to vehicle cyb..."

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== Abstract ==

The recent trend for vehicles to be connected to unspecified devices, vehicles, and infrastructure increases the potential for external threats to vehicle cybersecurity. Thus, intrusion detection is a key network security function in vehicles with open connectivity, such as self-driving and connected cars. Specifically, when a vehicle is connected to an external device through a smartphone inside the vehicle or when a vehicle communicates with external infrastructure, security technology is required to protect the software network inside the vehicle. Existing technology with this function includes vehicle gateways and intrusion detection systems. However, it is difficult to block malicious code based on application behaviors. In this study, we propose a machine learning-based data analysis method to accurately detect abnormal behaviors due to malware in large-scale network traffic in real time. First, we define a detection architecture, which is required by the intrusion detection module to detect and block malware attempting to affect the vehicle via a smartphone. Then, we propose an efficient algorithm for detecting malicious behaviors in a network environment and conduct experiments to verify algorithm accuracy and cost through comparisons with other algorithms.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_549311457-beopen1228-1041-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.1155/2020/3035741 http://dx.doi.org/10.1155/2020/3035741] under the license http://creativecommons.org/licenses/by/4.0/

* [https://doi.org/10.1155/2020/3035741 https://doi.org/10.1155/2020/3035741] under the license http://creativecommons.org/licenses/by/4.0

* [http://downloads.hindawi.com/journals/jat/2020/3035741.pdf http://downloads.hindawi.com/journals/jat/2020/3035741.pdf],
: [http://downloads.hindawi.com/journals/jat/2020/3035741.xml http://downloads.hindawi.com/journals/jat/2020/3035741.xml],
: [http://dx.doi.org/10.1155/2020/3035741 http://dx.doi.org/10.1155/2020/3035741] under the license cc-by

* [http://dx.doi.org/10.1155/2020/3035741 http://dx.doi.org/10.1155/2020/3035741],
: [https://doaj.org/toc/0197-6729 https://doaj.org/toc/0197-6729],
: [https://doaj.org/toc/2042-3195 https://doaj.org/toc/2042-3195] under the license http://creativecommons.org/licenses/by/4.0/

* [https://www.hindawi.com/journals/jat/2020/3035741 https://www.hindawi.com/journals/jat/2020/3035741],
: [http://downloads.hindawi.com/journals/jat/2020/3035741.pdf http://downloads.hindawi.com/journals/jat/2020/3035741.pdf],
: [https://koreauniv.pure.elsevier.com/en/publications/malware-detection-in-self-driving-vehicles-using-machine-learning https://koreauniv.pure.elsevier.com/en/publications/malware-detection-in-self-driving-vehicles-using-machine-learning],
: [https://www.safetylit.org/citations/index.php?fuseaction=citations.viewdetails&citationIds[]=citjournalarticle_642796_19 https://www.safetylit.org/citations/index.php?fuseaction=citations.viewdetails&citationIds[]=citjournalarticle_642796_19],
: [https://academic.microsoft.com/#/detail/3000425587 https://academic.microsoft.com/#/detail/3000425587]

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Scipediacontent: Scipediacontent moved page Draft Content 549311457 to 238,295hu

Scipediacontent: Created page with " == Abstract == The recent trend for vehicles to be connected to unspecified devices, vehicles, and infrastructure increases the potential for external threats to vehicle cyb..."