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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Comparative Analysis of Intrusion Detection Models on Internet of Vehicles Using TensorFlow Neural Network Classifiers. |
| Authors Name: | Abhishek Sebastian , Pragna R |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_184147
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| Published Paper Id: | IJNRD2211143 |
| Published In: | Volume 7 Issue 11, November-2022 |
| DOI: | http://doi.one/10.1729/Journal.32163 |
| Abstract: | IoV is anticipated to be one of the Future Mobility's ACES (autonomous, connected, electric, and shared) enablers. Security is usually the most crucial concern in the development of IOV since frequent data transmission and intricate connections among many different nodes make hostile attacks more sophisticated and diverse. Many decisions in the IOV architecture are based on artificial intelligence, which is computed utilizing big data and data mining. The attacker's decision could have devastating consequences if they are successful in carrying out a malicious attack and taking complete control of the vehicle. In order to create the best model to identify intrusions from the dataset, various model variations were trained using various optimizers. The best model developed from this study therefore has an accuracy of 99.999897% in detecting typical scenarios, 99.27548394% in detecting generic attacks, and 92.0736% in detecting exploits. Researchers are becoming more interested in intrusion detection as a technique that effectively guards the security of IOV by continuously monitoring network data flow, such as network traffic, connections, objects, etc. Systems called intrusion selection (IDS) can spot potentially dangerous substances and threats. |
| Keywords: | IoV, Intrusion Detection, Deep-learning. |
| Cite Article: | "Comparative Analysis of Intrusion Detection Models on Internet of Vehicles Using TensorFlow Neural Network Classifiers.", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.7, Issue 11, page no.b371-b378, November-2022, Available :http://www.ijnrd.org/papers/IJNRD2211143.pdf |
| Downloads: | 000118750 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2211143 Registration ID: 184147 Published In: Volume 7 Issue 11, November-2022 DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.32163 Page No: b371-b378 Country: Chennai, TN, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2211143 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2211143 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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