The rapid development of technology reveals several safety concerns for making life more straightforward. The advance of the Internet over the years has increased the number of attacks on the Internet. The IDS is one supporting layer for data protection. Intrusion Detection Systems (IDS) offer a healthy market climate and prevent misgivings in the network. Recently, IDS has been used to recognize and distinguish safety risks using Machine Learning (ML). This paper proposed a comparative analysis of the different ML algorithms used in IDS and aimed to identify intrusions with SVM, J48, and Naive Bayes. Intrusion is also classified. Work with the KDD-CUP data set, and their performance has been checked with the WEKA software. A comparison of techniques such as J48, SVM, and Naïve Bayes showed that the accuracy of j48 is the higher one which was (99.96%).
E-Government services have become more widely available in developing countries in recent years. This is beneficial for all stakeholders, especially for people, because it enables the facilitation of government services and contacts with citizens, which can then be evaluated for efficiency and effectiveness. Additionally, as internet use and digitalization have expanded, governments worldwide have taken the essential steps toward E-Governance, integrating government procedures with information technology. Despite this encouraging trend, there is evidence of limited citizen uptake and use of E-Government services. Electronic government services are implemented as technological initiatives, with the underlying premise that citizens will use them. As a result, citizens' expectations for these services are not realized. This study evaluates current research on E-Government to identify gaps, limitations, and future research paths. A recent study in this area primarily focuses on the national level, with little concentration on the local level. As a result, future research proposals focus on E-Government at the municipal level.
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