Collaborative Attacks in Wireless Ad Hoc Networks Prof

Collaborative Attacks in Wireless Ad Hoc Networks* Prof. Bharat Bhargava Department of Computer Sciences Center for Education and Research in Information Assurance and Security (CERIAS ) Purdue University www. cs. purdue. edu/people/bb * Supported in part by NSF grant IIS 0209059, 0242840

Outline n n n Characterizing collaborative/coordinated attacks Types of collaborative attacks Open issues Proposed solutions Conclusions and outlook 2

Collaborative Attacks Informal definition: “Collaborative attacks (CA) occur when more than one attacker or running process synchronize their actions to disturb a target network” 3

Collaborative Attacks (cont’d) n Forms of collaborative attacks n n n Multiple attacks occur when a system is disturbed by more than one attacker Attacks 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 place Attacks may be long-lived or short-lived Attacks on routing 4

Collaborative Attacks (cont’d) n Open issues n n Comprehensive understanding of the coordination among attacks and/or the collaboration among various attackers Characterization and Modeling of CAs Intrusion Detection Systems (IDS) capable of correlating CAs Coordinated prevention/defense mechanisms 5

Collaborative Attacks (cont’d) n From a low-level technical point of view, attacks can be categorized into: n n n n Attacks that may overshadow (cover) each other Attacks that may diminish the effects of others Attacks that interfere with each other Attacks that may expose other attacks Attacks that may be launched in sequence Attacks that may target different areas of the network Attacks that are just below the threshold of detection but persist in large numbers 6

Examples of Attacks that can Collaborate n n n n Denial-of-Messages (Do. M) attacks Blackhole attacks We are investigating the Wormhole attacks interactions among these forms of attacks Replication attacks Sybil attacks Rushing attacks Example of probably Malicious flooding incompatible attacks: Wormhole attacks need fast connections, but Do. M attacks reduce bandwidth! 7

Examples of Attacks that can Collaborate (cont’d) n Denial-of-Messages (Do. M) attacks n n Blackhole attacks n n A node transmits a malicious broadcast informing that it has the shortest and most current path to the destination aiming to intercept messages Wormhole attacks n Malicious nodes may prevent other honest ones from receiving broadcast messages by interfering with their radio An 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 8

Examples of Attacks that can Collaborate (cont’d) n Replication attacks n n Sybil attacks n Adversaries 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 A 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 9

Examples of Attacks that can Collaborate (cont’d) n Rushing attacks n n Malicious flooding n An 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) A bad node floods the network or a specific target node with data or control messages 10

Current Proposed Solutions n Blackhole attack detection n n Wormhole Attacks: defense mechanism n n E 2 E detector and Cell-based Open Tunnel Avoidance (COTA) Sybil Attack detection n n Reverse Labeling Restriction (RLR) Light-weight method architecture [Yi 06] based on hierarchical Modeling Collaborative Attacks using Causal Model 11

Blackhole attack detection: Reverse Labeling Restriction (RLR) n n Every host maintains a blacklist to record suspicious hosts who gave wrong route related information Blacklists are updated after an attack is detected The 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 blacklist Every 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 blacklist 12

RLR (cont’d) Detecting false destination sequence attack by destination host during route rediscovery n n During Route Rediscovery, False Destination Sequence Number Attack is Detected, S needs to find D again Node movement breaks the path from S to M (trigger route rediscovery) (1). S broadcasts a request that carries the old sequence + 1 = 21 D S 3 RREQ(D, 21) S S 1 S 2 S 4 (2) D receives the RREQ. Local sequence is 5, but the sequence in RREQ is 21. D detects the false destination sequence number attack. M Propagation of RREQ 13

RLR (cont’d) n Correct destination sequence number is broadcasted. Blacklist at each host in the path is determined BL {} S 3 BL {S 1} S BL S 4 {} D INVALID ( D, 5, 21, BL{}, Signature ) S 1 BL {S 2} S 2 BL {M} M BL {} S 4 BL {} 14

