A Novel AI-Based Integrated Cybersecurity Risk Assessment Framework and Resilience of National Critical Infrastructure
The modern digital world is experiencing growing security risks, with cyber threats happening more often and becoming more complicated. These threats affect both businesses and individuals. Machine learning (ML) and deep learning (DL) have emerged as vital tools in cybersecurity, enabling the analys...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10819345/ |
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| author | Sardar Muhammad Ali Abdul Razzaque Muhammad Yousaf Sardar Sadaqat Ali |
| author_facet | Sardar Muhammad Ali Abdul Razzaque Muhammad Yousaf Sardar Sadaqat Ali |
| author_sort | Sardar Muhammad Ali |
| collection | DOAJ |
| description | The modern digital world is experiencing growing security risks, with cyber threats happening more often and becoming more complicated. These threats affect both businesses and individuals. Machine learning (ML) and deep learning (DL) have emerged as vital tools in cybersecurity, enabling the analysis of extensive datasets to identify potential cyber threats. This study proposes a novel technique utilizing ML and DL algorithms for threat detection. We began with data preprocessing, which included cleansing the data, addressing missing values through Multiple Imputation by Chained Equations (MICE), and applying transformations such as encoding and standard scaling. To address class imbalance, we employed the Synthetic Minority Over-sampling Technique (SMOTE). For feature selection, we used Forward Feature Elimination (FFE), Backward Feature Elimination (BFE), and Recursive Feature Elimination (RFE) to identify the most relevant features. We trained three ML classifiers: Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbors (KNN), along with three DL models: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). Model performance was evaluated using metrics such as accuracy, precision, recall, and F1 score, alongside loss graphs and confusion matrices. The highest accuracy at 99% was attained by the LSTM model, while the CNN demonstrated superior precision (98%), recall (97%), and F1 score (97%). Additionally, the CNN, RNN, and SVM models achieved an accuracy of 98%. These results illustrate the effectiveness of ML and DL in identifying cybersecurity threats, highlighting their potential to enhance defenses against emerging cyber risks. |
| format | Article |
| id | doaj-art-9a5fddfa5c974dad917c9d49879e61f7 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9a5fddfa5c974dad917c9d49879e61f72025-01-24T00:01:47ZengIEEEIEEE Access2169-35362025-01-0113124271244610.1109/ACCESS.2024.352488410819345A Novel AI-Based Integrated Cybersecurity Risk Assessment Framework and Resilience of National Critical InfrastructureSardar Muhammad Ali0https://orcid.org/0009-0007-4746-4872Abdul Razzaque1Muhammad Yousaf2https://orcid.org/0000-0002-7210-9529Sardar Sadaqat Ali3National University of Sciences and Technology, Islamabad, PakistanNational University of Sciences and Technology, Islamabad, PakistanRiphah Institute of Systems Engineering (RISE), Riphah International University, Islamabad, PakistanKhyber Pakhtunkhwa Information Technology Board (KPITB), Abbottabad, PakistanThe modern digital world is experiencing growing security risks, with cyber threats happening more often and becoming more complicated. These threats affect both businesses and individuals. Machine learning (ML) and deep learning (DL) have emerged as vital tools in cybersecurity, enabling the analysis of extensive datasets to identify potential cyber threats. This study proposes a novel technique utilizing ML and DL algorithms for threat detection. We began with data preprocessing, which included cleansing the data, addressing missing values through Multiple Imputation by Chained Equations (MICE), and applying transformations such as encoding and standard scaling. To address class imbalance, we employed the Synthetic Minority Over-sampling Technique (SMOTE). For feature selection, we used Forward Feature Elimination (FFE), Backward Feature Elimination (BFE), and Recursive Feature Elimination (RFE) to identify the most relevant features. We trained three ML classifiers: Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbors (KNN), along with three DL models: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). Model performance was evaluated using metrics such as accuracy, precision, recall, and F1 score, alongside loss graphs and confusion matrices. The highest accuracy at 99% was attained by the LSTM model, while the CNN demonstrated superior precision (98%), recall (97%), and F1 score (97%). Additionally, the CNN, RNN, and SVM models achieved an accuracy of 98%. These results illustrate the effectiveness of ML and DL in identifying cybersecurity threats, highlighting their potential to enhance defenses against emerging cyber risks.https://ieeexplore.ieee.org/document/10819345/Cybersecuritycritical infrastructuresthreatsrisk analysisMLDL |
| spellingShingle | Sardar Muhammad Ali Abdul Razzaque Muhammad Yousaf Sardar Sadaqat Ali A Novel AI-Based Integrated Cybersecurity Risk Assessment Framework and Resilience of National Critical Infrastructure IEEE Access Cybersecurity critical infrastructures threats risk analysis ML DL |
| title | A Novel AI-Based Integrated Cybersecurity Risk Assessment Framework and Resilience of National Critical Infrastructure |
| title_full | A Novel AI-Based Integrated Cybersecurity Risk Assessment Framework and Resilience of National Critical Infrastructure |
| title_fullStr | A Novel AI-Based Integrated Cybersecurity Risk Assessment Framework and Resilience of National Critical Infrastructure |
| title_full_unstemmed | A Novel AI-Based Integrated Cybersecurity Risk Assessment Framework and Resilience of National Critical Infrastructure |
| title_short | A Novel AI-Based Integrated Cybersecurity Risk Assessment Framework and Resilience of National Critical Infrastructure |
| title_sort | novel ai based integrated cybersecurity risk assessment framework and resilience of national critical infrastructure |
| topic | Cybersecurity critical infrastructures threats risk analysis ML DL |
| url | https://ieeexplore.ieee.org/document/10819345/ |
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