Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Defending Sybil Attack in Peer2Peer Networks Distributed Search Techniques Md. Tanvir Al Amin 04 09 05 2064 Shah Md. Rifat Ahsan 10 09 05 2060 Adviser : Dr. Reaz Ahmed Sybil Attack A fundamental problem in distributed systems. honest malicious Single user assumes many fake/sybil identities Already observed in real-world p2p systems launch sybil attack Sybil identities can become a large fraction of all identities “Out-vote” honest users in collaborative tasks 2 Sybil attack Present in both Application level and P2P Networking Attacker creates many fake/sybil identities Many cases of real world attacks : Digg, Youtube Several research works shown how easy it was to subvert DHT like Chord or Kademlia using Sybil Attack Automated sybil attack on Youtube for $147! Defending against Sybil attacks Traditional solutions rely on central trusted authorities Runs counter to open membership policies of OSNs Recent proposals leverage social networks Lots of research activity recently Each optimized under assumptions about the graph structure Each evaluated on different datasets All schemes analyze the graph structure to isolate Sybils SybilGuard [SIGCOMM’06] SybilLimit [Oakland’08] Ostra [NSDI’08] SumUp [NSDI’09] SybilInfer [NDSS’09] Whanau [NSDI’10] MobID [INFOCOM’10] Defending against Sybil attacks Recent proposals leverage social networks Key Insight: Social links are hard to acquire in abundance Look for small cuts in the graph Conversely, look for communities around known trusted nodes Dunbar’s Number Power law node degrees Links difficult to create HOW DO SOCIAL NETWORKS LOOK LIKE SybilGuard: Defending Against Sybil Attacks via Social Networks Sybilguard is a system for detecting Sybil nodes in social graphs. Features of Sybil Guard SybilGuard enables an honest node to identify other nodes Verifier node V can verify if suspect node S is malicious Guaranteed bound on number of sybil groups Guaranteed bound on size of sybil groups Completely decentralize Key Insight: 1. Use a social network to limit Sybils 2.Social links are hard to acquire in abundance 3.Look for small cuts in the graph DBLP Network Dunbar’s number Limits the # of stable social relationships a user can have To less than a couple of hundred Linked to size of neo-cortex region of the brain Observed throughout history since hunter-gatherer societies Roughly reported to be 150 Also observed repeatedly in studies of OSN user activity Users might have a large number of contacts But, regularly interact with less than a couple of hundred of them Power-law node degrees U.S. highways U.S. Airlines 9 Path lengths and diameter all major networks have short path length from 4.25 – 5.88 six degrees of separation Facebook, 4.2 million for Octorber 2007, 6.12 from http://blog.paulwalk.net/2007/10/ 08/no-degrees-of-separation/ 10 Implications of Path lengths and diameter The small diameter and path lengths of social networks are likely to impact the design of techniques for finding paths in such networks 11 Link degree correlations high-degree nodes tend to connect to other high-degree nodes ? OR high-degree nodes tend to connect to low-degree nodes ? In real society: the former theory is true. By virtue of two metrics: the scale-free metric and the assortativity. Suggests that there exists a tightly-connected “core” of the high-degree nodes which connect to each other, with the lower-degree nodes on the fringes of the network. The next question: How big the core is 12 Implications of Link degree correlations Spread of Information “A Measurement-driven Analysis of Information Propagation in the Flickr Social Network” [WWW’ 09] 13 Densely connected core the graphs have a densely connected core comprising of between 1% and 10% of the highest degree nodes such that removing this core completely disconnects the graph. Sub logarithmic growth 14 Densely connected core the graphs have a densely connected core comprising of between 1% and 10% of the highest degree nodes such that removing this core completely disconnects the graph. Sub logarithmic growth 15 Implications of densely connected core Network