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

Using Stigmergy to Solve Numerical Optimization Problems

The current methodology for designing highly efficient technological systems needs to choose the best combination of the parameters that affect the performance. In this paper we propose a promising optimization algorithm, referred to as the Multilevel Ant Stigmergy Algorithm (MASA), which exploits stigmergy in order to optimize multi-parameter functions. We evaluate the performance of the MASA and Differential Evolution -- one of the leading stochastic method for numerical optimization -- in terms of their applicability as numerical optimization techniques. The comparison is performed using several widely used benchmark functions with added noise

Emergent Communication: The evolution of simplistic machines using different communication types

The methods of transmitting information may be divided as follows: direct; and, indirect. The âdirectâ method occurs when a creature transmits a signal that other creatures in its local environment can receive. Word of mouth advertising is a form of direct communication. âIndirectâ communication relays a message through the environment. This type of communication is known as stigmergy. Both word of mouth communication and stigmergy require the existence of groups of communicators. It is, however, difficult to analyse a very large number of local interactions that occur in group behaviour. A global phenomenon known as âemergenceâ arises from such behaviour. The phrase ââthe whole is greater than the sum of its partsâ normally describes emergence. In this research, we investigate how the two methods of communicating, direct and indirect (including a combination of these), result in emergent behaviour. In order to establish this outcome we employed the use of agent-based software in which we designed groups of agents to evolve over generations in response to specific situations. The manner in which these agent groups evolve is by a genetic algorithm. This is based on the consumption and collection of resources from the environment - a metric for gauging how well the population performs as a whole. For the purpose of this dissertation, we measure and examine the performance of four styles of the two methods of communication: No Communication, Word of Mouth, Stigmergic and Both (a combination of direct and indirect). We observe the fitness arising through successive generations of agents for each of the four styles and compare the results. The âNo Communicationâ style is markedly the worst performer and is âthe sum of the partsâ in terms of the definition of emergence. The âWord of Mouthâ style is marginally below the best performer but is rated well above that of âNo Communicationâ. The âStigmergicâ style is only the third best performer. Combining the direct and indirect methods yields the best result for the âBothâ style. All the communicating categories, considered âthe wholeâ in terms of the definition for emergence, outperform the âNo Communicationâ style. This demonstrates that emergence occurs when using these communication methods in groups. Keywords: Communication, Emergence, Genetic Algorithms, Group Behaviou

Stigmergy-based Load Scheduling in a Demand Side Management Context

This work proposes an approach, based on a fundamental coordination mechanism from nature, namely stigmergy. The proposed meta-heuristic is utilized to distributively calculate global schedules for a population of customers provided with intelligent devices. These schedules maximize renewable energy sources utilization. Furthermore, this approach is adapted and utilized as a coordination mechanism of autonomous customers to modify their consumption behavior in a real-time optimization context

Generating Compact Wasp Nest Structures via Minimal Complexity Algorithms.

Many models have been developed to explain the process of self organization-the emergence of seemingly purposeful behaviors from groups of entities with limited individual intelligence. However, the underlying behavior that facilitates the emergence of this global pattern is not generally well understood. Our study focuses on different low complexity building algorithms and characterizes how nests are built using these algorithms. Three rules postulated to be functions of wasps\u27 building behavior were developed. First is the random rule, in which there is no constraint per the choice of site to be initiated. The second is the 2-cell rule where only sites with at least two ready walls are initiated. Third, the maxWall rule ensures only sites with the maximum number of ready walls are initiated. This work provides better insight and visualization through simulation into wasps building behavior. This acquired knowledge can be applied to robotics and distributed optimization processes