Link-Based Xcorr Normalization and Attention Mechanism for Predicting the Threats over the Network Model

aspg-2-2766-1791417491 20261007235811 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cybersecurity and Information Management JCIM 2769-7851 2690-6775 2024 13 2 Link-Based Xcorr Normalization and Attention Mechanism for Predicting the Threats over the Network Model V. Jemmy Joyce Department of Mathematics, Karunya Institute of Technology and Sciences, Coimbatore, India K. Rebecca Jebaseeli Edna Department of Mathematics, Karunya Institute of Technology and Sciences, Coimbatore, India P. Sherubha Department of Information Technology, Karpagam College of Engineering, Coimbatore, India Arivazhagi Department of Computer Science and Engineering, University college of Engineering, Ariyalu, India Sensor Networks (SNs) play an essential role in upcoming technologies like the Internet of Things (IoT), where technical services are highly prone to crucial vulnerability due to attacks. This research motivates to provide a mechanism to identify the link reliability of connected sensor nodes. The privacy-preserving keys are distributed among the corresponding network nodes. When the nodes suffer from an attack, it damages the linking nodes' community. It has the nature of healing itself when the attacks are identified over the network. The self-healing nature is not so complex, and it is termed a lightweight process. A novel link-based intrusion prediction mechanism uses attention-based Deep Neural Networks ( style='mso-bidi-font-style:normal'> font-family:"Cambria Math",serif;mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:major-bidi;color:black;mso-themecolor:text1'>a coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f"> height:13.5pt'> o:title="" chromakey="white"/> -DNN) for lightweight linkage identification and labelling. This model helps predict basic network patterns using topological analysis with better generalization. The simulation is done with Python where the proposed style='mso-bidi-font-style:normal'> font-family:"Cambria Math",serif;mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:major-bidi;color:black;mso-themecolor:text1'>k type="#_x0000_t75" style='width:6pt;height:13.5pt'> o:title="" chromakey="white"/> -DNN model outperforms the five different conventional approaches with the adoption of a benchmark dataset (network traffic) for extensive analysis. The AUC is improved in an average manner with the adoption of normal'> mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:major-bidi; color:black;mso-themecolor:text1'>k type="#_x0000_t75" style='width:6pt;height:13.5pt'> o:title="" chromakey="white"/> -DNN. This model enhances the linkage connectivity to make different connectivity processes more efficient and reach the target non-convincing. It is sensed that the proposed style='mso-bidi-font-style:normal'> font-family:"Cambria Math",serif;mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:major-bidi;color:black;mso-themecolor:text1'>a type="#_x0000_t75" style='width:6pt;height:13.5pt'> o:title="" chromakey="white"/> -DNN outperforms the existing approaches by improving the network resilience by maintaining higher energy efficiency. 2024 96 108 2766 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCIM.130208 https://www.americaspg.com/journal/2/article/2766 https://www.americaspg.com/storage/91715068839.pdf