RLR (cont’d) n Malicious site is in blacklists of multiple destination hosts D 1 S 4 [M ] D 3 [M ] S 1 D 2 M [M ] S 3 D 4 [M ] S 2 M attacks 4 routes (S 1 -D 1, S 2 -D 2, S 3 -D 3, and S 4 -D 4). When the first two false routes are detected, D 3 and D 4 add M into their blacklists. When later D 3 and D 4 become victim destinations, they will broadcast their blacklists, and every host will get two votes that M is malicious host 15

RLR (cont’d) Acceleration in Intruder Identification Multiple attackers trigger more blacklists to be broadcasted by D 1, D 2, D 3 D 2 D 1 M 2 M 3 M 1 S 1 S 2 S 3 Coordinated attacks by M 1, M 2, and M 3 16

RLR (cont’d) n Update Blacklist by Broadcasted Packets from Destinations under Attack n n Next 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 value When timer expires, the suspicious host will be released from the blacklist and routing information from it will be accepted 17

RLR: Deal With Hosts in Blacklist n Packets from hosts in blacklist n n Route 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 ignored Route error: Will be processed as usual. RERR will activate re-discovery, which will help to detect attacks on destination sequence Broadcast of INVALID packet: If the sender is suspicious, the packet will be processed but the blacklist will be ignored 18

Attacks of Malicious Hosts on RLR n Attack 1: Malicious host M sends false INVALID packet n n Because the INVALID packets are signed, it cannot send the packets in other hosts’ name M sends INVALID in its own name n If the reported sequence number is greater than the real sequence number, every host ignores this attack n If 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 19

Attacks on RLR (cont’d) n Attack 2: Malicious host M frames other innocent hosts by sending false blacklist n If the malicious host has been identified, the blacklist will be updated n If the malicious host has not been identified, this operation can only make threshold lower. If the threshold is selected properly, it will not impact the identification results n Combining trust can further limit the impact of this attack 20

Attacks on RLR (cont’d) n Attack 3: Malicious host M only sends false destination sequence about some special host n n The special host will detect the attack and send INVALID packets Other hosts can establish new routes to the destination by receiving the INVALID packets 21

Two Attacks in Collaboration: blackhole & replication n n The RLR scheme cannot detect the two attacks working simultaneously The malicious node M relies on the replicated neighboring nodes to avoid the blacklist D 1 S 4 [M ] D 3 [M ] D 2 M [M ] S 3 D 4 [M ] Replicated nodes S 1 S 2 Regular nodes 22

Wormhole Attacks defense n n n A 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 neighbor 23

Wormhole Attacks: proposed defense mechanism n n This 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 neighbors It 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 24

Wormhole Attacks: proposed defense mechanism (cont’d) n The e 2 e mechanism can detect: n n n Closed wormhole Half open wormhole Open wormhole 25

Wormhole Attacks: proposed defense mechanism (cont’d) n n n 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 etc Because of that, an additional scheme called COTA is proposed to manage the detection information. It records and compares only a part of the <time, position> pairs Using a suitable relaxation, COTA has the same detection capability as the end-to-end mechanism 26

Wormhole Attacks: proposed defense mechanism (cont’d) n Simulation evaluations: false positive with no attack 27

Wormhole Attacks: proposed defense mechanism (cont’d) n Simulation evaluations: false positive with attack 28

Sybil Attack Detection n A Hierarchical Architecture for Sybil Attack Detection The Sybil attack is a harmful threat to sensor networks n n Sybil 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 29

Sybil Attack Detection: Proposed Method n n n Use identity certificates to defend against Sybil attacks Each node is assigned some unique information by the setup server The server then creates an identity certificate for each level-0 node binding this node’s identity to the assigned unique information The group leader creates an identity certificate for its group member (level-1 node) To securely demonstrate its identity, a node first presents identity certificate, then it proves that it possesses the associated unique information 30

