# Sine-Cosine-Adopted African Vultures Optimization with Ensemble Autoencoder-Based Intrusion Detection for Cybersecurity in CPS Environment

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## Abstract

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## 1. Introduction

- An automated SCAVO-EAEID technique comprising Z-score normalization, the SCAVO-FS technique, LSTM-AE-based intrusion detection, and the RMSProp optimizer is developed for intrusion detection in the CPS environment. To the best of the researchers’ knowledge, no researchers have proposed the SCAVO-EAEID technique in the literature.
- A new SCAVO-FS technique has been designed by integrating the sine-cosine scaling factor and the AVO algorithm for the repositioning of the vultures at the end of the iterations.
- Both the RMSProp optimizer and the LSTM-AE model are employed in this study for the intrusion detection process.
- The performance of the proposed SCAVO-EAEID technique was validated using two benchmark datasets such as the NSL-KDD 2015 and CICIDS2017 datasets.

## 2. Related Works

## 3. Proposed Model

#### 3.1. Data Used

#### 3.2. Data Preprocessing

#### 3.3. Processes Involved in the SCAVO-FS Technique

#### 3.4. Classification Model

#### 3.5. Hyperparameter Tuning Model

## 4. Results Analysis

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 3.**Overall classification outcomes of the proposed SCAVO-EAEID technique and other techniques on the NSL-KDD dataset.

**Figure 6.**Overall classification outcomes of the SCAVO-EAEID and other techniques on the CICIDS-2017 dataset.

**Figure 7.**TACC and VACC analytical outcomes of the SCAVO-EAEID technique upon the CICIDS-2017 dataset.

Best Cost | ||
---|---|---|

Methods | NSL-KDD-2015 | CICIDS-2017 |

SCAVO-FS | 0.05101 | 0.41204 |

AHSA-FS | 0.05433 | 0.04311 |

BBFO-FS | 0.07382 | 0.06445 |

BFO-FS | 0.09371 | 0.08753 |

SSO-FS | 0.10384 | 0.09422 |

WOA-FS | 0.11940 | 0.11790 |

Number of Selected Features | ||
---|---|---|

Methods | NSL-KDD-2015 | CICIDS-2017 |

Total Features | 41 | 80 |

SCAVO-FS | 14 | 17 |

AHSA-FS | 15 | 19 |

BBFO-FS | 18 | 24 |

BFO-FS | 19 | 30 |

SSO-FS | 20 | 28 |

WOA-FS | 20 | 33 |

**Table 3.**Classification outcomes of the proposed SCAVO-EAEID technique and other techniques on the NSL-KDD dataset.

Training/Testing Phase (%) | Accuracy | Precision | Recall | F-Score |
---|---|---|---|---|

40:60 | ||||

SCAVO-EAEID | 98.70 | 99.16 | 98.13 | 99.23 |

PRO-DLBIDCPS | 98.29 | 98.80 | 97.74 | 98.91 |

BBFO-GRU Model | 97.92 | 98.44 | 97.42 | 98.41 |

Optimal GRU Algorithm | 97.44 | 98.21 | 97.02 | 98.05 |

GRU Algorithm | 97.16 | 97.85 | 96.79 | 97.69 |

50:50 | ||||

SCAVO-EAEID | 98.74 | 99.24 | 98.14 | 99.53 |

PRO-DLBIDCPS | 98.48 | 99.03 | 97.92 | 99.30 |

BBFO-GRU Model | 98.12 | 98.73 | 97.65 | 98.96 |

Optimal GRU Algorithm | 97.92 | 98.32 | 97.27 | 98.53 |

GRU Algorithm | 97.63 | 97.87 | 96.80 | 98.27 |

60:40 | ||||

SCAVO-EAEID | 98.91 | 99.50 | 98.17 | 99.71 |

PRO-DLBIDCPS | 98.41 | 99.15 | 97.90 | 99.30 |

BBFO-GRU Model | 97.96 | 98.71 | 97.54 | 98.87 |

Optimal GRU Algorithm | 97.62 | 98.34 | 97.21 | 98.60 |

GRU Algorithm | 97.25 | 97.99 | 96.86 | 98.40 |

70:30 | ||||

SCAVO-EAEID | 98.95 | 99.50 | 99.12 | 99.81 |

PRO-DLBIDCPS | 98.6 | 99.15 | 98.81 | 99.58 |

BBFO-GRU Model | 98.33 | 98.93 | 98.45 | 99.19 |

Optimal GRU Algorithm | 98.02 | 98.44 | 97.99 | 98.69 |

GRU Algorithm | 97.69 | 98.16 | 97.62 | 98.29 |

80:20 | ||||

SCAVO-EAEID | 99.20 | 99.58 | 99.42 | 99.84 |

PRO-DLBIDCPS | 99.00 | 99.12 | 99.03 | 99.41 |

BBFO-GRU Model | 98.79 | 98.89 | 98.55 | 98.95 |

Optimal GRU Algorithm | 98.49 | 98.47 | 98.24 | 98.52 |

GRU Algorithm | 98.24 | 98.16 | 97.91 | 98.26 |

**Table 4.**Classification outcomes of the SCAVO-EAEID and other techniques on the CICIDS-2017 dataset.

Training/Testing Phase (%) | Accuracy | Precision | Recall | F-Score |
---|---|---|---|---|

