International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | A Machine Learning - Based Intrusion Detection and Forecasting System |
|---|---|
| ABSTRACT | The increasing sophistication of cyberattacks demands intelligent systems capable of both detecting ongoing intrusions and anticipating future threats. This paper presents a Machine Learning-Based Intrusion Detection and Forecasting System that integrates intrusion detection with attack trend prediction using the UNSW-NB15 dataset. The proposed framework applies data preprocessing techniques, including data cleaning, label encoding, normalization, and train-test splitting, before training Random Forest, Gradient Boosting, and Deep Neural Network (DNN) models for intrusion detection. To enable proactive cybersecurity, Linear Regression and Long Short-Term Memory (LSTM) models are employed to forecast future attack trends based on historical attack data. Experimental results demonstrate that the proposed detection models achieve high classification performance, while the forecasting models accurately predict attack patterns with low MAE and RMSE values. An interactive Streamlit dashboard is developed to visualize detection results, model performance, and forecasted cyber-attack trends, providing an effective decision-support tool for network security administrators. |
| AUTHOR | GANNABATHULA SAI KANTH PUSHKAR, DR. V. UMA RANI Post-Graduate Student, Department of Computer Science Engineering, Computer Networks and Information Security, Jawaharlal Nehru Technological University, Hyderabad, India Professor, Department of Computer Science Engineering, Jawaharlal Nehru Technological University, Hyderabad, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407029 |
| pdf/29_A Machine Learning - Based Intrusion Detection and Forecasting System.pdf | |
| KEYWORDS | |
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