Intrusion Detection System (IDS) has increasingly become a crucial issue for computer and network systems. Optimizing performance of IDS becomes an important open problem which receives more and more attention from the research community. In this work, A multi-layer intrusion detection model is designed and developed to achieve high efficiency and improve the detection and classification rate accuracy .we effectively apply Machine learning techniques (C5 decision tree, Multilayer Perceptron neural network and Naïve Bayes) using gain ratio for selecting the best features for each layer as to use smaller storage space and get higher Intrusion detection performance. Our experimental results showed that the proposed multi-layer model using C5 decision tree achieves higher classification rate accuracy, using feature selection by Gain Ratio, and less false alarm rate than MLP and naïve Bayes. Using Gain Ratio enhances the accuracy of U2R and R2L for the three machine learning techniques (C5, MLP and Naïve Bayes) significantly. MLP has high classification rate when using the whole 41 features in Dos and Probe layers.

In recent years, the development and demand of multimedia product grows increasingly fast, contributing to insufficient bandwidth of network and storage of memory device. Therefore, the theory of data compression became more significant for reducing data redundancy to save more hardware space and transmission bandwidth. Cloud computing on the other hand; provides elastic services, high performance and scalable data storage to a large and everyday increasing number of healthcare users. Today, clouds are mainly used for handling highly intensive computing workloads and for providing very large data storage facilities. Both goals are combined with a third goal of potentially reducing healthcare data storage cost. In this research, distributed cloud storage that can interact with many cloud providers was used as a backend while hybrid image compression/decompression technique was used in the front end.

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Abstract-Governments in developing countries are increasingly making efforts to provide more access to information and services for citizens, businesses, and civil servants through smart devices. However, providing strategically high impact m-services is facing numerous challenges, such as complexity of different mobile technologies, creating secured networks to deliver reliable service, and identifying the types of services that can be easily provided on mobile devices. Those problems could be solved by applying cloud computing model to the business process of E-government to build a government cloud. This research, proposes an environment for citizens to have greater access to their government and, in theory, makes citizen-to-government contact more inclusive. In addition, it examines an application that allows anyone to report and track non-emergency issues via the internet. It can also encourage citizens to become active in improving and taking care of their community by reporting issues in their neighborhood in order to improve the Egyptian e-government development index.

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Mobile Ad-hoc networks are characterized as networks without any physical connections. In these networks there is no fixed topology due to the mobility of nodes, interference, multi-path propagation and path loss. One particularly challenging environment for multicast is a mobile ad-hoc network (MANET), where the network topology can change randomly and rapidly, at unpredictable times. As a result, several specific multicast routing protocols for MANET have been proposed.[1].The objective of this paper is to study the effects of mobility models on the new proposed secured and enhanced reliable Ad Hoc Multicasting Protocol (SERAMP). SERAMP is a new technique to be used for Multicasting in Ad-Hoc Networks and to solve the security problems associated with multicasting in Ah-Hoc Networks. The proposed protocol added two parameters to secure the network, the first parameter is the encryption of the message using random key for the selection of the encryption algorithm, and the second parameter is to use the same random key to calculate the authentication code of the message [2]. This paper applies the proposed secured protocol for the previous work and a comparative study has been made between the proposed secured enhanced and reliable Ad Hoc Multicasting Protocol under the two mobility models, Random Way Point Mobility Model and Reference Point Group Mobility Model.

Over the past several years, the Internet environment has become more complex and un-trusted. Enterprise networked systems are inevitably exposed to the increasing threats posed by hackers as well as malicious users internal to a network. Intrusion Detection System (IDS) technology is one of the important tools used now-a-days, to detect such threats, which is a predictable element of the computer network system. Various IDS techniques has been proposed, which identifies and alarms for such threats or attacks. Data mining provides a wide range of techniques to classify these attacks. Today's IDS faces a number of key challenging issues. The challenges like detect malicious activities of the large amount of network traffic. The main challenging if some attacks were sneaking as normal connection. [1] This paper provides a proposed system which performs well when compare to other IDS also introduce a comparative study of strong and weak points with other system on the attack detection rate of these existing classification techniques.