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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: | Anomaly Detection in Car Booking System |
| Authors Name: | Chetan Santosh Sahu , Nurussaba Sayyad , Girish Rakshit , Mayur Atalkar , Rahul Bambodkar |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_215405
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| Published Paper Id: | IJNRD2404209 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | The escalating reliance on digital platforms for car booking services necessitates the development of robust anomaly detection mechanisms to ensure system security and reliability. This research is dedicated to crafting and deploying advanced anomaly detection techniques tailored to the distinctive characteristics of car booking systems. Utilizing machine learning algorithms and data analytics, the study targets the identification and mitigation of anomalous activities, including fraudulent transactions, unauthorized access, and system malfunctions. The research methodology involves the analysis of historical booking data to establish baseline behavior patterns, employing anomaly detection models to discern deviations from the norm. Special consideration is given to challenges inherent in the car booking domain, such as dynamic user behavior, fluctuating demand patterns, and diverse geographical factors. The proposed anomaly detection framework is designed for adaptability and evolution over time, ensuring effectiveness in detecting emerging threats and evolving attack vectors. Through the implementation of this system within existing car booking platforms, the research aims to fortify the overall security posture, safeguarding user data, financial transactions, and system integrity. The anticipated outcomes promise valuable contributions to the broader field of anomaly detection in digital service ecosystems, fostering a more secure and reliable environment for users and service providers alike. |
| Keywords: | Anomaly detection, Car booking system, Security, Reliability, Machine learning algorithms, Data analytics, Fraudulent transactions, Unauthorized access, System malfunctions, Fluctuating demand patterns, Geographical factors, Financial transactions security |
| Cite Article: | "Anomaly Detection in Car Booking System ", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.b897-b901, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404209.pdf |
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| 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:IJNRD2404209 Registration ID: 215405 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: b897-b901 Country: Nagpur , Maharashtra , India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404209 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404209 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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