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International Journal for Research Trends and Innovation
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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
Issue per Year : 12
Volume Published : 10
Issue Published : 115
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| Paper Title: | AI-Driven Identification of Cryptographic Algorithms from Encrypted Data |
| Authors Name: | V. Bala Vardhini , M . Naga Manikanta , T. Revanth , V. Ammulu , T. Sushma Latha |
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IJRTI_202604
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| Published Paper Id: | IJRTI2504176 |
| Published In: | Volume 10 Issue 4, April-2025 |
| DOI: | |
| Abstract: | With the rapid rise in digital communication, cryptographic algorithms lie at the core of providing data integrity and protection. Identification of the underlying encryption algorithm from a set of encrypted data is pivotal for assessing vulnerability and the strengthening of cryptographic protocols.In this paper, it is proposed to employ an AI classifier model to identify cryptographic algorithms through the integration of machine learning (ML) and natural language processing (NLP) methods. The approach utilizes ciphertext data transformation through TF-IDF vectorization, cryptographic parameter feature engineering, and the application of robust classifiers like XGBoost and Logistic Regression for multi-class classification. Regularization techniques, hyperparameter adjustment, and explainability models (SHAP/LIME) are used by the approach for improving interpretability. The data set, having several encryption schemes, is treated for feature extraction, encoding, and imputation for improving model robustness. Experimental results reveal ensemble learning approaches dominating traditional techniques by a clear margin in detecting algorithms. The study contributes to the field of cybersecurity analytics and introduces the gateway for automated cryptanalysis and AI-driven cryptographic tests. Experimental results reveal ensemble learning approaches dominating traditional techniques by a clear margin in detecting algorithms, with Random Forest achieving the highest accuracy of 88.88%. Future research directions can involve using deep learning architectures like LSTMs and transformers to increase sequence pattern recognition within ciphertext. |
| Keywords: | Cryptoanalysis, Machine Learning, Ciphertext Classification, Cryptographic Algorithm Identification, Artificial Intelligence In Cybersecurity, Explainable Artificial Intelligence, Feature Engineering. |
| Cite Article: | "AI-Driven Identification of Cryptographic Algorithms from Encrypted Data", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.b613-b630, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504176.pdf |
| Downloads: | 000296 |
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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: IJRTI2504176
Registration ID:202604
Published In: Volume 10 Issue 4, April-2025
DOI (Digital Object Identifier):
Page No: b613-b630 Country: Bhimavaram, Andhra Pradesh, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2504176 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2504176 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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