## 4 Results and Discussion
### Security Analysis Using an Adversarial Attack Model
#### 4.2.2 Attack Generation and Preprocessing of Generated Attack Data
The proposed model emphasizes the implementation of global traffic inspections before ingress into an organization's network, as well as the implementation of two-layer security strategies. The proposed model also inspects the entire traffic with a faster model and inspects the subdomains with high accuracy at all times. Compared with other IDS models, the proposed XGBoost model has high accuracy and efficiency. Although the proposed multi-model IDS is comparable to existing solutions in terms of accuracy, analyzing performance metrics reveals additional insights. The precision, F1-score, and recall of the system were carefully considered, showing its ability to identify true positives while minimizing false positives at the same time. In addition to its robust generalization across diverse datasets and simulated network traffic scenarios, the proposed multi-model IDS can adapt to varying threat landscapes, demonstrating its efficiency in dynamic environments. The proposed model can be improved in several ways. For instance, the IPS prevention of malicious IPs through firewalls can be accomplished automatically by using a threat intelligence feed. The UNSW-NB15 dataset will be used. We also plan to focus on the network architecture inside an authentic setting pertaining to a particular business. Subsequently, we will proceed to perform an empirical investigation within this real-world environment, wherein we will gather relevant traffic data. These data will be appropriately processed and formatted to align with the models used in this paper. Deep learning algorithms may represent a good alternative to conduct this kind of learning and analysis. Conceptualization, N.A., M.R. and A.A.; methodology, N.A., M.R. and A.A.; software, N.A. ; validation, N.A., M.R. and A.A.; formal analysis, N.A., M.R. and A.A.; investigation, N.A., M.R. and A.A.; resources, N.A. and A.A.; data curation, N.A. ; writing--original draft preparation, N.A. ; writing--review and editing, N.A., M.R. and A.A.; visualization, M.R. ; supervision, M.R. and A.A.; project administration, N.A., M.R. and A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript. This research work was funded by Umm Al-Qura University, Saudi Arabia, under grant number 25 UQU4361220 GSSR02. Not applicable. The raw data supporting the conclusions of this article will be madeavailable by the authors on request. The authors extend their appreciation to Umm Al-Qura University, Saudi Arabia, for funding this research work through grant number 25 UQU4361220 GSSR02. ## References

* (1) [PERSON]; [PERSON] An Effective Blockchain-Based Defense Model for Organizations against Vishing Attacks. _Appl. Sci._**2022**, _12_, 13020. [CrossRef]
* (2) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A machine learning approach for improving the performance of network intrusion detection systems. _Ann. Emerg. Technol. Comput. (AETIC)_**2021**, \(5\), 201-208. [CrossRef]
* (3) [PERSON]; [PERSON] Software defined networking architecture, traffic management, security, and placement: A survey. _Comput. Netw._**2021**, _192_, 108047. [CrossRef]
* (4) [PERSON]; [PERSON]; [PERSON] A Systematic Study on Network Attacks and Intrusion Detection System. In _Machine Intelligence and Data Science Applications: Proceedings of MIDAS 2021_; Springer: Berlin/Heidelberg, Germany, 2022; pp. 195-210. * (5) [PERSON]; [PERSON]; [PERSON] Intrusion detection systems alerts reduction: New approach for forensics readiness. In _Security and Privacy Management, Techniques, and Protocols_; IGI Global: Hershey, PA, USA, 2018; pp. 255-275. * (6) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Systematic Review of Artificial Intelligence Techniques Used for IDS Analysis. _J. Comput. Biomed. Inform._**2023**, \(5\), 52-67. * (7) [PERSON]; [PERSON]; [PERSON] Network intrusion detection system: Machine learning approach. _Indones. J. Electr. Eng. Comput. Sci._**2022**, _25_, 1151-1158.