the topology of covert conflict shishir nagaraja, ross anderson cambridge university
Post on 21-Dec-2015
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TRANSCRIPT
Topology and Resilience
• Many real-world networks can be modeled as scale-free – social contacts, disease spread, spread of computer viruses
• Power-law distribution of vertex order, often arising from preferential attachment
• Highly-connected nodes greatly enhance connectivity
• This gives resilience against random failure
Topology and Vulnerability
• Although power-law vertex order distribution gives resilience to random failure, it makes the network vulnerable to targeted attack
• If you attack high-order nodes, the network is rapidly disconnected (Albert, Jeong and Barabási, 2000)
• Example: Sierra Leone HIV/AIDS program treated prostitutes first – only 2% of population infected (vs 40% in Botswana)
Topology and Vulnerability (2)
• Music companies target high-order nodes in peer-to-peer networks (prolific uploaders)
• More traditional example: if you conquer a country, subvert or kill the bourgeoisie first
• What about the dynamic case, e.g. insurgency? Police keep arresting, insurgents keep recruiting
• We set out to study this dynamic case, using evolutionary game theory
Simulation Methodology
• After Axelrod’s work on iterated prisoners’ dilemma
• Scale-free network of 400 nodes• At each round, attacker kills 10 nodes –
their selection is his strategy• Defender recruits 10 more, then
reconfigures network – how he does this is his strategy
• Iterate search for defense, attack strategy
Naïve Defenses Don’t Work!
• Basic vertex-order attack – network dead after 2 rounds
• Random replenishment – 3 rounds
• Scale-free replenishment – 4 rounds
Evolving Defense Strategies
• Black – scalefree replenishment
• Green – replace high-order nodes with rings
• Cyan - replace high-order nodes with cliques
• Cliques work very well against the vertex-order attack
Evolving Attack Strategies• Centrality
attacks are the best counter we found to clique-based defenses
• Rings: G, B cliques: C, M
• Vertex-order attack: B, G, C
• Attack using centrality: R, B, M
Next Evolution …
• Combine two defensive strategies – yellow graph is delegation plus cliques
• Modern terror network?
• 3rd-generation music-sharing network?
What this teaches
• People set out to make peer-to-peer systems robust by arranging the nodes in rings. This didn’t work. Clubs do work
• We have some insight into why insurgents organise themselves in cells
• We can model strategies for wiretapping, surveillance, counterinsurgency …
• What about biology?
Biological Robustness
• Redundancy via homologous genes makes an organism better able to evolve (phenotypic changes less often lethal)
• This evolvability is an important element of robustness (Hiroaki Kitano, Nature, Nov 2004, pp 826–837)
• What we call ‘cells’ biologists think of as conserved clusters, the bows in bow-tie networks, or evolutionary capacitors
• Our work may give an insight into the evolution of hierarchical modularity
Conclusion
• We’ve built a bridge between network analysis and evolutionary game theory
• Using our simulation methodology, we get insights into why revolutionaries use cells, the effects of modern policing, and more
• Simulations let us explore many new attack and defense strategies
• Implications for all sorts of networks – computer, social, political … biological?