contains dense core of users Core necessary for connectivity of 90% of users Most short paths pass through core Could be used for quickly disseminating information So 10% at core What about remaining nodes (90% at fringe) 16 What does the structure look like the networks contain a densely connected core of high-degree nodes; octopus and that this core links small groups of strongly clustered, low-degree nodes at the fringes of the network. Mixing time Random walk: choose each hop randomly Mixing time: #hops until uniform probability Fast mixing network: mixing time = O(log n) Sampling by random walks A random walk has o(1) chance of escaping* True when g bounded by o(n/log n) Of r walks, (1-o(1))r = Ω(r) end nodes are good! Can’t distinguish good from bad nodes in set Honest region Sybil region non-escaping path escaping paths Creating Social Link Is Hard Social links maintained over Internet Social network … Social network Honest region Attack edges A malicious user fools an honest user Creates an attack edge Sybil region … Sybil resilience & group attachment theory Sybil schemes find bond groups around a trusted node But, these are only a fraction of all honest nodes Bond groups are hard for Sybils to infiltrate Not the case with identity groups Yu, Kaminsky, Gibbons, Flaxman, Sigcomm 2006 SYBILGUARD Problem Formulation and Objective Social network » n honest human users » 1+ malicious users : multiple sybil identities » SybilGuard enables an honest node to identify other nodes » Verifier node V can verify if suspect node S is malicious SybilGuard Guaranteed bound on number of sybil groups » » Guaranteed bound on size of sybil groups » Divides n nodes into m equivalence classes A group is sybil if it contains 1+ sybil nodes In a group, at most w sybil nodes Completely decentralized » » » An honest node accepts honest nodes with high probability Rejects malicious nodes with high probability Accepts bounded number of sybil nodes Random Routes Foundation of SybilGuard: different from random walk Random route begins at a random edge of a node At every node » For an incoming edge i, there is a unique outgoing edge j » Thus, input to output is one-to-one mapped A node A with d neighbors uniformly randomly chooses a permutation “x1,x2, . . . ,xd” among all permutations of 1,2, . . . ,d. If a random route comes from the ith edge, A uses edge xi as the next hop. SybilGuard Algorithm Attack Model node A: verify node B n honest users: One identity/node each Malicious users: Multiple identities each (sybil nodes) A computes d random routes (length w) B computes d random routes (length w) If d/2 random routes intersects, accept S Else reject S If few attack edges, then a sybil node’s random route is less likely to reach honest region And vice-versa Main Assumptions of SybilGuard Attack edges Honest Nodes Sybil Nodes Properties of Random Routes Convergence » Once two routes merge, they will remain merged Routes are back-traceable There can be only one route with length w that traverses e along the given direction at its ith hop If two random routes ever share an edge in the same direction, then one of them must start in the middle of the other Cycles can exist, but with low probability » Prob. (diameter k cycle) = 1/d(k-2) Sybilguard Algorithm B Steps: 2 Step 1: Choose a verifier (A) and a suspect Bootstrap the(B). network. A and B send out random of a All userswalks exchange certain length signed keys. (2). Lookexchange for intersections. Key implies that both parties are A knows B is not a Sybil human and because multiple paths trustworthy. intersect and they do so at different nodes. A 32 SybilGuard Algorithm, cont. A B 33 SybilGuard Caveats Bootstrapping requires human interaction. Assumes short random walks lie mostly in the honest region Results in poor threshold to colluding attackers. In a million node network ,each attack edge accepts nearly 2000 sybil nodes. In million node network , SybilGuard cannot bound the number of sybils at all if there are > 15,000 attack edges . SybilLimit A Near-Optimal Social Network Defense Against Sybil Attacks SybilLimit A Near-Optimal Social Network Defense Against