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Self-Stopping Worms. Justin Ma, Geoffrey M. Voelker, and Stefan Savage Presented: Khanh Nguyen. Self-Stopping Worms. Another type of spreading worm The goal is to infected as many hosts as possible until it reach a target population then stop.
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Self-Stopping Worms Justin Ma, Geoffrey M. Voelker, and Stefan Savage Presented: Khanh Nguyen
Self-Stopping Worms • Another type of spreading worm • The goal is to infected as many hosts as possible until it reach a target population then stop. • This would make it harder to identify the presence of infected hosts. • PROBLEM: how do these independent worms know when to stop?
Overview • Self-Stopping Worms Algorithms • Random Scanning Strategy • Permutation Scanning Strategy • Evaluation
Self-Stopping Worms Algorithms(Random scanning) • Greedy: An infected node infects as many hosts as possible without stopping • Blind-k: An infected node deactivates w/ probability 1/k at the end of each timestep • Non-Exchange, Non-Estimating Strategies • Based on The Distributed systems literature • dI/dt = γ/A(N-I)a and da/dt = γ/A(N-I)a – (1/k)a • a(I) = I + (1/k)(A/γ)log(1-I/N), ex: A=232, N= 217, γ=4,000, resulted: 97.8% infected • PROBLEM: known A, N, γ prior to infection to get a good k value
Self-Stopping Worms Algo. (cont.)(Random scanning) • Stop-k: Stop with probability 1/k after redundant hit. • Infection-status feedback • da/dt = γ/A(N-I)a – (1/k)(γI/A)a • A(I) = (k+1)/k*I + (N/k)log(1-I/N). Ex: k=3, N=2^17, infected population = 98% • Tree: Stop after infecting k new hits on vulnerable
Self-Stopping Worms Algo. (cont.)(Random Scanning) • Sum-Count: • An infected host keeps 2 counters: one for the number of vulnerable hosts it has contacted H, one for the number of scans it has produced S. • Nest = HA/S
Self-Stopping Algorithms (cont.)(Random Scanning) • Bitmap: • Uses 2 bitmaps, each w/ size of A bits • Bitv records the vulnerable hosts it has attempted to infect. • Bits records the hosts it has scanned. • Nest = bitsset(Bitv)*A/bitsset(Bits) • Disadvantage: large amount of memory required
Self-Stopping Algorithms (cont.)(Random Scanning) • Sum-Count-X: Operates like Sum-Count, except that when node A contacts w/ node B, then the HA + HB and SA + SB • Bitmap-X: Operates like Bitmap, except that when node A contacts w/ node B, Bitsv,A U Bitsv,B and Bitss,A U Bitss,B
Self-Stopping Worms Algor. (cont.)(Permutation scanning) • Greedy Permutation: If the host achieves a redundant hit, it will randomly choose a new seed and continue. • Stop-k Permutation: same as Stop-k • Sum-Count-X Permutation: Same as Sum-Count-X, except with the reseed-upon-redundant-hit policy • Partitioned Permutation: Kind of like divide and conquer. Give up half of the unscanned spaces to the newly infected descendant. Stops when reaching its interval (found a redundant hit)
Evaluation • Basic Heuristics • Blind-k (k=32), Stop-k (k=3) and Tree (k=50) • A=2^32, N=2^17, γ =4,000 • Would infect about 98% of the vulnerable hosts • Dynamic Heuristics • Sum-Count and Sum-Count-X • Compared them against Greedy, Blind-32, and the ideal heuristics: Know-NI, Know-N, and Know-I
