Volume 13 , Issue 2 , PP: 96-108, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
V. Jemmy Joyce 1 , K. Rebecca Jebaseeli Edna 2 , P. Sherubha 3 * , Arivazhagi 4
Doi: https://doi.org/10.54216/JCIM.130208
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 (
-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 
-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 -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 -DNN outperforms the existing approaches by improving the network resilience by maintaining higher energy efficiency.
Sensor Network , link-based prediction , topological analysis , linkage identification , labelling , generalization , Kernel-based Deep Neural Networks
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