Sybil Attacks Motivation : To mitigate the problems of SybilGuard. Basic insight : Social network (same as SybilGuard) SybilLimit Novelity : 1. use many random routes but shorter ones. 2. intersect edges not nodes 3. limit how often each edge is used. Identity Registration Each node (honest or sybil) has a locally generated public/private key pair “Identity”: V accepts S means V accepts S’s public key KS NO assumption/need PKI Every suspect S “registers” KS on some other nodes Registration Goals Ensure that sybil nodes (collectively) register only on limited number of honest nodes Still provide enough “registration opportunities” for honest nodes K: registered keys of sybil nodes K: registered keys of honest nodes K K K K K K K K K K K K K K honest region K K sybil region Acceptance Criteria Accept S only if KS is register on K: registered keys of sybil nodes K: registered keys of honest nodes sufficiently many honest nodes K K K K K K K K K K K K K K honest region K K sybil region Key Idea Take random “walks” of w= (log n) hops Honest nodes: likely to remain in honest region* Sybil nodes: must cross an attack edge to reach honest region K K K • Register key at last hop of “walk” K K K K K K K K K K K K K honest region sybil region Verification Procedure AB 1. request S’s set of tails 2. I have three tails AB; CD; EF V S 3.common tail: EF 4. Is KS registered? 5. Yes. V accepts S EF F CD 4 messages involved Tails intersect + key registered Sybil nodes accepted g Attack edges g O n / log n g between n / log n and On / log n g n / log n SybilGuard SybilLimit ( g n log n) ( g log n) unbounded ( g log n) unbounded unbounded SybilInfer: How to Win the Zombie Wars! Prateek Mittal, George Danezis (MSRC Intern) (MSR Cambridge) SybilInfer Work from UIUC and Microsoft Research A centralized algorithm Uses the fast mixing properties of social network to design a Bayesian Classifier Classify nodes Formal Model Assign probabilities of cuts being honest P( X Honest | T ) Using Bayes Theorem, we have that : P ( X Honest | T ) P (T |X Honest ) P ( X Honest ) Z Z X V P(T | X Honest ) P( X Honest ) Next Challenge: Model P(T | X Honest ) Formal Model probX X probXX 1 EXX |V | probX X 1 EX X |V | P(T | X honest ) prob X X X prob XX X prob X X prob N prob N prob N prob N XX XX XX XX XX XX XX XX SYBIL PROOF DHT Distributed Hash Table Interface: PUT(key, value), GET(key)→value Route to peer responsible for key GET( sip://alice@foo ) PUT( sip://alice@foo, 18.26.4.9 ) DHTs are subject to the Sybil attack Attacker creates many pseudonyms Disrupts routing or stabilization s t {IDt} The Sybil attack on open DHTs Brute-force attack Clustering attack Sybil Proof DHT How to build a sybil resilient DHT ? Works from MIT PDOS Group Parallel and Distributed Operating Systems Quest to build Sybil Proof DHT Sybil-resistant DHT routing 2005 A Sybil proof One hop DHT SocialNets 2008 Whanau NSDI 2010 A Sybil proof one hop DHT Motivation: SybilGuard/SybilLimit:Not a DHT, but a “general” Sybil defense Honest node accepts at most O(g log n) Sybils Features : DHTs are subject to the Sybil attack Social networks provide useful information Created a Sybil-resistant one-hop DHT Resistant to g = o(n/log n) attack edges Table sizes and routing BW O(√n log n) Uses O(1) messages to route Basic one-hop DHT design Construct finger table by r random walks Route to t by asking all fingers about t If r = Ω(√n log n), some finger knows t WHP Adversary cannot interfere with routing s {t’s IP address} {know t?} {know t?} {t?} {t?} {t?} {t?} {t?