Collaborative attacks in wireless ad hoc networks

Collaborative Attacks in Wireless Ad Hoc Networks*Prof. Bharat BhargavaDepartment of Computer Sciences Center for Education and Research in Information Assurance and Security (CERIAS )Purdue Universitywww.cs.purdue.edu/people/bb * Supported in part by NSF grant IIS 0209059, 02428403/23/041OutlineCharacterizing collaborative/coordinated attacksTypes of collaborative attacksOpen issuesProposed solutionsConclusions and outlook3/23/042Collaborative AttacksInformal definition: “Collaborative attacks (CA) occur when more than one attacker or running process synchronize their actions to disturb a target network”3/23/043Collaborative Attacks (cont’d)Forms of collaborative attacksMultiple attacks occur when a system is disturbed by more than one attackerAttacks in quick sequences is another way to perpetrate CA by launching sequential disruptions in short intervals Attacks may concentrate on a group of nodes or spread to different group of nodes just for confusing the detection/prevention system in placeAttacks may be long-lived or short-lived Attacks on routing 3/23/044Collaborative Attacks (cont’d)Open issuesComprehensive understanding of the coordination among attacks and/or the collaboration among various attackersCharacterization and Modeling of CAsIntrusion Detection Systems (IDS) capable of correlating CAsCoordinated prevention/defense mechanisms3/23/045Collaborative Attacks (cont’d)From a low-level technical point of view, attacks can be categorized into:Attacks that may overshadow (cover) each otherAttacks that may diminish the effects of othersAttacks that interfere with each otherAttacks that may expose other attacksAttacks that may be launched in sequenceAttacks that may target different areas of the networkAttacks that are just below the threshold of detection but persist in large numbers3/23/046Examples of Attacks that can Collaborate Denial-of-Messages (DoM) attacksBlackhole attacksWormhole attacksReplication attacksSybil attacksRushing attacksMalicious floodingWe are investigating the interactions among these forms of attacksExample of probablyincompatible attacks:Wormhole attacks need fast connections, but DoM attacks reduce bandwidth!3/23/047Examples of Attacks that can Collaborate (cont’d)Denial-of-Messages (DoM) attacksMalicious nodes may prevent other honest ones from receiving broadcast messages by interfering with their radio Blackhole attacksA node transmits a malicious broadcast informing that it has the shortest and most current path to the destination aiming to intercept messages Wormhole attacksAn attacker records packets (or bits) at one location in the network, tunnels them to another location, and retransmits them into the network at that location 3/23/048Examples of Attacks that can Collaborate (cont’d)Replication attacksAdversaries can insert additional replicated hostile nodes into the network after obtaining some secret information from the captured nodes or by infiltration. Sybil attack is one form of replicated attacks Sybil attacksA malicious user obtains multiple fake identities and pretends to be multiple, distinct nodes in the system. This way the malicious nodes can control the decisions of the system, especially if the decision process involves voting or any other type of collaboration 3/23/049Examples of Attacks that can Collaborate (cont’d)Rushing attacksAn attacker disseminates a malicious control messages fast enough to block legitimate messages that arrive later (uses the fact that only the first message received by a node is used preventing loops) Malicious flooding A bad node floods the network or a specific target node with data or control messages 3/23/0410Current Proposed Solutions Blackhole attack detectionReverse Labeling Restriction (RLR)Wormhole Attacks: defense mechanismE2E detector and Cell-based Open Tunnel Avoidance (COTA)Sybil Attack detectionLight-weight method based on hierarchical architecture [Yi06]Modeling Collaborative Attacks using Causal Model 3/23/0411Blackhole attack detection: Reverse Labeling Restriction (RLR) Every host maintains a blacklist to record suspicious hosts who gave wrong route related informationBlacklists are updated after an attack is detectedThe destination host will broadcast an INVALID packet with its signature when it finds that the system is under attack on sequence. The packet carries the host’s identification, current sequence, new sequence, and its own blacklistEvery host receiving this packet will examine its route entry to the destination host. The previous host that provides the false route will be added into this host’s blacklist3/23/0412RLR (cont’d)During Route Rediscovery, False Destination Sequence Number Attack is Detected, S needs to find D againNode movement breaks the path from S to M (trigger route rediscovery)DSS1S2MS3S4RREQ(D, 21)(1). S broadcasts a request that carries the old sequence + 1 = 21(2) D receives the RREQ. Local sequence is 5, but the sequence in RREQ is 21. D detects the false destination sequence number attack.Propagation of RREQDetecting false destination sequence attack by destination host during route rediscovery3/23/0413RLR (cont’d)Correct destination sequence number is broadcasted. Blacklist at each host in the path is determinedDSS1S2MS3S4BL {}BL {S2}BL {}BL {M}BL {S1}BL {}INVALID ( D, 5, 21, BL{}, Signature )S4BL {}3/23/0414RLR (cont’d)Malicious site is in blacklists of multiple destination hostsD4D1S3S1MD3S4S2D2[M][M][M][M]M attacks 4 routes (S1-D1, S2-D2, S3-D3, and S4-D4). When the first two false routes are detected, D3 and D4 add M into their blacklists. When later D3 and D4 become victim destinations, they will broadcast their blacklists, and