Dr. M. Prakash
@vmkvasc.edu.in
Professor & Head, Department of Computer Science
Vinayaka Mission's Kirupananda Variyar arts and Science College, Vinayaka Mission's Research Foundation Deemed University
Scopus Publications
Scopus Publications
M. Sivaram, M. Kaliappan, S. Jeya Shobana, M. Viju Prakash, V. Porkodi, K. Vijayalakshmi, S. Vimal, and A. Suresh
Springer Science and Business Media LLC
Samuel Raj Samuel Raj, M. V. Prakash, T. Prince, K. Shankar, V. Varadarajan and Fredi Nonyelu
Web applications are utilized on an extensive scale across the globe and it handles sensitive individual information of users. Structured Query Language (SQL) Data Inference (DI) and injection are procedures that abuse a security defenselessness occurring in the database layer of an application. This research article focuses on website page database security with the help of optimization and encryption methods for Web of Things Environments. Initially, the selected queries in webpage application are injected as per Discrete Bee Colony Optimization (DBCO) procedure. After the Proxy filtering, the injection prevention model is utilized, the injected data with various queries of different special characters are utilized. At long last, the attack gets detected depending on the user query with the assistance of query tree mechanism. Besides, an effective Fully Homomorphic Encryption (FHE) encryption is proposed in the study. From the implementation results, it is to be noted that the proposed method achieved 93.56% security level for the prevented webpage implication-based databases. The effect on the businesses must be comprehended to decrease the risk involved in SQL and DI injection assaults.
M. Viju Prakash, V. Porkodi, S. Rajanarayanan, Mujeebudheen Khan, Banar Fareed Ibrahim, and M. Sivaram
IEEE
This Fog Computing is an extension of Cloud Computing technology to the network edge, which enables a newer breed of services and applications. Resource sharing and resource discovery is considered to be critical for the performance of fog computing applications. In general, the methods to reduce are consumption of energy in heterogonous network is very minimal. Considering the higher consumption of energy in heterogeneous networks as a problem statement, this work proposes a machine learning model to reduce the consumption of energy demand in fog computing Internet of Things (IoT) services. Considering the problem, the machine learning model adopts network density, latency and mobility as its energy constraints and designs an objective function to support the lower energy consumption in the network. The simulation of the proposed method is carried out between the proposed and existing methods in terms of various performance metrics. The result shows that the proposed machine learning method in Fog IoT environment is efficient in conserving the energy than the other methods.