Artificial Life

“Perhaps the greatest significance of the computer lies in its impact on Man’s view of himself… The computer aids him to obey, for the first time, the ancient injunction Know thyself.”

—Herbert Simon (Nobel Laur., 1978) Group Members

Amit Arora

Manpreet Singh

Varun Garg

Vishaal Jatav

RoadMap

Define Artificial Life

Artificial Immune Systems (AIS)

Algorithms of Artificial Immune Systems

Applications of AIS

Danger Theory

Conclusion

What is Life ?

State of a functional activity and continual change, before death (defined complimentary as end-of-life).

Characterized by the capability to: Reproduce itself, Adapt to an environment in a quest for survival, and Take Actions independent of exterior agents.

Definition of Artificial Life Artificial Life is “the study of man-made systems that

exhibit behaviours characteristic of natural living systems” (Langton)

Artificial life, also known as alife or a-life, is the study of life through the use of human-made analogues of living systems. ( en.wikipedia.org/wiki/Artificial_life )

A field of study that aims to discover the essential nature and universal features of "life": not only life as we currently know it, but life as it could be , whether on earth, within computers, or elsewhere , and in whatever shape or form that it may be found or made within our universe.

Motivation Agent Based Modelling

Economics , Ecological Resource Management Infectious Diseases Societal Structure and Dynamics Consumer Market flows and Traffic Flows

Robotics Gives rise to various classes of Algorithms

Swarm Intelligence Artificial Immune Systems Neural Networks Genetic Algorithms

Ultimate Goal

Ultimate aim is to replicate all life processes

Commonly referred to as CYBORG Cybernetic Organism

Artificial Immune Systems

Artificial Immune Systems Our body’s immune system is a perfect example of a

learning system.

It is able to distinguish between good cells and potentially harmful ones(Antigens).

Artificial Immune Systems (AISs) are learning and problem solvers based on our own immune systems

AISs have been used to solve a wide variety of problems including: Computer Security, Pattern Recognition, Bridge Fault Detection Data Mining

Pathogens are the germs that cause infection in the body

B cells are the detecting antibodies

T Cells are generated in B Cells that attack the Antigens/Pathogens

Memories of the previous infections are retained

Immune System Overview

Immune Systems - basic

Immune System principles Immune network theory

Network of B cells

Negative Selection Creation of detector set

Clonal Selection theory Cloning of fit population

Generalization

Parallel to a Self learning System

Initial set of cases used as training data

Self Learning System

Past experiences used

Artificial Immune Networks

Network of B cells Artificial Recognition Ball (ARB) used to

represent similar B cells ARB Network creation procedure

Matching ARB found for the newly created B cell B cell deleted if no matching ARB found Empty ARBs deleted Two B cells linked if the affinity value is less than

Network Affinity Threshold (NAT)

Negative Selection

Concept of Self and non-Self Antibodies should not react with body cells Analogously detector set should not detect

self cells Procedure

Randomly generate detector Cells Destroy any cell that matches self cells Accept it otherwise

Clonal Selection

Fit cells are allowed to grow in number Unfit are slowly removed Cloning is directly proportional to the fitness Mutation is inversely proportional to fitness Procedure

calculate the fitness select K best fit clone them proportional to their fitness mutate them inversely proportional to fitness

Fitness Calculation

Fitness Calculationf stimulationA : AXB −R

f stimulationA a,b=g f affinitya,b f stimulationB : BXB −Rf suppressionB : BXB − R

F b =∑ f stimulationA a,b ∑ f stimulation

B b,b' ∑ f suppressionB b,b'

Overall Algorithm1. Select Randomly a set P of cells2. Apply negative selection

Removes self detecting cells

3. Clonal Selection1. Calculate fitness2. Select best fit ones3. Clone and mutate accordingly

4. Network formation1. Assign ARBs2.Inter Link ARBs

5. Repeat till termination condition

Modelling a Problem in AIS Symbols

Representation of measurements of the system Pattern/Encoding

Simple structure of symbols. Easy to sort.

Self Set List of Patterns that represent normal functioning. Obtained by training data.

Detector Set (B-Cell) List of patterns that represent abnormal functioning. Grows progressively by Learning.

AIS – Application : Bridge Fault Detection

Bridge Fault detection. Bridge is analogous to the Human Body. Vibrations caused in the bridge are antigens. Self-Set contains safe patterns (e.g. cars, trucks

etc). Detector-Set (B-Cell) contains unsafe/dangerous

vibrations (e.g. very heavy trailers, earthquake, etc).

