使用高级野马标准加密方法检测物联网智能环境中的攻击并确保数据传输安全的自动计量图神经网络

Q1 Mathematics
R. Yadawad, Umakant P. Kulkarni, Jafar A. Alzubi
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引用次数: 0

摘要

在物联网(IoT)的巨大推动下,智慧城市(SC)正在建设之中。在舒适和高效的基础上实时提高生活质量。在大多数智能城市中,安全和隐私是直接影响网络性能的关键问题。人们提出了许多安全数据传输的方法,但目前的方法精度不高,计算时间长。为了解决这些问题,我们提出了一种使用优化增强的基于身份的加密技术在物联网中进行攻击检测和安全数据传输的自动计量图神经网络(AGNN-AWHSE-ST-IoT)。输入数据主要来自 NSL-KDD 数据集。借助 NSL-KDD 收集到的输入数据将通过三个步骤进行预处理,即清晰数据转换、分割和归一化。然后,将预处理后的输入数据输入基于色彩和谐算法(CHA)的特征选择,以选择重要的特征。特征选择完成后,优先选择的特征将交给 AGNN 分类器。分类后,数据将交给基于身份的增强加密(EIBE),并使用野马优化器(WHO)对其进行优化,以便更安全地传输数据。正常数据的结果通过液晶显示器显示。AGNN-AWHSE-ST-IoT 方法在PYTHON 中实现。AGNN-AWHSE-ST-IoT 方法的准确率分别提高了 8.888%、13.953%、19.512%,累计准确率分别提高了 2.105%、6.593%、8.988%,加密时间分别缩短了 54.285%、54.285%、52.941%,解密时间分别缩短了 8.2%、3.3%、6.9%,加密时间分别缩短了 11.627%、10.344%。与 SBAS-ST-IoT、BDN-GWMNN-ST-IoT、DNN-LSTM-ST-IoT 等现有方法相比,安全等级分别提高了 11.627%、10.344%、6.666%,计算时间分别降低了 60.869%、70%、64%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Auto-metric Graph Neural Network for Attack Detection on IoT-based Smart Environment and Secure Data Transmission using Advanced Wild Horse Standard Encryption Method
Smart cities (SCs) are being constructed with the huge placement of the Internet of Things (IoT). Real-time enhancements to life quality based on comfort and efficiency. The key concerns in most SCs that immediately impact network performance are security and privacy. Numerous approaches are proposed for secure data transmission, but the current methods do not provide high accuracy and it provide high computational time. To resolve these problems, an Auto-metric Graph Neural Network for Attack Detection and Secure Data Transmission using Optimized Enhanced Identity-Based Encryption in IoT (AGNN-AWHSE-ST-IoT) is proposed. Primarily, the input data is taken from the NSL-KDD dataset. The input data is gathered with the aid of NSL-KDD is pre-processed using three steps, crisp data conversion, splitting, and normalization. Then the Pre-processed input is fed into the Colour Harmony Algorithm (CHA) based feature selection to select the important features. After feature selection, the preferred features are given to the AGNN classifier. After classifying, the data is given to Enhanced Identity-Based Encryption (EIBE), and it is optimized using Wild Horse Optimizer (WHO) for transmitting the data more safely. The outcomes of the normal data are displayed using the LCD monitor. The AGNN-AWHSE-ST-IoT method is implemented in PYTHON. The AGNN-AWHSE-ST-IoT method attains 8.888%, 13.953%, 19.512% higher accuracy, 2.105%, 6.593%, 8.988% higher cumulative accuracy, 54.285%, 54.285%, 52.941% lower encryption time, 8.2%, 3.3%, 6.9% lower decryption time, 11.627%, 10.344%, 6.666% higher security level and 60.869%, 70% and 64% lower computational time than the existing approaches such as SBAS-ST-IoT, BDN-GWMNN-ST-IoT and DNN-LSTM-ST-IoT respectively.
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来源期刊
CiteScore
4.10
自引率
0.00%
发文量
33
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