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Systematic and well grounded data transmission over wireless networks has been substance of uninterrupted research over the last few years. The paramount is scrutinizing the amount of security provisioning owing to the security challenges during transmission over wireless network. In fact, it is moderate to eavesdrop and alter data packets. Accessing the personal computer and public network possess the potentiality to apprehend the network traffic possibly compromising the privacy. Therefore for wireless applications, it is essential to ensure data integrity during data transmission. To efficiently address the above issues, a Deep Neural Certificate less Hessian Curve Heap Sign cryption (DNC-HCHS) method for secured data transmission in wireless network is proposed. Compared with the conventional, Certificate less Sign cryption DNC-HCHS method improves the data confidentiality and data integrity by generating smaller keys employing the Hessian Curve Heap function. Additionally with the assistance of the access point or the aggregator, the sensitivity of heaped sign crypted cipher text can improve the security of data transmission and reduce the message delivery time. Aimed at reducing the delay in data transmission, application of Certificate less Hessian Curve Heap Sign cryption in Deep Learning (i.e., Deep Neural Network) performs the overall process in a swift manner and performs a much better encryption. Simulation is performed to validate the viability and efficiency of the proposed method. The results show that the data confidentiality and data integrity rate are strongly improved, while the delay is minimized.

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