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The process of monitoring the events occurring in a computer system or network and analyzing them for sign of intrusions is known as intrusion detection system (IDS). This paper presents two hybrid approaches for modeling IDS. Decision trees (DT) and support vector machines (SVM) are combined as a hierarchical hybrid intelligent system model (DT–SVM) and an(More)
Intrusion detection is the process of monitoring the events occurring in a computer system or network and analyzing them for signs of intrusions, defined as attempts to compromise the confidentiality, integrity, availability , or to bypass the security mechanisms of a computer or network. This paper proposes the development of an Intrusion Detection Program(More)
This paper proposes a new perspective for solving systems of complex nonlinear equations by simply viewing it as a multiobjective optimization problem. Every equation in the system represents an objective function whose goal is to minimize the difference between the right and left term of the corresponding equation. An evolutionary computation technique is(More)
The use of intelligent systems for stock market predictions has been widely established. This paper introduces a genetic programming technique (called Multi-Expression programming) for the prediction of two stock indices. The performance is then compared with an artificial neural network trained using Levenberg-Marquardt algorithm, support vector machine,(More)
This chapter presents the biological motivation and some of the theoretical concepts of swarm intelligence with an emphasis on particle swarm optimization and ant colony optimization algorithms. The basic data mining terminologies are explained and linked with some of the past and ongoing works using swarm intelligence techniques. Swarm behavior can be seen(More)
Due to the wide deployment of sensor networks recently security in sensor networks has become a hot research topic. Popular ways to secure a sensor network are by including cryptographic techniques or by safeguarding sensitive information from unauthorized access/manipulation and by implementing efficient intrusion detection mechanisms. This paper proposes(More)
In this chapter, we introduce several nature inspired meta-heuristics for scheduling jobs on computational grids. Our approach is to dynamically generate an optimal schedule so as to complete the tasks in a minimum period of time as well as utilizing the resources in an efficient way. We evaluate the performance of Genetic Algorithm (GA), Simulated(More)
This Chapter summarizes some of the well known stigmergic computational techniques inspired by nature, mainly for optimization problems developed by mimicking social insects' behavior. Some facts about social insects namely ants, bees and termites are presented with an emphasis on how they could interact and self organize for solving real world problems. We(More)