Profiling and Identification of Web Applications in Computer Network

Characterising network traffic is a critical step for detecting network intrusion or misuse. The traditional way to identify the application associated with a set of traffic flows uses port number and DPI (Deep Packet Inspection), but it is affected by the use of dynamic ports and encryption. The research community proposed models for traffic classification that determined the most important requirements and recommendations for a successful approach. The suggested alternatives could be categorised into four techniques: port-based, packet payload based, host behavioural, and statistical-based. The traditional way to identifying traffic flows typically focuses on using IANA assigned port numbers and deep packet inspection (DPI). However, an increasing number of Internet applications nowadays that frequently use dynamic post assignments and encryption data traffic render these techniques in achieving real-time traffic identification. In recent years, two other techniques have been introduced, focusing on host behaviour and statistical methods, to avoid these limitations. The former technique is based on the idea that hosts generate different communication patterns at the transport layer; by extracting these behavioural patterns, activities and applications can be classified. However, it cannot correctly identify the application names, classifying both Yahoo and Gmail as email. Thereby, studies have focused on using statistical features approach for identifying traffic associated with applications based on machine learning algorithms. This method relies on characteristics of IP flows, minimising the overhead limitations associated with other schemes. Classification accuracy of statistical flow-based approaches, however, depends on the discrimination ability of the traffic features used. NetFlow represents the de-facto standard in monitoring and analysing network traffic, but the information it provides is not enough to describe the application behaviour. The primary challenge is to describe the activity within entirely and among network flows to understand application usage and user behaviour. This thesis proposes novel features to describe precisely a web application behaviour in order to segregate various user activities. Extracting the most discriminative features, which characterise web applications, is a key to gain higher accuracy without being biased by either users or network circumstances. This work investigates novel and superior features that characterize a behaviour of an application based on timing of arrival packets and flows. As part of describing the application behaviour, the research considered the on/off data transfer, defining characteristics for many typical applications, and the amount of data transferred or exchanged. Furthermore, the research considered timing and patterns for user events as part of a network application session. Using an extended set of traffic features output from traffic captures, a supervised machine learning classifier was developed. To this effect, the present work customised the popular tcptrace utility to generate classification features based on traffic burstiness and periods of inactivity for everyday Internet usage. A C5.0 decision tree classifier is applied using the proposed features for eleven different Internet applications, generated by ten users. Overall, the newly proposed features reported a significant level of accuracy (~98%) in classifying the respective applications. Afterwards, uncontrolled data collected from a real environment for a group of 20 users while accessing different applications was used to evaluate the proposed features. The evaluation tests indicated that the method has an accuracy of 87% in identifying the correct network application.Iraqi cultural Attach

The effectiveness of the numbered heads strategy in learning the skills of handling from top and bottom in volleyball for students

The study is summarized in preparing an educational curriculum according to the numbered heads strategy in learning the skill of handling from the top in volleyball for students as one of the modern strategies that actively contribute to encouraging active learning among learners and achieve satisfactory educational results for the teacher, both at the level of learners’ achievement or on the smoothness of its steps and the reflection of its results on The level of the teacher’s performance in the lesson, and the research sample was determined from the fifth stage students at Al-Wathba Model School for Boys in Misan Governorate for the academic year 2021-2022, and they numbered (68) students, and (6 students) were selected for the exploratory sample and (24 students) were chosen. The researchers used the experimental method with two equal groups, and through the results of the research, they concluded that the experimental group outperformed the control group because of the effectiveness of applying the numbered heads strategy in the group’s educational units. The researchers recommended the necessity of applying this strategy for this age group of students, as well as applying it to the rest of the skills the basics of volleyball and other games

Presenting a Model for Locating and Allocating Multi-Period Hubs and Comparing It With a Multi-Objective Imperialist Competitive Algorithm

Recently, air pollution has received much attention as a result of reflections on environmental issues. Accordingly, the hub location problem (HLP) seeks to find the optimal location of hub facilities and allocate points for them to meet the demands between source-destination pairs. Thus, in this study, decisions related to location and allocation in a hub network are reviewed and a multi-objective model is proposed for locating and allocating capacity-building facilities at different time periods over a planning horizon. The objective functions of the model presented in this study are to minimize costs, reduce air pollution by diminishing fuel consumption, and maximize job opportunities. In order to solve the given model, the General Algebraic Modeling System (GAMS) along with innovative algorithms are utilized. The results presented a multi-objective sustainable model for full-covering HLP, and provided access to a hub network with minimum transport costs, fuel consumption, and GHG (greenhouse gas) emissions, and maximum job opportunities in each planning horizon utilizing MOICA (multi-objective imperialist competitive algorithm) and GAMS to solve the proposed model. The study also assessed the performance of the proposed algorithms with the aid of the QM, MID, SM, and NSP indicators, acquired from comparing the proposed meta-heuristic algorithm based on some indicators, proving the benefit and efficiency of MOICA in all cases