Ayman I. Madbouly

Higher education quality assessment model: towards achieving educational quality standard

This paper presents a developed higher education quality assessment model (HEQAM) that can be applied for enhancement of university services. This is because there is no universal unified quality standard model that can be used to assess the quality criteria of higher education institutes. The analytical hierarchy process is used to identify the priority and weights of the model criteria and their alternatives. The model has 3 levels with 8 main objectives and 53 alternatives. It included e-services criteria, which is one of the recent modern university components, in addition to new sub-criteria for enhancing the model. It produces important recommendations for university higher authorities for achieving demanded quality services. A questionnaire was developed to examine the quality criteria for evaluating the model at King Abdulaziz University, as an applied case study. The model proposed is flexible and can be applied in many other universities.

Relevant Feature Selection Model Using Data Mining for Intrusion Detection System

Abstract — Network intrusions have become a significant threat in recent years as a result of the increased demand of computer networks for critical systems. Intrusion detection system (IDS) has been widely deployed as a defense measure for computer networks. Features extracted from network traffic can be used as sign to detect anomalies. However with the huge amount of network traffic, collected data contains irrelevant and redundant features that affect the detection rate of the IDS, consumes high amount of system resources, and slowdown the training and testing process of the IDS. In this paper, a new feature selection model is proposed; this model can effectively select the most relevant features for intrusion detection. Our goal is to build a lightweight intrusion detection system by using a reduced features set. Deleting irrelevant and redundant features helps to build a faster training and testing process, to have less resource consumption as well as to maintain high detection rates. The effectiveness and the feasibility of our feature selection model were verified by several experiments on KDD intrusion detection dataset. The experimental results strongly showed that our model is not only able to yield high detection rates but also to speed up the detection process.

Enhanced relevant feature selection model for intrusion detection systems

With the increased amount of network threats and intrusions, finding an efficient and reliable defence measure has a great focus as a research field. Intrusion detection systems IDSs have been widely deployed as effective defence measure for existing networks. IDSs detect anomalies based on features extracted from network traffic. Network traffic has many features to measure. The problem is that with the huge amount of network traffic we can measure many irrelevant features. These irrelevant features usually affect the performance of detection rate and consume the IDSs resources. In this paper, we proposed an enhanced model to increase attacks detection accuracy and to improve overall system performance. We measured the performance of the proposed model and verified its effectiveness and feasibility by comparing it with nine-different models and with a model that used the 41-features dataset. The results showed that, our enhanced model could efficiently achieves high detection rate, high performance rate, low false alarm rate, and fast and reliable detection process.

Agent-based Managing for Grid Cloud System — Design and Prototypal Implementation

Here, we present the design and architecture of an Agent-based Manager for Grid Cloud Systems (AMGCS) using software agents to ensure independency and scalability when the number of resources and jobs increase. AMGCS handles IaaS resources (Infrastructure-as-a-Service — compute, storage and physical resources), and schedules compute-intensive jobs for execution over available resources based on QoS criteria, with optimized task-execution and high resource-utilization, through the capabilities of grid clouds. This prototypal design and implementation has been tested and shown a proven ability to increase the reliability and performance of cloud application by distributing its tasks to more than one cloud system, hence increase the reliability of user jobs and complex tasks submitted from regular machines.

SCADM: A Developed Smart Classroom Acoustics Design Model for Enhancing Educational Environment@@@نموذج مطور لتصميم الصوتيات داخل الفصول الدراسية الذكية لتحسين البيئة التعليمية

The quality and efficiency of learning and teaching processes in academic universities depend on many important factors. Acoustic quality within the learning environment of the classroom and its surroundings is one of the most important factors that affect the quality of the learning process. Several studies and researches focused on the subject of acoustical comfort in university classrooms. In this paper we study smart classroom acoustics design issues required to achieve high quality acoustics conditions. These designing issues include inside and outside noise sources, designing for optimum reverberation time, selection of sound insulation and acoustical treatment materials, designing for speech intelligibility, and auralization inside the KAU classrooms. The aim is to suggest a developed smart classroom acoustics design model (SCADM) that can be used by architects, acoustics engineers and designers in an early stage of classroom design in order to achieve the acoustical conditions of KAU university classrooms. The goal of this research is to raise the quality and efficiency of the educational environment to reach an excellent learning environment, and hence increasing students learning outcomes.