## 7 Conclusions  
A new physical layer authenticated encryption (PLAE) technique focused on the multi-parameter fractional Fourier transform-orthogonal frequency division multiplexing (MP-FrFT-OFDM) is proposed in this paper for secure image transmission over public IoT networks. This paper designs and studies a new, robust multi-cascaded chaotic modular fractional sine map (MCC-MF sine map). A novel dynamic chaotic biometric (Digital Fingerprint) signature (DCBS) generator based on combining the biometric signature and the suggested MCC-MF sine map random chaotic sequence output is also devised. It is based on the proposed MCC-MF sine map random chaotic sequence output. For the multi-parameter fractional Fourier transform in the OFDM system, which studies the encryption process in the frequency domain, the suggested DCBS generator's output is used as a dynamic secret key. The suggested DCBS secret key generator is used to satisfy the secrecy and authentication features. The proposed DCBS-MP-FrFT-OFDM cryptosystem over IoT network's security strengths are tested using statistical analysis, differential analysis, and key sensitivity analysis. The suggested proposed DCBS-MP-FrFT-OFDM cryptosystem's ability to withstand various attacks is tested in order to gauge how strong the encryption system is. The suggested approach for evaluating it was subjected to a safety examination, which covered discussions of the histogram, entropy, correlation coefficient, NPCR, UACI, and NIST randomness tests. This study adds to the body of literature by further examining the flaws of using the MPFrFT as two dimensions with multi-parameters of the FrFT, which increase the secret key space based on multi-phase shifting strategy in OFDM. The proposed DCBS-MP-FrFT-OFDM cryptosystem does not need any additional equipment, except the OFDM is replaced by MP-FrFT-OFDM and an external two-dimensional multi-parameters DCBS generator. The DCBS generator generates the all the secret keys in the proposed cryptosystem. In addition, the limitations of the proposed MPFrFT-OFDM scheme include that the two secret vectors can be optimized in order to improve the BER performance. On the other hand, for the security analysis, the MPFrFT OFDM has a very large key space as discussed in the key space analysis compared with other systems. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Criteria** & **Proposed** & **Ref. [(48)]** & **Ref. [(74)]** & **Ref. [(75)]** & **Ref. [(76)]** & **Ref. [(77)]** \\ \hline Key space & \(2^{2200}\) & - & \(2^{942}\) & \(2^{441}\) & - & - \\ \hline Entropy & 7.9999 & 7.7771 & 7.9997 & 7.9974 & 7.9022 & 7.9973 \\ \hline CC-H & \(-0.0011\) & -0.0219 & \(-0.0002\) & 0.0021 & 0.0022 & 0.0044 \\ \hline CC-V & 0.0127 & - & 0.0004 & - & - & \\ \hline CC-V & 0.0087 & - & 0.0001 & - & - & \\ \hline NPCR (\%) & 99.8945 & 99.7400 & 99.611 & 99.6123 & 99.62 & 99.63 \\ \hline UACI (\%) & 33.5283 & 27.5200 & 33.471 & 33.46 & 33.46 & – \\ \hline Authentication & ✓ & \(\times\) & ✓ & \(\times\) & \(\times\) & \(\times\) \\ \hline Encryption & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Performance comparison between the proposed cryptosystem results and other methods described in the literature for a Lena image of size \(256\times 256\). In the future, we will propose a brand-new deep CNN that can produce a digital signature in order to satisfy the identity property. Additionally, a digital deep CNN signcryption system can be created to combine the encryption and digital signature. Future studies could also concentrate on watermarking, data hiding in encrypted images, and stream video encryption and decoding. It will also be advised to use a new Deep Convolutional Neural Network for a compression-encryption system. Also, in future work, different optimization schemes can be used to optimize the selection of the two vectors \(\overline{a}\) and \(\overline{b}\) with a size of \(1\times 256\) fractional numbers in the range of (0 to 1) to improve the BER performance of the proposed MPFFFT OFDM.