# An Efficient Optimization Approach for Designing Machine Models Based on Combined Algorithm

^{1}

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## Abstract

**:**

## 1. Introduction

## 2. Corresponding Work

## 3. Optimization during FSS Phase

_{1}= accuracy of feature selection problem) and minimizing (f

_{2}= number of selected subset features) simultaneously.

## 4. Results and Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Ogundokun, R.O.; Awotunde, J.B.; Sadiku, P.; Adeniyi, E.A.; Abiodun, M.; Dauda, O.I. An enhanced intrusion detection system using particle swarm optimization feature extraction technique. Procedia Comput. Sci.
**2021**, 193, 504–512. [Google Scholar] [CrossRef] - Houssein, E.H.; Gad, A.G.; Wazery, Y.M. Jaya algorithm and applications: A comprehensive review. Metaheuristics Optim. Comput. Electr. Eng.
**2021**, 696, 3–24. [Google Scholar] - Zitar, R.A.; Al-Betar, M.A.; Awadallah, M.A.; Doush, I.A.; Assaleh, K. An intensive and comprehensive overview of JAYA algorithm, its versions and applications. Arch. Comput. Methods Eng.
**2022**, 29, 763–792. [Google Scholar] [CrossRef] [PubMed] - Mohammadi, M.; Rashid, T.A.; Karim, S.H.T.; Aldalwie, A.H.M.; Tho, Q.T.; Bidaki, M.; Rahmani, A.M.; Hosseinzadeh, M. A comprehensive survey and taxonomy of the SVM-based intrusion detection systems. J. Netw. Comput. Appl.
**2021**, 178, 102983. [Google Scholar] [CrossRef] - Ahmad, Z.; Shahid Khan, A.; Wai Shiang, C.; Abdullah, J.; Ahmad, F. Network intrusion detection system: A systematic study of machine learning and deep learning approaches. Trans. Emerg. Telecommun. Technol.
**2021**, 32, e4150. [Google Scholar] [CrossRef] - Zheng, K.; Wang, X.; Wu, B.; Wu, T. Feature subset selection combining maximal information entropy and maximal information coefficient. Appl. Intell.
**2020**, 50, 487–501. [Google Scholar] [CrossRef] - Aljanabi, M.; Ismail, M.A.; Mezhuyev, V. Improved TLBO-JAYA algorithm for subset feature selection and parameter optimisation in intrusion detection system. Complexity
**2020**, 2020, 528768. [Google Scholar] [CrossRef] - Mojtaba, S.; Bamakan, H.; Wang, H.; Tian, Y.; Shi, Y. An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization. Neuro Comput.
**2016**, 199, 90–102. [Google Scholar] - Rao, R.V.; Venkata Rao, R. Teaching-Learning-Based Optimization Algorithm; Springer International Publishing: Berlin/Heidelberg, Germany, 2016. [Google Scholar]
- Das, S.P.; Padhy, S. A novel hybrid model using teaching learning-based optimization and a support vector machine for commodity futures index forecasting. Int. J. Mach. Learn. Cybern.
**2018**, 9, 97–111. [Google Scholar] [CrossRef] - Das, S.P.; Achary, N.S.; Padhy, S. Novel hybrid SVMTLBO forecasting model incorporating dimensionality reduction techniques. Appl. Intell.
**2016**, 45, 1148–1165. [Google Scholar] [CrossRef] - Rao, R.V.; Patel, V. An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Sci. Iran.
**2013**, 20, 710–720. [Google Scholar] [CrossRef] - Rao, R.V.; Patel, V. Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Appl. Math. Model.
**2013**, 37, 1147–1162. [Google Scholar] [CrossRef] - Satapathy, S.C.; Naik, A.; Parvathi, K. Weighted teaching-learning-based optimization for global function optimization. Appl. Math.
**2013**, 4, 28834. [Google Scholar] [CrossRef] - Lin, W.; Yu, D.Y.; Wang, S.; Zhang, C.; Zhang, S.; Tian, H.; Luo, M.; Liu, S. Multi-objective teaching–learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations. Eng. Optim.