} {forwarded message from s} t r r Properties of this solution Finger table size: r = O( n log n) Bandwidth to construct: O(r log n) bits Bandwidth to query: O(r) messages Probability of failure: 1/poly(n) Chris Lesniewski-Laas M. Frans Kaashoek NSDI 2010 WHĀNAU: A SYBIL-PROOF DISTRIBUTED HASH TABLE Contribution Whānau: an efficient Sybil-proof DHT protocol GET cost: O(1) messages, one RTT latency Cost to build routing tables: O(√N log N) storage/bandwidth per node (for N keys) Oblivious to number of Sybils! Proof of correctness PlanetLab implementation Large-scale simulations vs. powerful attack Social network Honest region Attack edges Sybil regio n … Random walks c.f. SybilLimit [Yu et al 2008] Building tables using random walks c.f. SybilLimit [Yu et al 2008] What have we accomplished? • Small fraction (e.g. < 50%) of bad nodes in routing tables • Bad fraction is independent of number of Sybil nodes key value PUT(key, value) PUT Queue key SETUP Social Network LOOKUP Routing Tables value Routing table structure O(√n) fingers and O(√n) keys stored per node Fingers have random IDs, cover all keys WHP Lookup: query closest finger to target key Zyzzyva Finger tables: (ID, address) Aardvark Key tables: Kelvin (key,value) Keynes From social network to routing tables Finger table: randomly sample O(√n) nodes Most samples are honest ID IP address Honest nodes pick IDs uniformly A B Z C Y D X E W F V U G T H I S J R K Q L P O N M Plenty of fingers near key Sybil ID clustering attack A B Z C Y D X E W F V U G T H I S J R K Q Many bad fingers near key L P O N M [Hypothetical scenario: 50% Sybil IDs, 50% honest IDs] Honest layered IDs mimic Sybil IDs Layer 0 Layer 1 A A D X C Y C Y B Z B Z E W D X F V E W F V U G U G T H T H I S J R K Q L P O N M I S J R K Q L P O N M Every range is balanced in some layer Layer 0 Layer 1 A A C Y B Z B Z D X C Y E W D X F V E W F V U G U G T H T H I S J R K Q L P O N M I S J R K Q L P O N M Two layers is not quite enough Layer 0 Layer 1 A A B Z C Y S R Q L P O N M I J K E W F V T D X E W Ratio = 1 honest : 10 Sybils C Y D X U B Z F V G U H T Ratio = 10 honest : 100 Sybils S R Q L P O N M G H I J K Log n parallel layers is enough Layer 0 X Y Z A B C D E F G H I J W V U T S R Q Layer 1 P O L N M K W V U T S R X Y Z A B C Layer 2 D E F G H I Q P O L N M K J W V U T S R X Y Z A B C Layer L D E F G H I Q P O L N M K J log n layered IDs for each node Lookup steps: 1. 2. 3. Pick a random layer Pick a finger to query GOTO 1 until success or timeout … W V U T S R X Y Z A B C D E F G H I Q P O L N M K J From Social relations to Routing Tables key value PUT(key, value) PUT Queue key SETUP Social Network LOOKUP Routing Tables value Problems Whanau’s goal is to create a Sybil proof DHT Which ensures delivery Whanau uses the idea of random walk in fast mixing graphs Whanau has changed the basic structure of DHT Tables contain O(√n log n) entries !! The DHT has become a one hop DHT But O(√n) entries are insane !! Think of a DHT with 100000000 users How to handle churn ?? OUR IDEA OF A SYBIL PROOF DHT Our Idea We are given a social graph Each node knows about their friends in the social graph Same assumptions about SybilGuard or Whanau Fast mixing graphs Small cut around attack edges o(n/log n) attack edges at most Our Motivation for DHT Isn’t it possible to keep the basic routing features of a DHT while making it sybil resilient? O(log n) table size Lookup should take O(log n) We should use social information to build the DHT Bootstrapping the DHT Here comes the fundamental question How to convert a given social graph into a DHT So that the socially connected nodes are near Socially far nodes are far in the DHT Sybil nodes require significant amount of social engineering to be strongly connected members of a social group A new type of DHT We want to build a DHT Where distance between two nodes in the DHT-Space is related to their social-distance i.e, two friends in the social graph are expected to be onehop distant in the DHT-Space Most of the queries will be through friends Hence, the probability of reaching a Sybil node is less We use the idea of Plexus A novel DHT routing based on linear block codes Plexus: A Scalable Peer-to-Peer Protocol Enabling Efficient Subset Search : Reaz Ahmed and Raouf Boutaba ACM/ IEEE TON Feb 2009 Plexus: Index Clustering Cluster C = set of cluster heads Pattern Linear code, C <n,k,d> Cluster head Cluster head Codeword Generator matrix based routing Advertisement, P advSet(P) C Query, Q qSet(Q) C Q P qSet Q advSetP 85/15 g47 111 0 g120 1 g g g 2 23 021 1 g 220 G G 15 0 0 1 0gE 0 0 g k 02 k g k 1 0 1 1 g11n g 2 n 0 0 1 1 0 1 0 1 1 1 1 g kn0 <7, 4, 3> Hamming code Linear Binary Code C = <n, k, d> linear binary code n: number of bits in a codeword k: dimension 2k codeword in code d: minimum distance between any pair of codeword G e.g., 24=24, 12, 8 Generator Matrix G, g1 g11 g12 g g 2 21 g 22 G g k g k1 g k 2 g1n g 2 n g kn 2k codeword can be formed by applying XOR to any combination of these k codewords. 