Trust Propagation using Cellular Automata for UbiComp 28 th May 2004 —————— Dr. David Llewellyn-Jones, Prof. Madjid Merabti, Dr. Qi Shi, Dr. Bob Askwith —————— School of Computing and Mathematical Statistics Liverpool John Moores University James Parsons Building Byrom Street Liverpool, L3 3AF, UK {D.Llewellyn-Jones, M.Merabti, Q.Shi,

Problem Traditional security applied to –Individual computers –Domains of computers Improved by allowing multiple computers to work together –Particularly relevant in a UbiComp environment

Trust How can a large number of loosely affiliated devices trust each other? We propose a simple, scalable method for propagating trust in a UbiComp environment Example – using direct connections

Trust If a does not trust b, it is useful to propagate this information Developed to allow distributed analysis of components for maintaining security Could be used –to ensure correct analysis of components (is b returning correct results?) –to prevent viruses spreading (does b have a virus?) –to improve privacy through encryption (is b encrypting data?)

Solution We use a system of cellular automata to maintain and propagate trust information around the network What are cellular automata? –Studied mathematically, e.g. by John von Neumann (1949), Stephen Wolfram (1983) –Used to discretely model differential equations and real world systems –Each cell has a state and transition function –A cell applies its transition function to its state and the state of the cells around it –Every cell does this at each step

Cellular Automata Important properties: –Cells only need to know about a small neighbourhood – e.g. adjacent cells –Transition functions are very simple –With many cells, produces complex emergent behaviour The whole is more than the sum of its parts Example: Conway’s “Game of Life”

Cellular Automata Important properties: –Cells only need to know about a small neighbourhood – e.g. adjacent cells –Transition functions are very simple –With many cells, produces complex emergent behaviour The whole is more than the sum of its parts Example: Conway’s “Game of Life”

Cellular Automata Important properties: –Cells only need to know about a small neighbourhood – e.g. adjacent cells –Transition functions are very simple –With many cells, produces complex emergent behaviour The whole is more than the sum of its parts Example: Conway’s “Game of Life” –Created by John Conway in 1970 –Studied by mathematicians and computer scientists –Very simple rules, but complex result

Cellular Automata How can we use cellular automata? –Each node in the UbiComp environment represents a cell of the cellular automaton –Each node executes a simple transition function –We want to harness the complex emergent properties to manage and distribute the trust information The “Game of Life” network

Cellular Automata For an effective system, we must choose the correct transition function This will determine the emergent behaviour

Emergent Behaviour A security breach causes a reduction of the trust in the node The effect is like pulling down on an elasticated blanket This localises the effect of a security breach

Generating Networks Networks are normally far more complex than nodes arranged in a grid –For an effective system, more complex structures must be considered –Transition function is updated to handle such structures Experiments used the Klemm-Eguíluz method for generating a network topology –Small-World graph –Small average distance between nodes –High clustering coefficient –Scale-free network –Satisfies power-law connectivity distribution

Experimental results Malicious code propagation experiment –Malicious code reproduces itself to neighbouring nodes –Some nodes are able to detect the malicious code –Nodes only spend a certain proportion of time checking for malicious code –If detected, the nodes trust is adversely affected Experiment details –19600 node Klemm-Eguíluz network –Control experiment: same network but no Cellular Automata effect –Initially malicious code added to five random nodes –Experimental run of 2000 cycles

Experimental results 100% enabled, fast propagation

Experimental results 85% enabled, fast propagation

Experimental results 85% enabled, fast propagation, 20% active

Conclusion Initial tests show that trust can be effectively propagated using cellular automata techniques The process relies on the emergent properties of the system There is little theory properly relating transition functions with emergent properties in a network environment We hope to use the system described to allow distributed code analysis in a UbiComp environment

The End Thankyou for your time