**2015**, 47, 994–1007. [Google Scholar] [CrossRef] - Xu, Y.; Wang, L.; Wang, S.-Y.; Liu, M. An effective teaching-learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time. Neuro Comput.
**2015**, 148, 260–268. [Google Scholar] [CrossRef] - Al-Al-Janabi, M.; Ismail, M.A. Improved intrusion detection algorithm based on TLBO and GA algorithms. Int. Arab J. Inf. Technol.
**2021**, 18, 170–179. [Google Scholar] - Wang, S.H.; Muhammad, K.; Lv, Y.; Sui, Y.; Han, L.; Zhang, Y.D. Identification of Alcoholism based on wavelet Renyi entropy and three-segment encoded Jaya algorithm. Complexity
**2018**, 2018, 3198184. [Google Scholar] [CrossRef] - Migall’on, H.; Jimeno-Morenilla, A.; Sanchez-Romero, J.-L. Parallel improvements of the Jaya optimization algorithm. Appl. Sci.
**2018**, 8, 819. [Google Scholar] [CrossRef] - Gong, C. An enhanced Jaya algorithm with a two group Adaption. Int. J. Comput. Intell. Syst.
**2017**, 10, 1102–1115. [Google Scholar] [CrossRef] - Samuel, O.; Javaid, N.; Aslam, S.; Rahim, M.H. JAYA optimization based energy management controller for smart grid: JAYA optimization based energy management controller. In Proceedings of the 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, Pakistan, 3–4 March 2018. [Google Scholar]
- Yu, K.; Liang, J.J.; Qu, B.Y.; Chen, X.; Wang, H. Parameters identification of photovoltaic models using an improved JAYA optimization algorithm. Energy Convers. Manag.
**2017**, 150, 742–753. [Google Scholar] [CrossRef] - Aljanabi, M.; Ismail, M.A.; Ali, A.H. Intrusion detection systems, issues, challenges, and needs. Int. J. Comput. Intell. Syst.
**2021**, 14, 560–571. [Google Scholar] [CrossRef] - Khraisat, A.; Gondal, I.; Vamplew, P.; Kamruzzaman, J. Survey of intrusion detection systems: Techniques, datasets and challenges. Cyber Secur.
**2019**, 2, 1–22. [Google Scholar] [CrossRef] - Kavak, H.; Padilla, J.J.; Vernon-Bido, D.; Diallo, S.Y.; Gore, R.; Shetty, S. Simulation for cyber security: State of the art and future directions. J. Cyber Secur.
**2021**, 7, tyab005. [Google Scholar] [CrossRef] - Dash, M.; Liu, H. Feature selection for classification. Intell. Data Anal.
**1997**, 1, 131–156. [Google Scholar] [CrossRef] - Dumais, S.; Platt, J.; Heckerman, D.; Sahami, M. Inductive learning algorithms and representations for text categorization. In Proceedings of the Seventh International Conference on Information and Knowledge Management-CIKM’98, Bethesda, MD, USA, 3–7 November 1998. [Google Scholar]
- Jahan, A.; Mustapha, F.; Ismail, M.Y.; Sapuan, S.M.; Bahraminasab, M. A comprehensive VIKOR method for material selection. Mater. Des.
**2011**, 32, 1215–1221. [Google Scholar] [CrossRef] - Rodriguez, J.D.; Perez, A.; Lozano, J.A. Sensitivity analysis of k-Fold cross validation in prediction error estimation. IEEE Trans. Pattern Anal. Mach. Intell.
**2010**, 32, 569–575. [Google Scholar] [CrossRef] - Tavallaee, M.; Bagheri, E.; Lu, W.; Ghorbani, A.A. A detailed analysis of the KDD CUP 99 data set. In Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, ON, Canada, 8–10 July 2009; pp. 1–6. [Google Scholar]
- Al-Qatf, M.; Lasheng, Y.; Al-Habib, M.; Al-Sabahi, K. Deep learning approach combining sparse autoencoder with SVM for network intrusion detection. IEEE Access
**2018**, 6, 52843–52856. [Google Scholar] [CrossRef]