86/15 Plexus: Routing Table In a complete network each peer is responsible for a codeword Peer with codeword X maintains links to k+1 peers with IDs computed as: X gi 1 i k Xk+1 = X g1 g2 … gk Xi = Xk+1 is used for: Replication Reducing routing cost 87/15 Plexus: Routing Observation: C is closed under operation X , Y C Y X g i1 g i2 g it X21=X2g1 … X23=X2g3 … X2k=X2gk X1=Xg1 X2=Xg2 … Xk=Xgk Example: Route from X to Y where, X231=X23g1 … X235=X23g5 … X23k=X23gk Y X g 2 g3 g5 X2 g3 g2 X g3 X2 g5 X23 g2 X3 g5 X25 X23 X235=Y g5 g3 Y X k 1 X g1 g 2 g k g5 X5 g2 g3 X35 g2 88/15 Strengths of Plexus Routing Hamming distance based clustering & indexing Maximum routing hops (within a subnet) 89/15 ½ K in normal condition ½ K +2 in presence of failure. Disjoint routing paths Source X destination Y XY is disjoint from XYK+1 X Y’ Y Y’K+1 YK+1 Alternate routing paths Suitable for Multicasting Improved fault resilience Improved load balancing Y X YK+1 Social Network to Plexus Now, the problem reduces to assigning appropriate linear block codes to the nodes How to do that ? Naïve Idea All nodes u know their friends F1(u). All nodes u send F1(u) to all of their friends. At this point, Every node u, in addition to F1(u), can calculate its "mutual friend list" for each of its friends. For any two friends u, v : Their mutual friend set is M1 (u, v) F1 (u) F1 (v) Every node u, can also calculate F2(u), its exact twohop distant friend list. F2 (u) F1 ( F1 (u)) F1 (u) {u} Naïve Idea Each node u, sorts their friends according to an "influence metric.“ For each friend v of a node u, Influence(u,v) = Influence of v on u = I (u, v) | M 1 (u , v) | I (u , v) | F1 (u ) | it is highly probable that a sybil node will have very low influence on an honest node via attack edge due to very small number of mutual friends. However not only sybils, but also a common friend of two groups will have low influence on both group (however, this case is not handled in any algorithms) Naïve Idea Each node u, calculates I(u,v) and I(v,u) for all its friends. There are 2*deg(u) such quantities. C(u) = Those nodes for which u has more influence on v than v has on u P(u) = Those nodes for which v has more influence on u than u has on v. and R(u) = Those nodes for which u and v both has same influence on each other C (u ) {v : v F1 (u ), I (v, u ) I (u, v)} P(u ) {v : v F1 (u ), I (u, v) I (v, u )} R(u ) {v : v F1 (u ), I (u, v) I (v, u )} Naïve Idea max{ C(u) } = x = The friend, on which u is maximum influential. However, it doesn’t mean x doesn’t have a friend more influential than u. It means, u does not have a friend on which it has more influence than it has on x. max { P(u) } = y = The friend which has the highest influence on u. It also doesn’t mean y doesn’t have friends on which it has more influence than it has on u. max { R(u) } = z = The friends which has same influence on u as u has on them. Naïve Idea lx = I(x,u) ; Iy = I(u,y) , Iz = I(u,z) = I(z,u) MI = { Ix, Iy, Iz} If Max { MI } = Ix : u is an “influencial” node Iy : u is an “influenced” node Iz : u is an “neutral” node Naïve Idea Action D : If u is “influenceD”, it decides not to generate any ID, and decides to take command from y. It sends a message to y that it has come into his control. Action L : If u is an influentiaL node, it decides to generate ID for u and some of F1(u) and F2(u) Action N : If u is Neutral, then decides Action L or Action D by a uniform bernoulli trial. Now, u generates ID for itself, for those of Gang(u). It will try to keep friend IDs as close as possible, also those of Gang(u) which are friends themselves will get close ID as possible. u will inform all of Gang(u) all the ids generated by it. Members of Gang(u) will take care of id generation of their neighbors But how to handle collision ? Some gossip protocols needed !! Naïve Idea Thus Id’s will be assigned in the code space according to their “Social Groups”