# A Hybrid AES with a Chaotic Map-Based Biometric Authentication Framework for IoT and Industry 4.0

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## Abstract

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## 1. Introduction

- Users send their fingerprints to systems using fingerprint sensors. After executing necessary image processing tasks, Raspberry Pi extracts biometric templates from the sensor’s fingerprints.
- These biometric templates are encrypted before transmissions and sent to servers by Raspberry Pi for avoiding hackers. Servers compare the received templates with previously saved database templates of users.
- When fingerprints match in these comparisons, servers grant access, the lack of which results in denial of access to systems.
- Here, the proposed AES algorithm is combined with the chaos system to create the encryption key, which is then used to encrypt the image.

## 2. Related Work

**Inference:**The fingerprint was selected as the biometric authentication method in this study. Sensing fingerprints are less costly than other biometric sensors that gather information on iris or hand geometries. In contrast to other works, this study employed the industry’s standard encryption technologies to ensure biometric template secrecy during transmissions and storage. Other investigations, including feature transformations (stenographical encryptions) and biometric cryptosystems (lightweight cryptographies), do not adversely influence performance rates when employed for creating template safeguards. Evaluations of the proposed work scheme with existing encryption techniques show that the proposed HAES-CM encryption scheme beats others in terms of processing speeds.

## 3. Proposed Methodology

#### 3.1. Fingerprint Enrolment and Client Pat

#### 3.2. Proposed HAES-CM-Based Security Model

Algorithm 1. The steps of the encryption algorithm |

Input:$E\times S\to FI$ // Dactylogram input picture and ${\mathfrak{X}}_{0},a$ // confidential attributesOutput: EFI //Encrypted Fingerprint pictureStep1: Read Dactylogram picture FI Step2: Algorithm to shift FI based on quadrate chaotic map (2). Step3: The shifted picture should be encrypted using the AEScryptography technique. Step4: Publish the encrypted picture EFI. |

Algorithm 2. The steps of the permutation process based on chaotic maps |

Input: Dimension $dim\to E\times S\to FI$ // Dactylogram input picture and ${\mathfrak{X}}_{0},a$ // confidential attributesOutput: $PFI$ // Shifted Fingerprint picture and original text $T$Step1: Let $\mathit{dim}=E\times S$Step2: Take a random pattern $\mathfrak{X}$ by quadratic map$\mathrm{Let}a=0.4,\mathfrak{X}=0.15,{\mathfrak{X}}_{0}=a\times {\mathfrak{X}}_{0}^{2}$ $\mathrm{For}=1todim$ ${\mathfrak{X}}_{i}=a\times {\mathfrak{X}}_{i-1}^{2}$ End Step3: Mapping the range of chaotic map $\mathfrak{X}$$\mathrm{from}[-0.60.6]$$\mathrm{to}[1dim]$, $\mathrm{max}=dim$, $\mathrm{min}=1$$\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}{T}_{i}=\frac{\left(Max\u2013Min\right)}{Max-Min}\times \left({\mathfrak{X}}_{i}\u2013Max\right)+Max$ End Step4: $\mathrm{Convert}\mathrm{the}PFI$ from 2D to 1DStep5: FI’s pixel locations should be moved in accordance with T.Step6: Resize the 1D array to create a PFI dectylogram that is 2D permuted. |

- Byte substitution: Each data block’s byte is changed to another block using an S-box.
- Row transformation: Depending on where it is in the state matrix, each row is given a cyclic shift to the right side.
- Each column of the state matrix is multiplied by that of the fixed matrix in the mix transformation of columns, which is a matrix multiplication operation.
- Round Key Addition: An XOR operation is carried out between the round key and the new state matrix.

- Sensitivity to the beginning value: Repetitive calculations on a chaotic map with the parameters result in a completely different sequence depending on small initial value changes.
- Sensitivity to the parameters: Repeated calculations on a chaotic map with the input values result in a completely new sequence for small changes in the parameters.
- Randomness: Most of the chaos sequences produced by chaos maps are pseudorandom sequences, and their structures are extremely difficult to anticipate and analyze.
- The attacker cannot predict the chaos sequence without the proper control parameters and beginning values. To put it another way, chaotic systems can increase the security of picture encryption systems.

## 4. Experimental Results and Discussion

#### 4.1. SSIM Comparison Results

#### 4.2. QILV Comparison Results

#### 4.3. Precision Comparison Results

#### 4.4. Sensitivity Comparison Results

#### 4.5. F-Measure Comparison Results

#### 4.6. PSNR Comparison Results

#### 4.7. MSE Comparison Results

#### 4.8. Accuracy Comparison Results

#### 4.9. Computation Time Comparison

## 5. Conclusions and Future Work

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 4.**The general framework diagram of cipher and decipher using AES [27].

**Figure 5.**Block diagram of the AES method [28].

Methods | SSIM | QILV | Precision (%) | Sensitivity (%) | F-Measure (%) | PSNR (dB) | MSE | Accuracy (%) | Computation Time (s) |
---|---|---|---|---|---|---|---|---|---|

ECC | 0.8241 | 0.8992 | 0.856 | 0.826 | 0.848 | 62.12 | 3.9 | 82.65 | 6.5 |

AES | 0.8564 | 0.9042 | 0.869 | 0.853 | 0.876 | 65.32 | 3.5 | 89.23 | 5.7 |

HAES-CM | 0.8896 | 0.9098 | 0.879 | 0.894 | 0.912 | 68.23 | 3.2 | 93.48 | 3.5 |

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**MDPI and ACS Style**

Altameem, A.; P, P.; T, S.; Poonia, R.C.; Saudagar, A.K.J.
A Hybrid AES with a Chaotic Map-Based Biometric Authentication Framework for IoT and Industry 4.0. *Systems* **2023**, *11*, 28.
https://doi.org/10.3390/systems11010028

**AMA Style**

Altameem A, P P, T S, Poonia RC, Saudagar AKJ.
A Hybrid AES with a Chaotic Map-Based Biometric Authentication Framework for IoT and Industry 4.0. *Systems*. 2023; 11(1):28.
https://doi.org/10.3390/systems11010028

**Chicago/Turabian Style**

Altameem, Ayman, Prabu P, Senthilnathan T, Ramesh Chandra Poonia, and Abdul Khader Jilani Saudagar.
2023. "A Hybrid AES with a Chaotic Map-Based Biometric Authentication Framework for IoT and Industry 4.0" *Systems* 11, no. 1: 28.
https://doi.org/10.3390/systems11010028