## 1 Introduction  
Recently, the need for cybersecurity and protective measures against cyberattacks has increased. Cyberattacks are typically criminal activities launched over the Internet. Cyberattacks include theft of corporate intellectual property, theft of online bank accounts, creation and distribution of malware on systems, disclosure of valuable corporate information through public media such as the Internet, and disruption of countries' critical infrastructure. Thefts or losses of data or information are very serious consequences of cyber-attacks worldwide [1]. In order to learn more, cyber threats should be studied proactively, and the experiences of other organizations affected by cyber-attacks should be shared [2]. In both the education and digital industries, especially small and medium enterprises (SMEs), online and computer networks are increasingly exposed to extremely complex cyber threats, resulting in financial losses [3]. Therefore, the research and development of cybersecurity technologies are important for IDSs to establish the first lines of defense, in order to prevent and respond to intrusion threats when new security issues arise. The development of data-driven intelligent IDS can analyze various patterns of cyber events, and then predict threats based on the examined data. Therefore, artificial intelligence (AI) expertise that uses MLTs to learn from security datasets can play a critical role in mitigating these threats. In the area of predictive analytics, tree-based strategies perform better in MLTs [4]. Modern security datasets contain rich features and dimensions in terms of security properties, with many irrelevant features that add to the complexity of efficient cyberattack modeling. These additional features also form the basis for several problems, such as increased variances, leading to overfitting of data in tree-based models that learn decisions based on single paths; an increase in computation and execution time of training models; and a lack of model generalization [5; 6; 7; 8]. This leads to a lower prediction accuracy of attack detection rate. One of the most accurate metrics scores, the accuracy score, illustrates how effectively the model generates correct forecasts in general. Therefore, the main goal of this work is to minimize the security problems described above and to develop efficient data-driven IDS for cybersecurity. To achieve this goal, this work proposed OGBDT-IDS based on MLIs for network security. The proposed scheme helps to mitigate the above problems. First, the security features are ranked according to their modeling relevance, and then a tree-based generalized IDS is constructed based on the selected relevant features using GAs. Model validation of the OGBDT trees created in training is performed using test data. By reducing overlap in modeling, the computational complexity of the proposed model is reduced by dimensionality reduction before generating the results. The proposed scheme is ideal for the improved prediction of new and not-found cases. The contributions of this research are listed below:  
1. For high-dimensional data, MLTs are proposed as a method for ranking the importance of security attributes using Gini indices. 2. OGBDT-IDS is a network security technique based on MLTs that produces trees using derived attribute ranks and selects important features using GAs. 3. To maximize the predictive power of the GBDT models, an OGBDT is created based on the selected significant attributes. Hyperparameters can be tuned manually or by automated methods, such as those based on EABOs. 4. The proposed IDS (OGBDT-IDS) is then evaluated through experiments. The experimental results of this study with test data show that the proposed methods improve cyber intrusion detection for new and unused cases. The performance of the proposed OGBDT-IDS is compared with the existing one given in the literature [6; 7; 8; 9; 10; 11], for validation reasons. OGBDTs perform better than all other models in terms of accuracy.