You are currently browsing the tag archive for the ‘Bio-Inspired Ant-like Clustering’ tag.

There’s Plenty of Room at the Bottom“, ~ Richard Feynman (referring to NanoTechnology).

There are huge life scales in our world with which we are not acquainted to. While some prefer to wonder about “alien life” on movie theaters simultaneously eating popcorn, right here at Planet Earth, some lakes and rivers are full of them. What you see above is a tiny water flea ‘Crown Thorns‘ photographed by zoologist Jan Michels (Christian Albrecht University in Kiel, Germany). It was nominated as the best microscopic life image of 2009, last week, at BioScapes (short for Biological landscapes – a competition sponsored by Olympus  in order to recognize microscope photos of plants, animals, and other life-forms that capture the “fascinating minutia of life”).

The snaking ridge at top left took top honors in the 2009 BioScapes microscope imaging contest. If water flea parents sense that their habitat is shared by their main predators, tadpole shrimp, the flea offspring sport these pointy crowns – which are unappetizing to the shrimp. Jan Michels, added a dye to reveal the tiny animal’s exoskeleton (green) and cellular nuclei (blue smudges). The blue-and-red dots are one of the animal’s compound eyes, like those of a fly.

This image, kind of remembers me of another one I used in the past for a series of Artificial Intelligence conferences I have held in the past, during 2004 (Budapest, Hungary), 2005 (Muroran, Japan) and 2006 (Jinan, China) (SIP workshop series Swarm Intelligence and Patterns). This image below was used as the conference symbol; a termite head scanned trough SEM (Scanning Electron Microscope) taken by University of Toronto, Canada.

But probably one of the images I most love at this nano-scale  is one  of a red ant grabbing  a tiny electronic circuit board (microchip) on his mouth (Science Museum, UK). Reason is simple. This image (below) could have several readings. By using SEM, image is formed by focusing an electron beam onto the sample surface.  As the beam scans across the surface the sample emits secondary electrons which are then detected and used to modulate the image signal much like a television.  More electrons is translated into a brighter image.  As the beam scans the surface each point is mapped out just as the electron beam in a television maps the image onto the screen.  Here we are able to see all the details of one of natures smallest denizens holding one of mankind’s smallest creations, a silicon microchip (the building blocks of digital electronics).

What’s funny is that ant colonies are known (among many other interesting features) for their remarkable cemetery organization capabilities, that is, their sequential clustering task of corpses and objects (as this microchip below). Ant colonies do show that the coordination and regulation of building activities do not depend on the workers themselves but are mainly achieved by the nest structure: a stimulating object configuration triggers the response of a termite worker, transforming the configuration into another configuration that may trigger in turn another (possibly different) action performed by the same termite or any other worker in the colony.

Ants do all this by simple manipulating objects using stigmergic capabilities. Ants form piles of items such as dead bodies (corpses), larvae, or grains of sand. Initially, they deposit items at random locations. When other ants perceive deposited items, they are stimulated to deposit items next to them, being this type of cemetery clustering action, organization, and brood sorting a type of self-organization and adaptive behavior. Some  bio-inspired branches of computer science use this kind of behaviors to solve highly complex problems, such as Data Mining, Data analysis and classification, Data clustering, Image retrieval, among many others.

Indeed, life on its own is the ultimate science-fiction. And, as Richard Feynman mentioned once, there is plenty of room below!

 

Self-Organized Ant-based clustering results on IDS data (MIT Lincoln Labs) using a full data set with 11982 samples (41 features each) in the initial and final steps.

Self-Organized Ant-based clustering results on IDS data (MIT Lincoln Labs) using a full data set with 11982 samples (41 features each) in the initial and final steps.

[] Vitorino Ramos, Ajith Abraham, ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System,  in Swarm Intelligence and Patterns special session at WSTST-05 – 4th IEEE Int. Conf. on Soft Computing as Transdisciplinary Science and Technology – Japan, LNCS series, Springer-Verlag, Germany, pp. 977-986, May 2005.

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.

(to obtain the respective PDF file follow link above or visit chemoton.org)

[...] People should learn how to play Lego with their minds. Concepts are building bricks [...] V. Ramos, 2002.

@ViRAms on Twitter

Archives

Blog Stats

  • 255,000 hits