Forecasting Models on Cyber Attacks and Control Measures

Registration ID: IJNRD_327320

Published ID: IJNRD2607316

: https://doi.org/10.56975/ijnrd.v11i7.327320

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Mr. Shridhar S. Kharade, Mr. Rajwardhan S. Todkar

Cyber Security, Threat Prediction, Time Series Analysis, Machine Learning, Predictive Analytics.

The threats in the cyber world have matured at a rapid pace and have become an important threat to Cyber-Physical Systems (CPS) that combine computational intelligence and physical processes from the real world. Traditionally, cyber security has been a reactive subject and only focused on post attack scenarios, reducing its ability to cope with present, complex threats. To overcome this limitation, there is a need for proactive and predictive security mechanisms, which will be able to detect potential cyber-attack patterns before they harm. In this project, the aim is to predict cyber threats using secondary datasets, which are structured data (e.g. CSV files, databases) but can be converted to time-series data to record temporal attack behaviors. It has a comprehensive preprocessing pipeline that involves cleaning, normalizing, and selecting relevant features, ensuring that the input data is of high quality and relevance. The predictive model used is the Random Forest algorithm, which is chosen for its reliability and robustness in dealing with complex datasets, using ensemble methods such as bootstrap sampling and randomness in features. Evaluation of the model performance is done through measures including accuracy, precision, recall, F1-score and confusion matrix analysis. The overall architecture encompasses the integration of data collection, preprocessing, model training, model evaluation, and user interface, creating a comprehensive and efficient framework for predictive cyber threat analysis. The suggested framework allows users to enter new data and receive immediate forecasts of possible cyber attacks. In this study, comparison is made between statistical and machine learning methods and ensemble methods are found to be more effective in the context of cybersecurity forecasting. The findings illustrate how predictor models based on data can be useful in improving the proactivity defense mechanisms in CPS. Moreover, this research will serve as a foundation for future studies on graph-based threat modeling and advanced AI systems for cybersecurity.

Published Paper   E-Certificate

Mr. Shridhar S. Kharade & Mr. Rajwardhan S. Todkar (July-2026). Forecasting Models on Cyber Attacks and Control Measures. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(7), d140-d150. https://doi.org/10.56975/ijnrd.v11i7.327320

Volume 11 Issue 7, July-2026

Pages : d140-d150

Paper Reg. ID: IJNRD_327320

Published Paper Id: IJNRD2607316

Research Area: Other area not in list

Author Type: Indian Author

Country: Kolhapur, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2607316.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2607316

Crossref DOI: https://doi.org/10.56975/ijnrd.v11i7.327320

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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