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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Volume 11 | Issue 3
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
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| Paper Title: | CYBER ATTACK PREDICTION USING MACHINE LEARNING |
| Authors Name: | MOHAMMED SAIFULLAH S , PANDIDURAI D , NAVEEN S , VINOTH S , Mr.Pavun Kumar P |
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IJRTI_206970
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| Published Paper Id: | IJRTI2511083 |
| Published In: | Volume 10 Issue 11, November-2025 |
| DOI: | |
| Abstract: | One of the biggest challenges in today’s recruit ment systems is making sure that candidate resumes match job descriptions. This matching directly affects the quality of hiring decisions and the productivity of the organization. To accomplish this, we propose a supervised finetuning method for semantic resume–job matching that leverages SentenceBERT (SBERT) embeddings to match candidates to job descriptions with high accuracy. Our approach represents both resumes and job descriptions in a shared embedding space. This allows the method to use highquality computation of similarity for the retrieval of topk job match rankings. The model was fine tuned and trained on a labeled dataset of resume–job pairs, and evaluated using Spearman and Pearson correlation coeffi cients to assess agreement with ground truth relevance, with additional metrics of topk retrieval, namely Precision, Recall and Normalized Discounted Cumulative Gain (NDCG). Experi mental results show that the finetuned method outperformed the pretrained baseline, achieving high correlations, precision, and accuracy in identifying relevant candidates. This work demonstrates the use of embedding, along with supervised fine tuning, can improve accuracy and applicability of resumejob matching approaches.The experimental analysis shows that the f inetuned model consistently gets higher performance scores than the pretrained baseline |
| Keywords: | SentenceBERT, Supervised FineTuning, Semantic Embeddings, Spearman Correlation, Pearson Correlation, Similarity Based Ranking |
| Cite Article: | "CYBER ATTACK PREDICTION USING MACHINE LEARNING", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.a705-a709, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511083.pdf |
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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 |
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Published Paper ID: IJRTI2511083
Registration ID:206970
Published In: Volume 10 Issue 11, November-2025
DOI (Digital Object Identifier):
Page No: a705-a709 Country: ERODE, TAMILNADU, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2511083 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2511083 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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