## 4 Results and Discussion
### Security Analysis Using an Adversarial Attack Model
#### 4.2.2 Attack Generation and Preprocessing of Generated Attack Data
[CrossRef]
* (8) [PERSON]; [PERSON]; [PERSON]; [PERSON] Intrusion detection systems based on machine learning algorithms. In Proceedings of the 2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2 CACIS), Shah Alam, Malaysia, 26 June 2021 ; pp. 282-287. * (9) [PERSON]; [PERSON] Machine learning based intrusion detection systems for IoT applications. _Wirel. Pers. Commun._**2020**, _111_, 2287-2310. [CrossRef]
* (10) [PERSON]; [PERSON] A Study on Taxonomy and State-of-the-Art Intrusion Detection System. In Proceedings of the 2 nd International Conference on Advancement in Electronics & Communication Engineering (AECE 2022), Ghaziabad, India, 14-15 July 2022. * (11) [PERSON]; [PERSON]; [PERSON] Review on Intrusion Detection System (IDS) for Network Security using Machine Learning Algorithms. _Int. Res. J. Adv. Sci. Hub_**2022**, \(4\), 67-74. [CrossRef]
* (12) [PERSON]; [PERSON]; [PERSON] Intrusion detection systems, issues, challenges, and needs. _Int. J. Comput. Intell. Syst._**2021**, _14_, 560-571. [CrossRef]
* (13) [PERSON]; [PERSON]; [PERSON]; [PERSON] Intrusion Detection: A Review. _Mesopotamian J. Cybersecurity._**2021**, _2021_, 1-4. [CrossRef]
* (14) [PERSON]; [PERSON]; [PERSON] An efficient intrusion alerts miner for forensics readiness in high speed networks. _Int. J. Inf. Secur. Priv. (JIISP)_**2014**, \(8\), 62-78. [CrossRef]
* (15) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Explainable machine learning in cybersecurity: A survey. _Int. J. Intell. Syst._**2022**, _37_, 12305-12334. * (16) [PERSON]; [PERSON] A review on cybersecurity based on machine learning and deep learning algorithms. _J. Soft Comput. Data Min._**2021**, \(2\), 14-25. * (17) [PERSON] [PERSON]; [PERSON]; [PERSON] When is deep learning better and when is shallow learning better: qualitative analysis. _Int. J. Parallel Emergent Distrib. Syst._**2022**, _37_, 589-595. [CrossRef]
* (18) [PERSON] Machine learning: Algorithms, real-world applications and research directions. _SN Comput. Sci._**2021**, \(2\), 160. [CrossRef]
* (19) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] A comparative evaluation of machine learning algorithms for the prediction of R/C buildings' seismic damage. _arXiv_**2022**, arXiv:2203.13449. * (20) [PERSON]; [PERSON] [PERSON]; [PERSON] _Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies_; MIT Press: Cambridge, MA, USA, 2020. * (21) [PERSON], [PERSON]; [PERSON]; [PERSON] Integration of machine learning with cybersecurity: Applications and challenges. In _Artificial Intelligence in Cyber Security: Theories and Applications_; Springer: Berlin/Heidelberg, Germany, 2023; pp. 67-81. * (22) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Cloud-Based Intrusion Detection Approach Using Machine Learning Techniques. _Big Data Min. Anal._**2023**, \(6\), 311-320. * _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A machine learning-based intrusion detection for detecting internet of things network attacks. _[PERSON]. Eng. [PERSON]_**2022**, _61_, 9395-9409. [CrossRef]
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON] [PERSON] A novel time efficient learning-based approach for smart intrusion detection system. _J. Big Data_**2021**, \(8\), 111. * _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Imbalanced data classification: A KNN and generative adversarial networks-based hybrid approach for intrusion detection. _Future Gener. Comput. Syst._**2022**, _131_, 240-254. * _[PERSON] and [PERSON] (2021)_ [PERSON]; [PERSON] Analysis, design, and comparison of machine-learning techniques for networking intrusion detection. _Designs_**2021**, \(5\), 9. [CrossRef]
* _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Implementation of Naive Bayes Classifier for Reducing DDoS Attacks in IoT Networks. _J. Algebr. Stat._**2022**, _13_, 2749-2757. * _[PERSON] et al.