A Comparison of Re-Sampling Techniques for Detection of Multi-Step Attacks on Deep Learning Models

A Comparison of Re-Sampling Techniques for Detection of Multi-Step Attacks on Deep Learning Models

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The increasing dependence on data analytics Adjustment Tube Set;Pedal Arm and artificial intelligence (AI) methodologies across various domains has prompted the emergence of apprehensions over data security and integrity.There exists a consensus among scholars and experts that the identification and mitigation of Multi-step attacks pose significant challenges due to the intricate nature of the diverse approaches utilized.This study aims to address the issue of imbalanced datasets within the domain of Multi-step attack detection.

To achieve this objective, the research explores three distinct re-sampling strategies, namely over-sampling, under-sampling, and hybrid re-sampling techniques.The study offers a comprehensive assessment of several re-sampling techniques utilized in the detection of Multi-step attacks on deep learning (DL) models.The efficacy of the solution is evaluated using a Multi-step cyber attack dataset that emulates attacks across six attack classes.

Furthermore, the performance of several re-sampling approaches with numerous traditional machine learning (ML) and deep learning (DL) models are compared, based on performance metrics such as accuracy, precision, recall, F-1 score, and G-mean.In contrast to preliminary studies, the research focuses on Multi-step attack detection.The results indicate that the combination of Convolutional Neural Networks (CNN) with Deep Belief Networks (DBN), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN) provides optimal results as compared to standalone ML/DL models.

Moreover, the results also depict that SMOTEENN, a hybrid re-sampling technique, demonstrates superior effectiveness in enhancing detection performance across various models and evaluation metrics.The findings indicate the AVOCADO LIME RANCH CHIPS significance of appropriate re-sampling techniques to improve the efficacy of Multi-step attack detection on DL models.