40:60 | ||||

SCAVO-EAEID | 99.04 | 98.97 | 98.17 | 99.26 |

PRO-DLBIDCPS | 98.73 | 98.63 | 97.91 | 98.76 |

BBFO-GRU Model | 98.26 | 98.38 | 97.65 | 98.42 |

Optimal GRU Algorithm | 97.79 | 97.90 | 97.27 | 98.18 |

GRU Algorithm | 97.49 | 97.51 | 97.07 | 97.72 |

50:50 | ||||

SCAVO-EAEID | 99.11 | 99.52 | 98.47 | 99.46 |

PRO-DLBIDCPS | 98.62 | 99.13 | 98.23 | 99.07 |

BBFO-GRU Model | 98.36 | 98.84 | 97.74 | 98.69 |

Optimal GRU Algorithm | 98.08 | 98.56 | 97.29 | 98.37 |

GRU Algorithm | 97.62 | 98.28 | 96.88 | 98.11 |

60:40 | ||||

SCAVO-EAEID | 98.76 | 99.32 | 98.14 | 99.51 |

PRO-DLBIDCPS | 98.43 | 98.89 | 97.68 | 99.01 |

BBFO-GRU Model | 98.01 | 98.49 | 97.40 | 98.63 |

Optimal GRU Algorithm | 97.63 | 97.99 | 96.92 | 98.31 |

GRU Algorithm | 97.29 | 97.59 | 96.61 | 97.92 |

70:30 | ||||

SCAVO-EAEID | 99.18 | 99.54 | 99.42 | 99.62 |

PRO-DLBIDCPS | 98.83 | 99.27 | 99.14 | 99.21 |

BBFO-GRU Model | 98.51 | 98.93 | 98.70 | 98.72 |

Optimal GRU Algorithm | 98.07 | 98.71 | 98.33 | 98.42 |

GRU Algorithm | 97.81 | 98.36 | 98.07 | 98.03 |

80:20 | ||||

SCAVO-EAEID | 99.10 | 99.67 | 99.82 | 99.73 |

PRO-DLBIDCPS | 98.60 | 99.23 | 99.55 | 99.51 |

BBFO-GRU Model | 98.25 | 98.84 | 99.24 | 99.29 |

Optimal GRU Algorithm | 97.79 | 98.40 | 98.98 | 99.01 |

GRU Algorithm | 97.52 | 97.92 | 98.54 | 98.64 |

**Table 5.**Comparative $acc{u}_{y}$ analysis outcomes achieved by the proposed SCAVO-EAEID technique and other techniques.

Methods | Accuracy (%) |
---|---|

SCAVO-EAEID | 99.20 |

PRO-DLBIDCPS Model [12] | 99.00 |

BBFO-GRU Model [23] | 98.79 |

DT Model [12] | 96.85 |

MLIDS Model [12] | 94.02 |

CSPSO Model [12] | 74.98 |

CO Model [12] | 98.47 |

DNN-SVM Model [12] | 93.31 |

GA-Fuzzy [12] | 97.51 |

FCM Model [12] | 97.4 |

GBT Model [12] | 84.64 |

Methods | Training Time (min) | Testing Time (min) |
---|---|---|

SCAVO-EAEID | 0.542 | 0.246 |

PRO-DLBIDCPS Model [12] | 0.752 | 0.381 |

BBFO-GRU Model [23] | 1.106 | 0.363 |

DT Model [12] | 0.888 | 0.677 |

MLIDS Model [12] | 1.212 | 0.331 |

CSPSO Model [12] | 1.242 | 0.425 |

CO Model [12] | 0.802 | 0.572 |

DNN-SVM Model [12] | 1.384 | 0.996 |

GA-Fuzzy [12] | 1.351 | 0.444 |

FCM Model [12] | 1.749 | 0.873 |

GBT Model [12] | 1.463 | 0.875 |

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## Share and Cite

**MDPI and ACS Style**

Almuqren, L.; Al-Mutiri, F.; Maashi, M.; Mohsen, H.; Hilal, A.M.; Alsaid, M.I.; Drar, S.; Abdelbagi, S.
Sine-Cosine-Adopted African Vultures Optimization with Ensemble Autoencoder-Based Intrusion Detection for Cybersecurity in CPS Environment. *Sensors* **2023**, *23*, 4804.
https://doi.org/10.3390/s23104804

**AMA Style**

Almuqren L, Al-Mutiri F, Maashi M, Mohsen H, Hilal AM, Alsaid MI, Drar S, Abdelbagi S.
Sine-Cosine-Adopted African Vultures Optimization with Ensemble Autoencoder-Based Intrusion Detection for Cybersecurity in CPS Environment. *Sensors*. 2023; 23(10):4804.
https://doi.org/10.3390/s23104804

**Chicago/Turabian Style**

Almuqren, Latifah, Fuad Al-Mutiri, Mashael Maashi, Heba Mohsen, Anwer Mustafa Hilal, Mohamed Ibrahim Alsaid, Suhanda Drar, and Sitelbanat Abdelbagi.
2023. "Sine-Cosine-Adopted African Vultures Optimization with Ensemble Autoencoder-Based Intrusion Detection for Cybersecurity in CPS Environment" *Sensors* 23, no. 10: 4804.
https://doi.org/10.3390/s23104804