collaborative attacks in wireless ad hoc networks *

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

2

Outline

Characterizing collaborative/coordinated attacks

Types of collaborative attacks Open issues Proposed solutions Conclusions and outlook

3

Collaborative Attacks

Informal definition:

“Collaborative attacks (CA) occur when more than one attacker or running process

synchronize their actions to disturb a target network”

4

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

5


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

6


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 probablyincompatible attacks:

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

8

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

9


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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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)

D

S S1

S2 M

S3

S4

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

D

S S1

S2M

S3

S4

BL {}

BL {S2}

BL {}BL {M}

BL {S1}

BL {}

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

S4

BL {}

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RLR (cont’d) Malicious site is in blacklists of multiple destination hosts

D4

D1

S3

S1

M

D3

S4

S2

D2

[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 host

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RLR (cont’d)Acceleration in Intruder Identification

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

D3

M1

S1

D1

Coordinated attacks by M1, M2, and M3

D2

M2 M3

S2 S3

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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

D4

D1

S3

S1

M

D3

S4

S2

D2

[M]

[M]

[M]

[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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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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Simulation evaluations: false positive with no attack

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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 Use identity certificates to defend against Sybil attacksattacks

Each node is assigned some unique information by Each node is assigned some unique information by the setup serverthe setup server

The server then creates an identity certificate for The server then creates an identity certificate for each level-0 node binding this node’s identity to each level-0 node binding this node’s identity to the assigned unique informationthe 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

KI

Each node has a unique ID

Level-1 nodeLevel-0 node

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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

flgK , = Kg,l + f

fgK 0,

fgK 1,

flgK ,, , …,

fgK 0,

Kg,l

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Identity 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 g IDCertg = <vg , AuthPathg>

Level-0 node g can create a low-level certificate for level-0 node f using the low-level Merkle hash tree : =< , AuthPathg,f>

fgIDCert fK f

g ||0,

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An example of Two Levels of Merkle Hash Trees

C4

m1m2

m3 m4 m5 m6

C

u1u2

u3 u4 u5 u6

v1 v2 v3 v4 v5 v6 v7 v8

1||10.4K 2||2

0.4K 3||30.4K 5||5

0.4K 6||60.4K 7||7

0.4K 8||80.4K

Low-level Merkle hash tree

High-level Merkle hash tree

v4=H(C4||4)

3 1 2||u H v v

IDCertIDCert44 = < = <vv44, , AuthPathAuthPath44>>

AuthPathAuthPath44=={{vv33, u, u33, u, u22}}

2 2 14 4,0 4,0 4 2|| 2,{ ||1, , }IDCert K K m m

1 23 4,0 4,0||1|| || 2m H K K

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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 Ki,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|IDCertg| >

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

fgIDCert

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Secure Communication

IDCerti H(A1 , )j

iK 1,Round 0:

node i node j

IDCertj H(B1 , )ijK 1,

ijIDCert

jiIDCert

A1 H(A2 , )j

iK 2,Round 1:

B1 H(B2 , )ijK 2,

jiK 1,

ijK 1,

i

j

A2 H(A3 , )j

iK 3,Round 2:

B2 H(B3 , )ijK 3,

jiK 2,

ijK 2,

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)

node i node kA1 H(A2 , )

kiK 2,

Round 1:B1 H(B2 , )

ikK 2,

kiK 1,

ikK 1,

A2 H(A3 , )kiK 3,

Round 2:B2 H(B3 , )

ikK 3,

kiK 2,

ikK 2,

IDCerti H(A1 , )kiK 1,Round 0:

node i node gIDCertgigIDCert

giIDCerti

g

IDCertk H(B1 , )ikK 1,

node k node fIDCertfkfIDCert

fkIDCertk

f

IDCertg H(A1 , )kiK 1,

node g node f

fgIDCertg

IDCertf H(B1 , )ikK 1,

gfIDCertf

i

k

{

{

{

}

}

}

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

0

20

40

60

80

100

120

140

160

0 500 1000 1500 2000 2500

Number of Sensor Nodes

Mem

ory

Usa

ge

(KB

)

HSybil-Level0

HSybil-Level1

Identity Certification Generation Tim e

0

0.5

1

1.5

2

2.5

0 20 40 60 80 100

Num ber of Nodes per Group

Tim

e (

s)

HSybil

Energy Consum ption for Identity Certificates Generation

0

10

20

30

40

50

60

0 20 40 60 80 100

Num ber of Nodes per Group

En

erg

y C

on

sum

pti

on

(m

J)

HSybil-Level0

HSybil-Level1

0

20

40

60

80

100

120

140

160

0 20 40 60 80 100

Number of Nodes per Group

Mem

ory

Usa

ge

(KB

)

HSybil-Level0

HSybil-Level1

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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 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 low-level Merkle hash tree

jiK 0,

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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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Proc. IEEE Workshop on Security and Assurance in Ad hoc Networks, Jan. 2003.

[BH83] B. Bhargava and C. Hua, “A Causal Model for Analyzing Distributed Concurrency Control Algorithms,” IEEE Transactions on Software Engineering, 1983.

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