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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

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Outline

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

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Collaborative Attacks

Informal definition:

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

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Collaborative Attacks (cont’d)

Forms of collaborative attacks

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

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Collaborative Attacks (cont’d)

Open issues

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

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Collaborative Attacks (cont’d)

From a low-level technical point of view, attacks can be categorized into:

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

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Examples of Attacks that can Collaborate

Denial-of-Messages (DoM) attacks
Blackhole attacks
Wormhole attacks
Replication attacks
Sybil attacks
Rushing attacks
Malicious flooding

We are investigating the interactions among these forms of attacks

Example of probably

incompatible attacks:

Wormhole attacks need fast connections, but DoM attacks reduce bandwidth!

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Examples of Attacks that can Collaborate (cont’d)

Denial-of-Messages (DoM) attacks

Malicious nodes may prevent other honest ones from receiving broadcast messages by interfering with their radio

Blackhole attacks

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

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

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Examples of Attacks that can Collaborate (cont’d)

Replication attacks

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

Sybil 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

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Examples of Attacks that can Collaborate (cont’d)

Rushing attacks

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)

Malicious flooding

A bad node floods the network or a specific target node with data or control messages

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Current Proposed Solutions

Blackhole attack detection

Reverse Labeling Restriction (RLR)

Wormhole Attacks: defense mechanism

E2E detector and Cell-based Open Tunnel Avoidance (COTA)

Sybil Attack detection

Light-weight method based on hierarchical architecture [Yi06]

Modeling Collaborative Attacks using Causal Model

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Blackhole attack detection: Reverse Labeling Restriction (RLR)

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

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RLR (cont’d)

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)

RREQ(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 RREQ

Detecting false destination sequence attack by destination host during route rediscovery

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RLR (cont’d)

Correct destination sequence number is broadcasted. Blacklist at each host in the path is determined

BL {}

BL {S2}

BL {}

BL {M}

BL {S1}

BL {}

INVALID ( D, 5, 21, BL{}, Signature )

BL {}

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RLR (cont’d)

Malicious site is in blacklists of multiple destination hosts

[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 host

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RLR (cont’d)

Acceleration in Intruder Identification

Multiple attackers trigger more blacklists to be broadcasted by D1, D2, D3

Coordinated attacks by M1, M2, and M3

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RLR (cont’d)

Update Blacklist by Broadcasted Packets from Destinations under Attack

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

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RLR: Deal With Hosts in Blacklist

Packets from hosts in blacklist

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

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Attacks of Malicious Hosts on RLR

Attack 1: Malicious host M sends false INVALID packet

Because the INVALID packets are signed, it cannot send the packets in other hosts’ name
M sends INVALID in its own name

If the reported sequence number is greater than the real sequence number, every host ignores this attack
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

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Attacks on RLR (cont’d)

Attack 2: Malicious host M frames other innocent hosts by sending false blacklist

If the malicious host has been identified, the blacklist will be updated
If 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 results
Combining trust can further limit the impact of this attack

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Attacks on RLR (cont’d)

Attack 3: Malicious host M only sends false destination sequence about some special host

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

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Two Attacks in Collaboration: blackhole & replication

The RLR scheme cannot detect the two attacks working simultaneously
The malicious node M relies on the replicated neighboring nodes to avoid the blacklist

[M]

Replicated nodes

Regular nodes

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Wormhole Attacks defense

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

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Wormhole Attacks: �proposed defense mechanism

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

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Wormhole Attacks: �proposed defense mechanism (cont’d)

The e2e mechanism can detect:

Closed wormhole
Half open wormhole
Open wormhole

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Wormhole 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 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

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Wormhole Attacks: �proposed defense mechanism (cont’d)

Simulation evaluations: false positive with no attack

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Wormhole Attacks: �proposed defense mechanism (cont’d)

Simulation evaluations: false positive with attack

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Sybil Attack Detection

A Hierarchical Architecture for Sybil Attack Detection

The Sybil attack is a harmful threat to sensor networks

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

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Sybil Attack Detection: Proposed Method

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 its identity certificate, then it proves that it possesses the associated unique information

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Sybil Attack Detection: System Assumption

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 K_I
Each node has a unique ID

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Identity Certificate Generation for Level-0 Nodes

Each 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 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

= K_g,l + f

, …,

K_g,l

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Identity Certificate Generation for Level-0 Nodes (cont’d)

The setup server then downloads the identity certificate IDCert_g and the label of the high-level Merkle tree’s root C to each level-0 node g

IDCert_g = <v_g, AuthPathg>

Level-0 node g can create a low-level certificate for level-0 node f using the low-level Merkle hash tree

: =< , AuthPath_g,f>

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An example of Two Levels of Merkle Hash Trees

IDCert₄ = <v₄, AuthPath₄>

AuthPath₄={v₃, u₃, u₂}

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Identity Certificate Generation for Level-1 Nodes

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 K_i,0

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Identity Verification

After deployment, level-0 node g can prove its identity to another level-0 node f on demand

node g → node f: <g|IDCert_g| >

Indirect identity verification between the group members in the different groups

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

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Secure Communication

Intra-group exchanges

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

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Secure Communication (cont’d)

Inter-group exchanges

g and f 🡪 group leaders
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
Then, they continue exchanging messages authentications with successive keys in their key chains

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Performance Evaluation

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Identity 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 members
The group leader g then downloads the identity certificate IDCert_i 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 low-level Merkle hash tree

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Modeling Collaborative Attacks

Attack graph

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

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Causal model

Purposes:

Identify all attacks events that occur during the launch of individual and collaborative attacks

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.

Specifically, verify a sequence of events that lets the security checker proceeds from initial state to the goal state

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Causal 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 attacks

Determine whether the occurrences of some event/state transitions are based on message transmission or collaboration

Based on this, one can infer the degree of collaboration and temporal ordering in the system

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Causal 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 homogeneous
Each local attack Xi can be formally defined as:

<Sn, En, Mn, Ln>

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.

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Causal 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 others
There are certain relationships among the events and we model the relationships by causal rules.

Definition of causal rules

A causal rule U consists of
<P, Q, A>
P and Q are events
A is one of the causal relationships (->, 🡪, - 🡪>)

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Conclusions

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

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Future work

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

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