Duo to limited energy of the sensor nodes which forming the wireless sensor network (WSN), and almost deployed in harsh environment, therefore it is very important to minimize the consumption of the sensor node’s energy. For this reasons designing an energy efficient clustering and scheduling of sensor nodes considered the most efficient methods for extending the WSN’s lifetime. In this paper, we have proposed Genetic algorithm based methods for clustering and scheduling. The proposed methods have two stages; in the first stage GA is used for cluster formation where the chromosome is represented by using the sensor node’s position. While in the second stage GA is used for selecting a minimum number of nodes while maintaining the full coverage and the connectivity of the selected nodes. The simulation result shows that the proposed algorithm (AGA) is more efficient than the existent algorithms in terms of number of first node die per round, number of a live nodes, and energy consumption.

The openness of business toward telecommunication network in general and internet in particular is performed at the prize of high security risks. Every professional knows that the only way to secure completely a private network is to make it unreachable. However, even if this solution was undertaken for many years, nowadays it is not possible to close private network especially for business purpose. Thus security management becomes an important issue that must be considered carefully.This research concentrates on one particular aspect. where the networks becomes more complex (number of machines, number of users, number of connections....), making them more vulnerable to various kinds of complex security attacks. Therefore the Intrusion Detection Systems (IDSs) require more advanced models to deal with these requirements. Multi-Agent System (MAS) is suggested to provide powerful for the modeling and development of complex systems for detecting attacks. This can be done by depending on decomposition ofthe system into several interacting and autonomous entities called agents. Agent is an entity functions continuously an autonomously in an environment in which other processes take place and other agents exist.

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This chapter aims to build a proposed Wire/Wireless Network Intrusion Detection System (WWNIDS) to detect intrusions and consider many of modern attacks which are not taken in account previously. The proposal WWNIDS treat intrusion detection with just intrinsic features but not all of them. The dataset of WWNIDS will consist of two parts; first part will be wire network dataset which has been constructed from KDD'99 that has 41 features with some modifications to produce the proposed dataset that called modern KDD and to be reliable in detecting intrusion by suggesting three additional features. The second part will be building wireless network dataset by collecting thousands of sessions (normal and intrusion); this proposed dataset is called Constructed Wireless Data Set (CWDS). The preprocessing process will be done on the two datasets (KDD & CWDS) to eliminate some problems that affect the detection of intrusion such as noise, missing values and duplication.

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Routing is a technique used for choosing the best network’s path and forwarding the data over the selected path. This paper investigates the enhanced routing algorithms for wire/wireless networks through making a deep study of the most new routing algorithms in those networks, then analyzing these algorithms to examine the efficiency and effectiveness of the analyzed algorithms. Moreover, the paper deals with OSPF for the wired network and AODV for the wireless network. The emphasis of this research paper is concentrated on the survey in routing algorithms that used in wired and wireless networks such as OSPF and AODV because such algorithms are the best suitable kinds for the two types of Networks. The next subsection describes the basic features of these protocols. This paper also focuses on the common points in wire/wireless routing algorithms and using machine learning techniques for enhancements and improvements.

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Unwanted e-mails became one of the most risk experienced by e-mail users, which may be either harmless or e-mails that represent a threat to the internet. Filtering systems are used to filter e-mail messages from spam. This paper introduces a proposed hybrid system to filter the spam; the proposal hybrid Ant Colony System (ACS) and Naive Bayesian (NB) classifier.Where, ACS will depend on the Information Gain (IG) as a heuristic measure to guide the ants search to select the optimal worst features then omitting these features. The remind features will be the subset which is used to train and test NB classifier to classify whether the mail message spam or not. The proposal is experimented on spambase dataset, and the results show that; the accuracy, precision and recall with NB which use a subset of features extracted by proposing IG-based ACS is higher than the traditional NB with all set of features.