ORTHOGONAL REGRESSED STEEPEST DESCENT DEEP PERCEPTIVE NEURAL LEARNING FOR IoT- AWARE SECURED BIG DATA COMMUNICATION

IF 0.9 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Jordanian Journal of Computers and Information Technology Pub Date : 2023-01-01 DOI:10.5455/jjcit.71-1669807150
S. V., Swapna L
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Abstract

The Internet of Things (IoT) is a collection of interconnected intelligent devices that exists within the larger network known as the Internet. With the increasing popularity of IoT devices, massive data is generated day by day. The collected data need to be continuously uploaded to the cloud server. Besides, the transmission of data in the cloud environment is performed via the internet, which faces numerous threats. However, the security issue always lacks an effective big data communication. Therefore, a novel technique called Orthogonal Regressed Steepest Descent Deep Structured Perceptive Neural Learning based Secured Data Communication (ORSDDSPNL-SDC) is introduced with higher accuracy and lesser time consumption. The ORSDDSPNL-SDC technique comprises three phases, namely registration, user authentication, and secure data communication. In the ORSDDSPNL-SDC technique, the registration phase is carried out for creating the new ID, and password for each user in the cloud. The IoT device's data is then sent to a cloud server by the cloud user for storage. After that, the orthogonal regressed steepest descent multilayer deep perceptive neural learning is applied to examine the user_ ID with already registered ID based on Szymkiewicz–Simpson coefficient. Then the Maxout activation function is to classify the user as authorized or unauthorized. Finally, the steepest descent function is applied for minimizing the classification error and increasing the classification accuracy. In this way, the authorized or unauthorized user is identified. Then the secured communication is performed with the authorized cloud users. Experimental evaluation is carried out on the factors such as classification accuracy, classification time and error rate, and space complexity with respect to a number of users. The qualitative results and discussion indicate that the proposed ORSDDSPNL-SDC offers elevated performance with regard to achieving higher classification accuracy and minimum error as well as computation time when compared to the existing methods.
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面向物联网感知安全大数据通信的正交回归最陡下降深度感知神经学习
物联网(IoT)是存在于称为互联网的更大网络中的相互连接的智能设备的集合。随着物联网设备的日益普及,海量数据日益产生。采集到的数据需要持续上传到云服务器。此外,云环境中的数据传输是通过互联网进行的,这面临着许多威胁。然而,安全问题一直缺乏有效的大数据沟通。因此,提出了一种新的基于正交回归最陡下降深度结构感知神经学习的安全数据通信技术(ORSDDSPNL-SDC),该技术具有更高的精度和更少的时间消耗。ORSDDSPNL-SDC技术包括注册、用户认证和安全数据通信三个阶段。在ORSDDSPNL-SDC技术中,执行注册阶段,为云中每个用户创建新的ID和密码。然后,物联网设备的数据由云用户发送到云服务器进行存储。然后,基于Szymkiewicz-Simpson系数,应用正交回归最陡下降多层深度感知神经学习对已注册ID的user_ ID进行检测。然后Maxout激活功能将用户划分为已授权或未授权。最后,利用最陡下降函数最小化分类误差,提高分类精度。通过这种方式,可以识别已授权或未授权的用户。然后与授权的云用户进行安全通信。针对多个用户,对分类准确率、分类时间错误率、空间复杂度等因素进行了实验评价。定性结果和讨论表明,与现有方法相比,所提出的ORSDDSPNL-SDC在实现更高的分类精度和最小误差以及计算时间方面具有更高的性能。
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来源期刊
Jordanian Journal of Computers and Information Technology
Jordanian Journal of Computers and Information Technology Computer Science-Computer Science (all)
CiteScore
3.10
自引率
25.00%
发文量
19
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