A Novel AI-Based Integrated Cybersecurity Risk Assessment Framework and Resilience of National Critical Infrastructure

Abstract

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.