every host will get two votes that M is malicious host3/23/0415RLR (cont’d)Acceleration in Intruder Identification Multiple attackers trigger more blacklists to be broadcasted by D1, D2, D3D3M1S1D1Coordinated attacks by M1, M2, and M3D2M2M3S2S33/23/0416RLR (cont’d)Update Blacklist by Broadcasted Packets from Destinations under AttackNext hop on the false route will be put into local blacklist, and a counter increases. The time duration that the host stays in blacklist increases exponentially to the counter valueWhen timer expires, the suspicious host will be released from the blacklist and routing information from it will be accepted3/23/0417RLR: Deal With Hosts in BlacklistPackets from hosts in blacklistRoute request: If the request is from suspicious hosts, ignore it Route reply: If the previous hop is suspicious and the query destination is not the previous hop, the reply will be ignoredRoute error: Will be processed as usual. RERR will activate re-discovery, which will help to detect attacks on destination sequenceBroadcast of INVALID packet: If the sender is suspicious, the packet will be processed but the blacklist will be ignored3/23/0418Attacks of Malicious Hosts on RLRAttack 1: Malicious host M sends false INVALID packetBecause the INVALID packets are signed, it cannot send the packets in other hosts’ nameM sends INVALID in its own nameIf the reported sequence number is greater than the real sequence number, every host ignores this attackIf the reported sequence number is less than the real sequence number, RLR will converge at the malicious host. M is included in blacklist of more hosts. M accelerated the intruder identification directing towards M 3/23/0419Attacks on RLR (cont’d)Attack 2: Malicious host M frames other innocent hosts by sending false blacklistIf the malicious host has been identified, the blacklist will be updatedIf the malicious host has not been identified, this operation can only make the threshold lower. If the threshold is selected properly, it will not impact the identification resultsCombining trust can further limit the impact of this attack3/23/0420Attacks on RLR (cont’d)Attack 3: Malicious host M only sends false destination sequence about some special hostThe special host will detect the attack and send INVALID packetsOther hosts can establish new routes to the destination by receiving the INVALID packets3/23/0421Two Attacks in Collaboration: blackhole & replicationThe RLR scheme cannot detect the two attacks working simultaneouslyThe malicious node M relies on the replicated neighboring nodes to avoid the blacklist D4D1S3S1MD3S4S2D2[M][M][M][M]Replicated nodesRegular nodes3/23/0422Wormhole Attacks defenseA pair of attackers can form a tunnel, fabricating a false scenario that a short path between sender and receiver exists, and so packets go through a wormhole path being either compromised or dropped In many routing protocols, mobile nodes depend on the neighbor discovery procedure to construct the local network topology Wormhole attacks can harm some routing protocols by inducing a node to believe that a further away node is its neighbor3/23/0423Wormhole Attacks: proposed defense mechanismThis is a preliminary mechanism to classify wormhole attacks in its various forms It takes a more generic approach than previous work in the sense that it is end-to-end and does not rely on trust among neighborsIt assumes trust between sender and receiver only to detect wormhole attacks on a multi-hop route Geographic information is used to detect anomalies in neighbor relation and node movements 3/23/0424Wormhole Attacks: proposed defense mechanism (cont’d)The e2e mechanism can detect:Closed wormholeHalf open wormholeOpen wormhole3/23/0425Wormhole Attacks: proposed defense mechanism (cont’d)The approach requires considerable computation and storage power as periodical wormhole detection packets are transmitted and the response are used to compute nodes position, velocity etcBecause of that, an additional scheme called COTA is proposed to manage the detection information. It records and compares only a part of the pairsUsing a suitable relaxation, COTA has the same detection capability as the end-to-end mechanism 3/23/0426Wormhole Attacks: proposed defense mechanism (cont’d)Simulation evaluations: false positive with no attack3/23/0427Wormhole Attacks: proposed defense mechanism (cont’d)Simulation evaluations: false positive with attack3/23/0428Sybil Attack Detection A Hierarchical Architecture for Sybil Attack DetectionThe Sybil attack is a harmful threat to sensor networksSybil attack can disrupt multi-path routing protocols by using a single node to present multiple identities for the multiple paths Existing approaches are not oriented toward energy 3/23/0429Sybil Attack Detection: Proposed MethodUse identity certificates to defend against Sybil attacks Each node is assigned some unique information by the setup serverThe server then creates an identity certificate for each level-0 node binding this node’s identity to the assigned unique informationThe group leader creates an identity certificate for its group member (level-1 node)To securely demonstrate its identity, a node first presents its identity certificate, then it proves that it possesses the associated unique information3/23/0430Sybil Attack Detection: System AssumptionTwo types of nodes: Level-0 and level-1 nodesThe distribution of level-0 nodes is roughly uniformAll nodes are preloaded with a global initial key KIEach node has a unique ID3/23/0431Identity Certificate Generation for Level-0 NodesEach level-0 node g uses its key seed to generates N-1 key seeds. Ex. The key seed of node g for node f as Node g generates a