## 7 Conclusions  
Cyber-related attacks are evolving and increasing at exponential rates, and no method can recognize these modern attacks. Traditional IDSs are not good enough against modern cyberattacks. To build an effective IDS, feature creation, selection, and learning phases need to be evaluated carefully. All these processes are equally important to effectively fight against intrusions. In this paper, we explained a novel feature selection method which eliminates the redundant, irrelevant, and less-important features from the datasets. In addition, a hybrid (signature- and anomaly-based) classification technique is suggested to detect attacks both faster and with higher accuracy. The proposed method was performed on the KDD '99 as well as UNSW-NB15 datasets. The KDD '99 and UNSW-NB15 training datasets were analyzed while creating the signatures and rules to train the model. The created model was tested on the KDD '99 as well as UNSW-NB15 testing datasets. When the obtained results were compared against the known machine learning methods, better performance results were achieved based on the detection rates varying between 0.2% and 23%. In the suggested work, the test dataset, which contained the attack categories that were not included in the training dataset, was used in the test of the model created with the extracted signatures and rules. After intrusions were distinguished from the normal network traffic, the types of attacks were also identified. At this stage, we classified the types of attack as Dos, Probe, U2R, and U2L or Backdoor, Analysis, Generic, Fuzzers, Worms, etc. Considering the achieved accuracy values, it can be seen that unknown attack categories were also recognized with high accuracy. Furthermore, the proposed methodology outperformed machine learning algorithms in terms of memory and time usage. In addition, the proposed model was tested on the CIC-IDS2017 dataset for detecting attacks. An accuracy rate ranging from 99.52% to 99.91% was achieved. When comparing the proposed study against the state-of-the-art studies in the literature, only a few studies have developed new approaches for both the feature selection as well classification phases. It can be said that the performance of the proposed method is better than other methods in terms of the number of features used and the accuracy rate obtained. With these aspects, it can be said that the proposed method makes significant contributions to the literature.  
**Author Contributions:** Conceptualization, M.O.-O., O.A., R.S., S.K., T.I. and I.S.; methodology, M.O.-O. and R.S.; software, M.O.-O. and O.A.; validation, S.K., T.I. and I.S.; investigation, M.O.-O., S.K., T.I. and I.S.; writing--original draft preparation, M.O.-O., O.A., R.S. and T.I.; writing--review and editing, S.K., T.I. and I.S.; visualization, M.O.-O., R.S. and T.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.