AIS – Application in Data Mining Movie Recommender System.

Server is the Human Body. Incoming requests are the antigens. Encoding is: User = ({id

1,score

1},....,{id

n,score

n}).

Selection of Similarity Measure (correlation). Create clusters based on the correlation measure. AIS keeps on growing progressively by putting the

new user into the relevant clusters. Person interested in entertainment may also be

interested in (say) cricket.

AIS– Applications in Cyber Security Intrusion Detection System

Computer Network is the Human Body. Packets floating in the network are Antigens (B-Cells). Encode each data packet transferred as [<protocol><source ip><source port> <dest ip><dest port>] Common antigens are known (common connections). New connections activate the antigen, reporting the sysad. If sysad smells a threat

He applies the patch. Declares this antigen as SAFE to the Security System. Network is Immunized.

Else (false alarm) Declares this antigen as SAFE to the Security System.

Comparison with Genetic Algorithms (GA) and Neural Networks (NN)

Evolution/

Learning

LearningEvolutionDynamics

EncodingArtificial

Neurons

Chromosome

strings

Components

Discrete/

Networked

Networked

components

Discrete

components

Structure

Component

Affinity

Neuron

Activities

CrowdingThreshold

activity

AISN.N.G.A.Property

Self and Non-Self have been the classifications in the traditional theories of Immune System.

Danger Theory argues the concept as IS differentiates only “some self” from “some non-

self”. The definition of self changes with changes in human

body Danger theory proposes

Foreign invaders that are dangerous stimulate danger signals by initiating cell stress or death.

IS manipulates these danger signals to recognise the danger zone and then evaluates the danger.

Danger Theory

Concepts in Danger Theory Danger Signals can be classified as

Apoptotic alerts(A alerts) are low-level alerts that could result from legit actions, but could also be the preparations of an attack.

Necrotic alerts(N alerts) relate to actual damage caused by a successful attack.

Danger Zone Danger alerts are transmitted to the Immune System. IS can quantify the degree of alert and indicate the strength of

possible intrusion scenarios. If there are strong indication of intrusion IS activates other

sensors that are spatially, logically or temporally near the original sensor emitting the danger signal.

Danger Evaluation IS makes a decision whether to activate a sensor or not.

Anomaly detection in file systems by DT To the file systems, anomaly means unusual change or creation of

the files, which can be caused by computer viruses, hacker intrusion, or some incidental errors.

Fails in traditional Anomaly detection produces a large number of false flags (normal activities being tagged

as abnormal). unable to detect novel attacks. Rapid development in computer hardware and software not taken

into account. Correlation in DT and File systems

Human Body : The computer file system A Signal : Reading and writing of a file N Signal : Crashing of a file

Anomaly detection in file systems by DT (Contd...)

Correlation in DT and File systems Danger Zone: It receives the file information and creates a

dynamic time neighbourhood of the changed files. It analyses the information in the neighbourhood, and sends the result to the danger evaluation module.

Danger Evaluation: Gathering the information needed, a decision is made whether to send a danger signal or not. At the same time, a TCS (Threshold Control Signal) is sent back to the neighbourhood monitor, so that it can dynamically control its detection thresholds.

Anomaly detection in file systems by DT (Contd...)

Conclusions Artificial Immune Systems

Highly distributed Highly adaptive Has the ability to continually learn about new

encounters. Artificial Life

Has Contributed a number of significant Algorithms Has applications in a number of fields. Has innumerable unexplored areas

Bibliography Alife wikipeida the free encyclopedia ( http://en.wikipedia.org/wiki/Artificial_life )

Dasgupta, D (Editor). Artificial Immune Systems and Their Applications, ISBN 3-540-

64390-7, Springer-Verlag, 1999.

Aickelin, U. and Cayzer, S., 2002c, The danger theory and its applicatioto artificial

immune systems, in: Proc. 1st Int. Conf. on Artificial Immune Systems (Canterbury,

UK), pp. 141–148.

Kim, J. and Bentley, P., 2002, Towards an artificial immune systems fornetwork

intrusion detection: an investigation of dynamic clonal selection,Proc. Congress on

Evolutionary Computation 2002, pp. 1015–1020.

Dasgupta, D. Immunity-Based Intrusion Detection Systems: A General Framework. In

the Proceedings of the 22nd National Information Systems Security Conference

(NISSC), October 18-21, 1999.