one-way key chainThe setup server first creates the low-level Merkle hash tree using the key chain commitmentThe setup server then creates a high-level Merkle hash tree for level-0 nodes = Kg,l + f ,, , Kg,l3/23/0432Identity Certificate Generation for Level-0 Nodes (cont’d)The setup server then downloads the identity certificate IDCertg and the label of the high-level Merkle tree’s root C to each level-0 node gIDCertg = Level-0 node g can create a low-level certificate for level-0 node f using the low-level Merkle hash tree : = 3/23/0433An example of Two Levels of Merkle Hash TreesIDCert4 = AuthPath4={v3, u3, u2}3/23/0434Identity Certificate Generation for Level-1 NodesAfter deployment, the level-0 node g as the group leader starts the self-organization processAfter the localized self-organization process, the group leader g stores its group member’s identity i and the key seed commitment Ki,03/23/0435Identity VerificationAfter deployment, level-0 node g can prove its identity to another level-0 node f on demandnode g  node f: Indirect identity verification between the group members in the different groupsLet node i and node k be neighboring nodes, but belong to two different groupsNode i can prove its identity to its group leader gNode k can prove its identity to its group leader fGroup leaders g and f pass the verification results to each other3/23/0436Secure Communication Intra-group exchangesi and i  same groupIn round 0, two nodes i and j exchange their identity and identity certificates together with the hashes of their first messagesThen, they continue exchanging messages authentications with successive keys in their key chains3/23/0437Secure Communication (cont’d)Inter-group exchangesg and f  group leadersIn round 0, two nodes i and k prove their identity to each other and exchange the hashes of their first messages through their group leadersThen, they continue exchanging messages authentications with successive keys in their key chains3/23/0438Performance Evaluation3/23/0439Identity Certificate Generation for Level-1 Nodes (cont’d)The group leader g first creates a low-level Merkle hash tree using the key chain commitment The group leader g then creates a high-level Merkle hash tree for its group membersThe group leader g then downloads the identity certificate IDCerti to each group member i The group leader g downloads the low-level Merkle hash tree to each group member iThen the group member i can create a low-level certificate for another group member j using the low-level Merkle hash tree3/23/0440Modeling Collaborative Attacks Attack graphA general model technique used in assessing security vulnerabilities of a system and all possible sequences of exploits an intruder can take to achieve a specific goal We are currently working on a modeling for collaborative graph attacks to identify not only sequence of exploits but also concurrent and collaborative exploits. This leads to our Causal Model3/23/0441Causal model Purposes:Identify all attacks events that occur during the launch of individual and collaborative attacksEstablish a partial order (or causal relationship) among all attack events and produce a “causal attack graph”Verify the security properties of the causal attack graph using model checking techniques. Specifically, verify a sequence of events that lets the security checker proceeds from initial state to the goal state3/23/0442Causal model (cont’d)Identify the set of events that are critical to perform the attacks. Specifically, investigate how to find a minimum set of events that, once removed, would disable the attacksDetermine whether the occurrences of some event/state transitions are based on message transmission or collaborationBased on this, one can infer the degree of collaboration and temporal ordering in the system3/23/0443Causal model (cont’d)A collaborative attack X can be modeled as a set of attacks {Xi} such that Xi is the local attack launched by attacker n Each local attack Xi is modeled by a FSM (finite state machine) and has independent state and event specifications, such as preconditions, postconditions, and state transition rules In simple distributed attacks such as Distributed Denial-of-Service Attacks, the FSMs of each local attack can be the same. However, in sophisticated collaborative attacks, FSMs of local attacks are not necessarily homogeneousEach local attack Xi can be formally defined as: Sn denotes a set of states in the local attack, En denotes a set of events in the local attack, Mn denotes a set of communication messages, and Ln denotes a set of local operations on Mn.3/23/0444Causal model (cont’d)In collaborative attacks, events in attacks occur in certain sequences. A sequence of attack events may cause more damage to the system than othersThere are certain relationships among the events and we model the relationships by causal rules.Definition of causal rulesA causal rule U consists ofP and Q are eventsA is one of the causal relationships (->, , - >)3/23/0445ConclusionsExciting area of researchModeling attacks in collaboration is a very topical issueTradeoff between accuracy and computation inexpensiveness is critical3/23/0446Future workA lightweight learning toll is to be applied to enhance our current approachesThe remaining types of attacks will be addressedModels for detecting attacks in collaboration are underway and the causal model will be evaluated in depthGeneral guidelines will be defined to protect ad hoc networks from potential attacksMore simulations and real life experiments3/23/0447References (1)[BC03] P. Brutch and C. Ko, “Challenges in Intrusion Detection for Ad Hoc Networks,” Proc. 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