• Meesala Pavani
• Madipeddi Sai Manikanta
• Modegama Sindhuja
• Maruru Sujitha

This research paper presents a study on security breach prediction using numerical data. The primary methodology employed is the Artificial Neural Network (ANN) algorithm, which demonstrates its effectiveness in accurately detecting and predicting security breaches based on numerical patterns and indicators. The research explores various preprocessing techniques, including data cleaning, feature selection, normalization, and handling imbalanced data, to ensure the dataset's suitability for breach prediction analysis. Experimental results showcase promising performance, highlighting the potential of numerical data-driven approaches for proactive cybersecurity measures. The findings contribute to the development of more accurate and efficient security breach detection and prevention systems, with future work focusing on advanced feature engineering and the integration of textual and numerical data for enhanced breach prediction models.

deep learning, security breach prediction

"Security breach prediction using Artificial Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.b130-b133, July-2023, Available :http://www.jetir.org/papers/JETIR2307117.pdf

"Security breach prediction using Artificial Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppb130-b133, July-2023, Available at : http://www.jetir.org/papers/JETIR2307117.pdf