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@article{198515,
        author = {Shaik Mohammed Rizwan and Mohammad Rafeek Khan and Mohiuddin Ali Khan and Mohammad Imran Alam and Huda Fatima and Sarfaraz Ahmed},
        title = {An Enhanced Random Forest-Based Framework for Accurate Intrusion Detection in Modern Network Environments},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {8389-8398},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198515},
        abstract = {As digital infrastructures develop at an alarming rate, cybersecurity threats have also evolved to be more advanced, threatening the contemporary network environments. Conventional Intrusion Detection Systems (IDS), especially signature-based solutions do not identify zero-day attacks and in many cases, they have a high false positive. To overcome these shortcomings, this paper suggests a better Random Forest-based model to make precise and efficient intrusion detection. The proposed model applies the advanced preprocessing techniques, the optimization of feature selection based on the Information Gain and Chi-square method and the optimization of the hyper parameters of the model to enhance the performance of the classification. The framework is tested on benchmark datasets, including CICIDS2017 and NSL-KDD that contain various attack types, and real-world traffic distributions. The experimental findings indicate that the accuracy, precision, recall and F1-score have improved significantly against traditional machine learning models. The suggested system has high detection rates and low false positives, and it is applicable to be deployed in real-time as part of the modern cybersecurity system.},
        keywords = {Intrusion Detection System (IDS), Random Forest, Cybersecurity, Machine Learning, Network Security, Anomaly Detection, Feature Selection.},
        month = {April},
        }