Analyze and develop a model for sentimental reviews of e-government services using deep learning algorithms with CNN framework

Alagumuthukrishnan, S.; Nirmalkumar, A.; Devi, G. Naga Rama

doi:10.1063/5.0057936

Cited by 1 publication

(1 citation statement)

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“…In this study, deep learning was employed to effectively manage a variety of cyber security issues, including intrusion detection, malware or botnet identification, phishing, forecasting of cyberattacks, denial of service (DoS), fraud detection, and cyber abnormalities. Because deep learning is more exact, especially when learning from huge security datasets, it has an advantage for building security models [20]. Similarly, Wang and Wang [21] created a set of deep learning models with the goal of automating many e-government services.…”

Section: Introductionmentioning

confidence: 99%

Securing E-Governance Services Based on Two Level Classification Algorithms

Ibrahim¹,

Elmorsy²,

Kashef³

et al. 2023

MMEP

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Due to the expansion of cybercrime and cyberwarfare, the necessity for cyber security has recently expanded substantially. There are several trends in cyber security, but the most important is e-governance. E-government is regarded as one of the most essential platforms for transmitting data and services over the internet that frequently contain valuable and confidential data, making them subject to threats. The majority of egovernance systems rely on public-use services. The paper proposed a framework to detect threats in internet traffic flows. This paper uses a famous dataset that was collected from internet traffic called the UNSW-NB15 dataset, which consists of 307,099 instances. The framework consists of several steps, including pre-processing, identifying a correlation between features, and selecting the best ones. Finally, different machine learning algorithms are used to distinguish the normal traffic from the malware traffic. The findings uncover that SVM achieved very high accuracy (99.16%). Additionally, in the second part, which is called multi-class and consists of two stages, in the first one the study classified the abnormal flows into nine attacks with a lower accuracy of 77.80%. In the second stage with binary classification, the dataset contained both normal and abnormal, and the accuracy improved significantly to 97.48% for SVM.

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Section: Introductionmentioning

confidence: 99%