Abstract. The desire Quality of Service (QoS) of Voice over Internet Protocol (VOIP) is of growing importance for research and study Long Term Evolution (LTE) is the last step towards the 4 th generation of cellular networks. This revolution is necessitated by the unceasing increase in demand for high speed connection on LTE networks particularly for under variable mobility speed for VoIP in the LTE. This paper mainly focuses on performance of VOIP and the impact of resource limitations in the performance of Access Networks particularly important in regions where Internet resources are limited and the cost of improving these resources is prohibitive. By determine rate communication quality, is determined by end to end delay on the communication path, delay variation, packet loss. These performance indicators can be measured and the contribution in the access network can be estimated using simulation tool OPNET Modeler in varying mobility speed of the node. The overall performance of VOIP thus greatly improved significantly by deploying OPNET Modeler.

IDS (Intrusion Detection System) is a security component that protects computer and network systems. Variety of methods have been developed to improve the IDS accuracy. One of the most recent approaches is the use of real-time monitoring and irregular activity detection. When an intrusion is detected, a message will be sent to the network administrator. One of the disadvantages of IDS is the possibility of a bad packet by-passing through network traffic. As a result, an improvement on the Artificial Neural Network (ANN) is explored in this study to enhance attack detection in IDS. Standard and attack events are described using the NSL-KDD dataset. In this study, the Magnetic Optimization Algorithm (MOA) is combined with Particle Swarm Optimization (PSO) named PSOMOA, thus to increase the classification rate and achieve high detection accuracy in IDS. MOA is a heuristic optimization algorithm which deals with attraction between particles scattered in the search space and inspired by magnetic field theory in physics. NSL-KDD dataset represented as attacks and normal activities used in this study. Smurf and Neptune attacks are selected for testifying detection and classification ability of attack category of proposed PSOMOA. During the experimentation process, four of the most important features of the dataset were selected. The PSOMOA findings are compared to those of the other form, which employs PSO and MOA. According to the obtained results, the proposed PSOMOA could increase IDS accuracy by up to 99.5%.

Intrusion Detection System is a type of security application that protects computer and network systems. A variety of techniques have been proposed to increase IDS accuracy. Thisresearch study focuses on improving an IDS detection performance by combiningan Artificial Neural Network (ANN) with a Magnetic Optimization Algorithm (MOA), with the goal of increasing the classification rate and achieving high detection accuracy in IDS. The suggested ANNMOA result demonstrated that it is possible to improve IDS accuracy by up to 98.5 percent