Secure Deep Learning Architecture Model for Data Management and Scheduling of E-Commerce Data
DOI:
https://doi.org/10.69996/jsihs.2024007Keywords:
E-Healthcare, Data Security, Intrusion Detection System (IDS), Deep Learning, Scheduling, E-commerceAbstract
E-commerce refers to the buying and selling of goods and services online, including everything from clothes and electronics to food and household items. It typically involves online transactions, such as payments and delivery. E-healthcare, on the other hand, refers to the delivery of healthcare services and information through electronic means, such as the Internet, mobile devices, and telemedicine. Conventionally, healthcare systems using E-commerce are subjected to challenges associated with security, Regulatory Compliance, Quality, Payment, limited accessibility and adoption. Security in the Ecommerce platform is achieved with the Intrusion Detection System (IDS) which requires appropriate data management and scheduling process. This paper proposed a Secure Scheduling Key Management Deep Learning (SSKMDL) for the e-commerce in healthcare application. The SSKMDL model uses the key authority quantum channel for the key generation to minimize the eavesdropping, transmission error and data leakage to increase the security. The E-commerce user and content server communicate key server with the quantum channel for the encryption key and key server for the public channel in the groups. The SSKMDL model evaluate the different generated secret key for the user quantum distribution key to increase security with appropriate data management. Additionally, the SSKMDL model uses the nature-inspired algorithm for the accurate scheduling and detection of attacks in the data. The deep learning E-commerce application increases the detection rate of intrusion with the hybrid effectively and efficiently for the classification of anomalies network traffic and hybrid regression and Decision Tree model. The performance of proposed SSKMDL model is evaluated for the Network Security Laboratory Knowledge Discovery in Data Bases (NSL KDD) dataset and real-time E-healthcare dataset. The analysis stated that SSKMDL E-commerce model exhibits the optimal group key for the training and testing to achieve efficient data management with scheduling to increase security. The experimental results expressed that proposed SSKMDL model utilizes deep learning model for the generation of key, optimization, healthcare encryption and decryption to increase the security with deep learning model. With constructed SSKMDL model eavesdropping rate is reduced by 80% with the increases attack detection rate of 99%.
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