Network security is essential in the Internet world. Intrusion Detection is one of the network security components. Anomaly Intrusion Detection is a type of intrusion detection that captures the intrinsic characteristics of normal data and uses it in the detection process. To improve the performance of specific anomaly detector selecting the essential features of data and generating a good decision rule is important. The paper we present proposes suitable feature extraction, feature selection and a classification algorithm for traffic anomaly intrusion detection in using NSLKDD dataset. The generated rules of classification process are initial rules of a genetic algorithm.

No abstract

Wireless networks play an important role in science, including medicine, agriculture, the military, geography, and so on. The main issue with a network of wireless sensors is how to manage resource utilization to extend its lifetime. This paper investigates the various aspects of increased energy usage that may improve network life. Variables related to energy consumption and various performance metrics are investigated in terms of energy efficiency. To investigate how the network’s energy usage can be managed, a quick overview of clustering protocols, routing protocols, MAC protocols, and load balancing protocols is conducted. This paper can provide researchers with an idea of the various parameters that influence energy consumption and what methodologies could be adapted by each parameter to conserve energy, thereby extending the network’s lifetime.

No abstract

No abstract