| Title | Analysis of Efficient Network Security using Machine Learning in Convolutional Neural Network Methods |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Pandey, Amit, Genale, Assefa Senbato, Janga, Vijaykumar, Sundaram, B. Barani, Awoke, Desalegn, Karthika, P. |
| Conference Name | 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC) |
| Date Published | may |
| Keywords | artificial intelligence security, composability, Education, Encryption, Human Behavior, Instruments, IoT technology, learning (artificial intelligence), machine learning, Metrics, Network security, Organizations, pubcrawl, resilience, Resiliency, security methods |
| Abstract | Several excellent devices can communicate without the need for human intervention. It is one of the fastest-growing sectors in the history of computing, with an estimated 50 billion devices sold by the end of 2020. On the one hand, IoT developments play a crucial role in upgrading a few simple, intelligent applications that can increase living quality. On the other hand, the security concerns have been noted to the cross-cutting idea of frameworks and the multidisciplinary components connected with their organization. As a result, encryption, validation, access control, network security, and application security initiatives for gadgets and their inherent flaws cannot be implemented. It should upgrade existing security measures to ensure that the ML environment is sufficiently protected. Machine learning (ML) has advanced tremendously in the last few years. Machine insight has evolved from a research center curiosity to a sensible instrument in a few critical applications. |
| DOI | 10.1109/ICAAIC53929.2022.9793293 |
| Citation Key | pandey_analysis_2022 |
Analysis of Efficient Network Security using Machine Learning in Convolutional Neural Network Methods