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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Identification of Malicious Injection Attacks in Dense Rating and Co-Visitation Behaviors |
| Authors Name: | Kuthuru Kavyasri , A. Nageswari |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_205695
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| Published Paper Id: | IJRTI2508059 |
| Published In: | Volume 10 Issue 8, August-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i8.205695 |
| Abstract: | In on-line e-commerce Web sites, product reviews formed by the user input have been found to impact on purchasing behavior. But, the rising number of malicious injection-based attacks-light-weight malicious injection attacks-via-high-density rating games and through co-visitation spam actions-endangers the reputation of said platforms. This paper has therefore come up with a new model to combat this challenge, the “Comprehensive study of the spam review detection (CSSRD)” which amalgamates linguistics-based features and behavioural-based features in detecting spam reviews. The number of spam characteristics obtained by the proposed hybrid model is 12, with six belonging to the textual analysis (e.g., sentiment polarity, content repetition) and six to the contextual behavior (e.g., abnormal rating patterns, review frequency). All the reviews are spammed- scored with the mean-based scoring and then they are classified by ML algorithms, such as “Naive Bayes, Decision Tree, Support Vector Machine, and Neural Network”. Empirical findings based on Amazon Datafiniti review dataset indicate that the neural network model is the most accurate with 97 percent accuracy in recognition of both forms of spam pattern as compared to other classifiers. CSSRD model provides an extendable real-time e-commerce detection framework and gives importance to the type of multi-feature fusion in spam detection. |
| Keywords: | spam review detection, machine learning, neural network, linguistic analysis, behavioral analysis, e-commerce security, hybrid model, fake review identification”. |
| Cite Article: | "Identification of Malicious Injection Attacks in Dense Rating and Co-Visitation Behaviors", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 8, page no.a480-a485, August-2025, Available :http://www.ijrti.org/papers/IJRTI2508059.pdf |
| Downloads: | 000842 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2508059
Registration ID:205695
Published In: Volume 10 Issue 8, August-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i8.205695
Page No: a480-a485 Country: -, -, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2508059 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2508059 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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