A Comprehensive Framework for Enhancing Intrusion Detection Systems through Advanced Analytical Techniques
محورهای موضوعی : Pattern RecognitionChetan Gupta 1 , Amit Kumar 2 , Neelesh Kumar Jain 3
1 - Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, India
2 - Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, India
3 - Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, India
کلید واژه: IDS, DOS, XGBOOST, PCA, HIDS, NIDS,
چکیده مقاله :
Intrusion detection systems (IDS) are security technologies that monitor system activity, network traffic, and settings to detect potential threats. IDS provide proactive security management, detecting anomalies and ensuring continuous monitoring. It protects critical assets, such as sensitive data and intellectual property, from unauthorized access or data breaches, preventing downtime and disruption to business operations. In this paper we present a hybrid model based on Principal Component Analysis (PCA) and XGBoost algorithms. To show the effectiveness of the proposed system, various parameters are evaluated on the standard NSL-KDD dataset. First we trained the model using trained dataset and then evaluate the performance the model using testing dataset. In proposed work the we store the data into two-dimensional structure then we standardized and take a most significance features of the data then calculate the covariance matrix, after that calculate the eigenvalues and eigenvectors of the matrix and short in the descending order and using principal component identify the new features and remove the insignificant features. The proposed model outperforms and produces 97.76% accuracy and 94.51% precision; the recall rate is 93.44% and 93.97% F1-Score, which is much better than the previous proposed models. This hybrid approach is better to handle the categorical data and able to find the pattern well and the outcome of the model clearly shows the effectiveness of the proposed system.
Intrusion detection systems (IDS) are security technologies that monitor system activity, network traffic, and settings to detect potential threats. IDS provide proactive security management, detecting anomalies and ensuring continuous monitoring. It protects critical assets, such as sensitive data and intellectual property, from unauthorized access or data breaches, preventing downtime and disruption to business operations. In this paper we present a hybrid model based on Principal Component Analysis (PCA) and XGBoost algorithms. To show the effectiveness of the proposed system, various parameters are evaluated on the standard NSL-KDD dataset. First we trained the model using trained dataset and then evaluate the performance the model using testing dataset. In proposed work the we store the data into two-dimensional structure then we standardized and take a most significance features of the data then calculate the covariance matrix, after that calculate the eigenvalues and eigenvectors of the matrix and short in the descending order and using principal component identify the new features and remove the insignificant features. The proposed model outperforms and produces 97.76% accuracy and 94.51% precision; the recall rate is 93.44% and 93.97% F1-Score, which is much better than the previous proposed models. This hybrid approach is better to handle the categorical data and able to find the pattern well and the outcome of the model clearly shows the effectiveness of the proposed system.
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