Sybil Attack Detection: System Assumption n n Two types of nodes: Level-0 and level-1 nodes The distribution of level-0 nodes is roughly uniform All nodes are preloaded with a global initial key KI Each node has a unique ID 31

Identity Certificate Generation for Level-0 Nodes n n Each level-0 node g uses its key seed K g, l to generates N-1 key seeds. Ex. The key seed of node g for node f as = Kg, l + f Node g generates a one-way key chain , , …, The setup server first creates the low-level Merkle hash tree using the key chain commitment The setup server then creates a high-level Merkle hash tree for level-0 nodes 32

Identity Certificate Generation for Level-0 Nodes (cont’d) n n 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 g n IDCertg = <vg , Auth. Pathg> Level-0 node g can create a low-level certificate for level-0 node f using the low-level Merkle hash tree n : =< , Auth. Pathg, f> 33

An example of Two Levels of Merkle Hash Trees IDCert 4 = <v 4, Auth. Path 4> Auth. Path 4={v 3, u 2} 34

Identity Certificate Generation for Level-1 Nodes n n After deployment, the level-0 node g as the group leader starts the self-organization process After the localized self-organization process, the group leader g stores its group member’s identity i and the key seed commitment Ki, 0 35

Identity Verification n After deployment, level-0 node g can prove its identity to another level-0 node f on demand n n Indirect identity verification between the group members in the different groups n n node g node f: <g|IDCertg| > Let node i and node k be neighboring nodes, but belong to two different groups Node i can prove its identity to its group leader g Node k can prove its identity to its group leader f Group leaders g and f pass the verification results to each other 36

Secure Communication Intra-group exchanges n i and i same group n In round 0, two nodes i and j exchange their identity and identity certificates together with the hashes of their first messages n Then, they continue exchanging messages authentications with successive keys in their key chains 37

Secure Communication (cont’d) Inter-group exchanges n g and f group leaders n In round 0, two nodes i and k prove their identity to each other and exchange the hashes of their first messages through their group leaders n Then, they continue exchanging messages authentications with successive keys in their key chains 38

Performance Evaluation 39

Identity Certificate Generation for Level-1 Nodes (cont’d) n n n 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 members The 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 i Then the group member i can create a low-level certificate for another group member j using the lowlevel Merkle hash tree 40

Modeling Collaborative Attacks n Attack graph n n A 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 Model 41

Causal model Purposes: n Identify all attacks events that occur during the launch of individual and collaborative attacks n n Establish 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. n Specifically, verify a sequence of events that lets the security checker proceeds from initial state to the goal state 42

Causal model (cont’d) n Identify the set of events that are critical to perform the attacks. n n Determine whether the occurrences of some event/state transitions are based on message transmission or collaboration n Specifically, investigate how to find a minimum set of events that, once removed, would disable the attacks Based on this, one can infer the degree of collaboration and temporal ordering in the system 43

Causal model (cont’d) n n 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 homogeneous n Each local attack Xi can be formally defined as: <Sn, En, Mn, Ln> n n 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. 44

Causal model (cont’d) n n n In collaborative attacks, events in attacks occur in certain sequences. A sequence of attack events may cause more damage to the system than others There are certain relationships among the events and we model the relationships by causal rules. Definition of causal rules n n A causal rule U consists of <P, Q, A> P and Q are events A is one of the causal relationships (->, , - >) 45

Conclusions n n n Exciting area of research Modeling attacks in collaboration is a very topical issue Tradeoff between accuracy and computation inexpensiveness is critical 46

Future work n n n A lightweight learning toll is to be applied to enhance our current approaches The remaining types of attacks will be addressed Models for detecting attacks in collaboration are underway and the causal model will be evaluated in depth General guidelines will be defined to protect ad hoc networks from potential attacks More simulations and real life experiments 47

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