DEVELOPMENT OF SECURE MODELS AND ALGORITHMS FOR THE EXCHANGE OF SERVICE MESSAGES BY MESSENGERS

Abstract

Recently, intrusion detection systems (IDS) have been introduced to effectively secure networks. Using neural networks and machine learning in detecting and classifying intrusions are powerful alternative solutions. In this research paper, both of Gradient descent with momentum (GDM)-based back-propagation (BP) and Gradient descent with momentum and adaptive gain (GDM/AG)-based BP algorithms are utilized for training neural networks to operate like IDS. To investigate the efficiency of the two proposed learning schemes, a neural network based IDS is built using the proposed learning algo- rithms. The efficiency of both algorithms is inspected in terms of convergence speed to achieve system learning, and elapsed learning time using various settings of neural network parameters. The result demonstrated that the GDM/AG-based BP learning algorithm outperforms the GDM-based BP learning algorithm.

Mobile Instant Messenger is one of the most application which use to exchange a message between Sender and Receiver over public network, so that it is very important building an application with good level of security aspect and fast communication. This paper propose a secure Mobile IM in excanging data over internet with an virtual network method. In this model, we propose a Mobile Instant Messenger with virtual network. It will implemented in JXTA protocols which establish a virtual network overlay on top of the Internet, allowing peers or device to directly interact and self-organize independently of their network connectivity and domain topology. This method developed with Pipes as virtual communication channels used to send and receive messages between services and applications, the pipe binding consists of searching and connecting the two or more ends of a pipe. This is new approach of IM communication method, that is message must be deliver over virtual network with good level security.