ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System

Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: the external intruders who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. Due to the fact that it is more and more improbable to a system administrator to recognize and manually intervene to stop an attack, there is an increasing recognition that ID systems should have a lot to earn on following its basic principles on the behavior of complex natural systems, namely in what refers to self-organization, allowing for a real distributed and collective perception of this phenomena. With that aim in mind, the present work presents a self-organized ant colony based intrusion detection system (ANTIDS) to detect intrusions in a network infrastructure. The performance is compared among conventional soft computing paradigms like Decision Trees, Support Vector Machines and Linear Genetic Programming to model fast, online and efficient intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special track at WSTST 2005, Muroran, JAPA

Development of Manufacturing Cells Using an Artificial Ant-Based Algorithm with Different Similarity Coefficients

Although there exists several ways of solving the cellular manufacturing problem, including several ant-based algorithms, many of these algorithms focus on obtaining the best possible answer instead of efficiency. An existing artificial-ant based algorithm AntClass, was modified so that it is easier to manipulate. AntClass uses Euclidean vectors to measure the similarity between parts, because similarity is used to group parts together instead of distances, the modified version uses similarity coefficients. The concept of heaping clusters was also introduced to ant algorithms for cellular manufacturing. Instead of using Euclidean vectors to measure the distance to the center of a heap, as in the AntClass algorithm, an average similarity was introduced to measure the similarity between a part and a heap. The algorithm was tested on five common similarity coefficients to determine the similarity coefficient which gives the better quality solution and the most efficient process

Swarms on Continuous Data

While being it extremely important, many Exploratory Data Analysis (EDA) systems have the inhability to perform classification and visualization in a continuous basis or to self-organize new data-items into the older ones (evenmore into new labels if necessary), which can be crucial in KDD - Knowledge Discovery, Retrieval and Data Mining Systems (interactive and online forms of Web Applications are just one example). This disadvantge is also present in more recent approaches using Self-Organizing Maps. On the present work, and exploiting past sucesses in recently proposed Stigmergic Ant Systems a robust online classifier is presented, which produces class decisions on a continuous stream data, allowing for continuous mappings. Results show that increasingly better results are achieved, as demonstraded by other authors in different areas. KEYWORDS: Swarm Intelligence, Ant Systems, Stigmergy, Data-Mining, Exploratory Data Analysis, Image Retrieval, Continuous Classification.Comment: 6 pages, 3 figures, at http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_45.htm

Swarm Intelligence Optimization Algorithms and Their Application

Swarm intelligence optimization algorithm is an emerging technology tosimulate the evolution of the law of nature and acts of biological communities, it has simple and robust characteristics. The algorithm has been successfully applied in many fields. This paper summarizes the research status of swarm intelligence optimization algorithm and application progress. Elaborate the basic principle of ant colony algorithm and particle swarm algorithm. Carry out a detailed analysis of drosophila algorithm and firefly algorithm developed in recent years, and put forward deficiencies of each algorithm and direction for improvement

Metaheuristics for the Order Batching Problem in Manual Order Picking Systems

In manual order picking systems, order pickers walk or drive through a distribution warehouse in order to collect items which are requested by (internal or external) customers. In order to perform these operations effciently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic approaches for the solution of this problem; the rst one is based on Iterated Local Search, the second one on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed approaches are benchmarked against classic solution methods. It is demonstrated that the proposed methods are not only superior to existing methods, but provide solutions which may allow for operating distribution warehouses signicantly more effcient.Warehouse Management, Order Picking, Order Batching, Iterated Local Search, Ant Colony Optimization