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Classification of network traffic is an important topic for network management, traffic routing, safe traffic discrimination, and better service delivery. Traffic examination is the entire process of examining traffic data, from intercepting traffic data to discovering patterns, relationships, misconfigurations, and anomalies in a network. Between them, traffic classification is a sub-domain of this field, the purpose of which is to classify network traffic into predefined classes such as usual or abnormal traffic and application type. Most Internet applications encrypt data during traffic, and classifying encrypted data during traffic is not possible with traditional methods. Statistical and intelligence methods can find and model traffic patterns that can be categorized based on statistical characteristics. These methods help determine the type of traffic and protect user privacy at the same time. To classify encrypted traffic from end to end, this paper proposes using (XGboost) algorithms, finding the highest parameters using Bayesian optimization, and comparing the proposed model with machine learning algorithms (Nearest Neighbor, Logistic Regression, Decision Trees, Naive Bayes, Multilayer Neural Networks) to classify traffic from end to end. Network traffic has two classifications: whether the traffic is encrypted or not, and the target application. The research results showed the possibility of classifying dual and multiple traffic with high accuracy. The proposed model has a higher classification accuracy than the other models, and finding the optimal parameters increases the model accuracy.

Locating files in an exact time is considered one of the greatest problems and the tedious process in universities nowadays. This problem becomes greater when the university has a large number of departments and transactions, as well as the documents are moving from one department to another. Especially, developing countries that have many problems and unstable environment and that may lead to lost or damage the important documents that influence on the decision making. Furthermore, the traditional manner not only wasted the time and energy, but also the paper cost for printing copies of required file. And with the advancement of technology and the increase of Internet users, documents are still being sent in Iraqi universities manually between departments. Although the higher education and scientific research ministry was recommended the public universities for using modern technologies during the daily transactions between the departments or amongst the units.  Therefore, this study sought to design and evaluate the prototype system which tracks movements of the documents from one department to another as well as check the completion rate for each department. For providing opportunities to assess how well the use of e-file tracking system meets the needs of management units in universities. The systems implementation research notes the need to fit between tasks, technologies and users.  Thus, this empirical study utilized the task technology fit model for this purpose. The results from selected participants indicated that all the factors significant effect on the employee’s performance in E-file Tracking System, excepted, task characteristics. This study will be contributed to reduce the corruption and enhance the transparency and help the decision-makers make the right decision at the right time.