**5. Conclusions**

In this work, we propose a novel hybrid intrusion detection system to secure data in cloud computing based on an improved genetic algorithm (GA) and support vector machine (SVM) algorithm to create an intelligent IDS model. The improved GA is characterized by a newly developed fitness function that is used to evaluate the performance of the hybrid IDS. The fitness function combines three measures: F1-score, accuracy, and TPR, with different weights for each measure. This system was examined using two datasets: the newly developed dataset CICIDS2017, which consists of normal and most up-to-date common attacks, and the well-known dataset KDD CUP 99. The brilliance of the proposed system is that the GA and SVM are executed simultaneously to achieve two objectives at once, which are obtaining the best subset of 79 features with maximum accuracy. The SVM is used to classify data into benign and abnormal using different values for its hyperparameters, including the kernel function, gamma, and degree. Folding of the dataset is applied to change the training and testing dataset to examine the behavior of the system on these kinds of data. The results were analyzed and compared with similar works that applied GA

and SVM for IDS. The results showed that the proposed model outperformed these works in terms of accuracy by a maximum of 5.14% and a minimum of 3.32% using CICIDS2017, a maximum of 4.97% and a minimum of 3.03% using the KDD CUP 99 dataset, and a maximum of 5.74% and a minimum of 0.12% using the NSL-KDD dataset. This system proved its efficiency through the results obtained. To generalize the results, further experiments must be conducted. These must include using even larger datasets, using different datasets, trying different machine learning algorithms or combinations of ML algorithms, and trying more hyperparameters and parameter values for these ML algorithms.

**Author Contributions:** A.A. Conceptualization, Methodology, software, project administration, supervision, writing—review and editing. F.A. Formal analysis, investigation, Methodology, data curation, software, writing—original draft. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
