MS / MPhil

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Browsing MS / MPhil by Author "Hina Jabbar"

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    AI Based Cyber Attacks Detection Model for IoT Networks
    (UMT, Lahore, 2025) Hina Jabbar
    With the Passage of time adoption of IoT continues rise the threat of cyber-attacks is also growing. It demanding the effective and accurate mechanism for detection. Traditional cyber attack detection mechanisms often suffer from the imbalanced attacks classification, underutilization of the datasets and high false negative rates especially for the minority attack categories. These limitations decrease the ability of models to detect less frequent but most critical types of attacks, compromising cybersecurity. This study addresses these challenges by familiarizes an optimized model based on the Gradient Boosting for cyber attacks detection. The model is design to enhance the minority attacks classes while maintaining the overall accuracy. To attain this, we employ CICIoT2023 dataset, utilizing its large-scale structure to ensure the comprehensive model training and robust generalization of it. The evaluation of large-scale dataset provides better generalization of model that improved the representation of the real-world pattern of attack and it help to reduce bias. It allowing the model to make classification more accurately.so, various preprocessing techniques including data resampling and dimensionality reduction applied to model learning and address the class imbalance challenge. However, the resampling methods help balance the classes of dataset but lead to overfitting, generate artificial pattern and decrease ability of the model to generalize to unseen the attacks. We introduce multiple variants of the Gradient Boosting model through tunning of hyperparameter. One optimized variant GB_10D4 demonstrating best performance both for binary and multiclassification.