Ref. | Limitation |
---|---|

[6] | Computational cost |

[7] | Classification accuracy |

[8] | The need for training the model when adding new features |

C | γ |
---|---|

16 | 8 |

2 | 4 |

8 | 0.56 |

0.2 | 2 |

C | Subset Feature | Accuracy |
---|---|---|

16 | Fixed | 0.96 |

2 | Fixed | 0.87 |

8 | FIXED | 0.97 |

0.2 | Fixed | 0.963 |

C | Subset Feature | Accuracy | |
---|---|---|---|

16 | Constant | 0.96 best of worst | Worst group |

2 | Constant | 0.87 | |

8 | Constant | 0.97 best of best | Best group |

0.2 | Constant | 0.963 |

$\mathit{\gamma}$ | Subset Feature | Accuracy |
---|---|---|

8 | Constant | 0.96 |

4 | Constant | 0.97 |

0.56 | Constant | 0.982 |

2 | Constant | 0.98 |

$\mathit{\gamma}$ | Subset Feature | Accuracy | |
---|---|---|---|

8 | Constant | 0.96 | Worst group |

4 | Constant | 0.97 best of worst | |

0.56 | Constant | 0.982 best of best | Best group |

2 | Constant | 0.98 |

C | γ | Subset Feature | Accuracy |
---|---|---|---|

11.96 | 11.74 | Fixed | Result |

Measure | Formula |
---|---|

Accuracy | $\mathrm{A}\mathrm{c}\mathrm{c}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{y}=\frac{\mathrm{T}\mathrm{P}+\mathrm{T}\mathrm{N}}{\mathrm{T}\mathrm{P}+\mathrm{T}\mathrm{N}+\mathrm{F}\mathrm{P}+\mathrm{F}\mathrm{N}}$ |

False positive rate (FPR) | $\mathrm{F}\mathrm{P}\mathrm{R}=\frac{\mathrm{F}\mathrm{P}}{\mathrm{F}\mathrm{P}+\mathrm{T}\mathrm{N}}$ |

False negative rate (FNR) | $\mathrm{F}\mathrm{N}\mathrm{R}=\frac{\mathrm{FN}}{\mathrm{TP}+\mathrm{FN}}$ |

Detection rate (DR) | $\mathrm{D}\mathrm{R}=\frac{\mathrm{F}\mathrm{P}}{\mathrm{T}\mathrm{P}+\mathrm{F}\mathrm{P}}$ |

Recall | $\mathrm{R}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l}=\frac{\mathrm{T}\mathrm{P}}{\mathrm{T}\mathrm{P}+\mathrm{F}\mathrm{N}}$ |

F-Measure (F-M) | $\mathrm{F}-\mathrm{M}=\frac{(2\ast \mathrm{D}\mathrm{R}\ast \mathrm{R}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l})}{(\mathrm{D}\mathrm{R}+\mathrm{R}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l})}$ |

Error rate (ER) | $\mathrm{E}\mathrm{R}=\frac{\mathrm{F}\mathrm{P}+\mathrm{F}\mathrm{N}}{\mathrm{T}\mathrm{P}+\mathrm{F}\mathrm{P}+\mathrm{F}\mathrm{N}+\mathrm{T}\mathrm{N}}$ |

**Table 9.**NSL-KDD dataset [31].

Attack Classes | 22 Types of Attacks | No. of Instances |
---|---|---|

Normal | 67,343 | |

Dos | Smurt, Neptune, pod, teardrop, back, land | 45,927 |

R2L | Phf, ftp-write, imap, multihop, warezclient, warezmaster, spy, guess password | 995 |

U2R | Perl, loadmodule, buffer-overflow, rootkit | 52 |

Probing | Portsweep, ipsweep, satan, nmap | 11,656 |

Parameter | Value |
---|---|

MTLBO Population size | 41 |

MTLBO Number of generations | 82 |

JAYA Population size | 41 |

JAYA Number of generations | 82 |

MJAYA Population size | 41 |

MJAYA Number of generations | 82 |

MTLBO Population size | 41 |

MJAYA Number of generations | 82 |

t-Test on NSL-KDD Dataset | Value |
---|---|

p-value | 0.02 |

T-value | 3.2 |

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**MDPI and ACS Style**

Larijani, A.; Dehghani, F.
An Efficient Optimization Approach for Designing Machine Models Based on Combined Algorithm. *FinTech* **2024**, *3*, 40-54.
https://doi.org/10.3390/fintech3010003

**AMA Style**

Larijani A, Dehghani F.
An Efficient Optimization Approach for Designing Machine Models Based on Combined Algorithm. *FinTech*. 2024; 3(1):40-54.
https://doi.org/10.3390/fintech3010003

**Chicago/Turabian Style**

Larijani, Ata, and Farbod Dehghani.
2024. "An Efficient Optimization Approach for Designing Machine Models Based on Combined Algorithm" *FinTech* 3, no. 1: 40-54.
https://doi.org/10.3390/fintech3010003