A Solution for Prevention of Selective Dropping and Selﬁsh Attacks in

Opportunistic Networks

Samaneh Rashidibajgan

Department of Computer Science, Rostock University, Albert-Einstein Strasse 22, Rostock, Germany

Keywords:

Opportunistic Networks, Dropping and Selective Dropping Attack, Selﬁsh Attack, Game Theory.

Abstract:

Opportunistic Networks (OppNet) are based on routing messages from a node to another node, from a source

to the destination. There is not a connection to the Internet in these networks and nodes play routers role, So

it is important that all of the nodes participate in the routing protocol. These networks have high potential to

vulnerable against ”Dropping and Selective Dropping Attacks” and ”Selﬁsh attacks”. Some nodes may prefer

to discard some messages in order to save their Battery life, memory space and so on, while they use the

network services. It causes an interruption in the network and makes a high delay for messages. In this paper,

we propose a new method based on Game Theory to prevent these attacks against OppNet, and we will prove

that our strategy is a Nash equilibrium. Also we will discuss that our algorithm is resistance against various

attacks.

1 INTRODUCTION

In Opportunistic Networks, there are any fundamental

infrastructures and devices which people carry with

themselves, like their cell phones and tablets, will

save, carry and forward messages from a source to

the destination. There are many attacks against these

networks, which two of them are ”Dropping and Se-

lective Dropping Attacks” and ”Selﬁsh attacks” (Ala-

jeely et al., 2015). Each node in the network has an

important role and acts as a router. If a node discards

some messages without a reason or it will be selﬁsh, it

causes the whole network performance fall down. So

preventing these attacks are very important in Opp-

Net.

In ”Dropping and Selective Dropping Attacks”

malicious nodes drop all or some of their received

packets, and the sender could not ﬁnd that their mes-

sages are discarded. In ”Selﬁsh Attacks”, some nodes

may use network services, but refuse to cooperate

with other nodes to carry and forward their messages.

Some proposed algorithms in the literature for de-

tecting selective dropping attacks are as follows:

In order to detecting selective dropping attacks, a

multi-dataﬂow topology (MDT) scheme was used in

(Sun et al., 2007). In this algorithm, a network is

divided into some clusters which they have overlap

with each other, so messages are sent from different

paths with redundancies. If a node discards a mes-

sage, other nodes will send it. In OppNet, we don’t

have knowledge about the structure of a network and

nodes are not always online, so this algorithm is not

really useful in OppNet.

Hai and Huh (Hoang and Huh, 2008) proposed a

lightweight detection scheme for Selective dropping

attacks. In this algorithm, each node monitors two

hop neighbors and considers a threshold for them. If

the malicious counter crosses the threshold, then this

node will be introduced as a malicious node and other

nodes will omit it from their neighborhood lists. This

algorithm is not resistance against cheating.

An ant based algorithm was introduced in (Ku-

mari and Paramasivan, 2015) for detecting selective

forwarding attacks. Some ant nodes are used in this

algorithm in order to collect knowledge about misbe-

havior nodes. Then, these collected data are used for

calculating a trust value, and a threshold is used for

detecting an attack.

A repeated continued non cooperative game for

detecting selective discard attacks was introduced in

(Liao and Ding, 2015). In this algorithm, nodes mon-

itor their neighbors in order to ﬁnd more reliable

neighbors. Authors calculated the best response for

each player in their scheme and then a Nash equilib-

rium for the game was calculated.

Also some algorithms for detecting selﬁsh attacks

are as follows:

COOPON was introduced in (Jo et al., 2013), (Su-

Rashidibajgan, S.

A Solution for Prevention of Selective Dropping and Selﬁsh Attacks in Opportunistic Networks.

DOI: 10.5220/0006233200300035

In Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security (IoTBDS 2017), pages 30-35

ISBN: 978-989-758-245-5

jitha et al., 2015). This algorithm uses determinis-

tic channel allocation information for detecting selﬁsh

attacks. This approach was designed for cognitive ra-

dio ad hock networks. Authors have used autonomous

and cooperative characters of ad-hock networks in or-

der to increasing the detection reliability.

Kargl and colleagues (Kargl et al., 2004), used

multiple sensors in parallel in order to detecting self-

ish nodes. They have used an iterative probing for

detecting selﬁsh nodes. When a sender does not re-

ceive acknowledgment from a receiver (X

) for a cer-

tain time of t, it will send a probe packet to it. If there

won’t be a reply in a certain time, it will send a probe

packet to X

n−1

, and it will continue this process until it

receives an answer or reaches to X

. When it receives

a message from X

, it will ﬁnd that X

i+1

is a selﬁsh

node. There is not stable path between two nodes in

OppNet, so it is impossible to use this algorithm in

OppNets.

In (Mittal, 2015), authors used an agent based

technique for detecting selﬁsh attacks. Every node in

the network works as a monitor module and it checks

its neighbors. Then they judge each node according

to the received information from its neighbors. There

is a probability to receive wrong information from

neighbors in this algorithm.

All of these algorithms are not suitable for Oppor-

tunistic Networks. In OppNet, nodes are moving and

we don’t have a stable path between the source and

a destination. In most of the mentioned algorithms,

authors assumed that they have a clear network struc-

ture while the topology of the network in OppNet is

changed frequently and nods attend and leave the net-

work mostly. So, because of the erratic structure of

OppNet, non of the proposed algorithms can be used

in OppNet.

In this paper, we propose a method for preven-

tion of Dropping and Selective Dropping Attacks and

Selﬁsh Attacks in OppNet without knowledge about

a network topology or density of nodes. Our algo-

rithm is based on Game Theory, and we prove that our

method is a Nash Equilibrium and any node has mo-

tivation to violate it. We deﬁne a best strategy which

players will play in a good history and when one of

them disobey from rules (bad history), other players

won’t play with it. In this order, any player could in-

crease its whole payoff by one game violation.

We consider the following assumptions in this pa-

per:

1. We consider an Opportunistic Network with lim-

ited number of nodes (For example a conference

in a department of an university which partici-

pants use OppNet.)

2. Every node has a pair of private and public key,

which it could share its public key with trusted

nodes. When nodes produce a message, they sign

it by their private key.

3. Nodes use a trust function in order to detect

trusted nodes, and they send and receive messages

only with trusted nodes (We consider the trust

structure which is introduced in (Rashidibajgan,

2016)).

4. When nodes are in communication range of each

other, they should be sure about the trust of other

nodes by using a trust function and then exchange

information.

5. Nodes are moving frequently.

6. After a period of time, a node will visit most of

the nodes in the network

The rest of this paper is organized as follow: we de-

scribe our method in the section two. In the section

three we prove that our method is resistance against

various attacks, and conclude our work in the section

four.

2 DESCRIPTION OF METHOD

We consider an Opportunistic network which has

some limited nodes, for example a conference which

cellphones of participants are our nodes, and they can

connect to each other via WiFi for sending and receiv-

ing messages. This Opportunistic network could help

participants to ﬁnd people how have a similar inter-

est ﬁeld in the science. When nodes are in the com-

munication range of each other, they exchange some

messages and also some parts of their tables, and after

that each one updates its table.

In our algorithm, we have designed some tables

and according to these tables, nodes give score to each

other in the network. We aim to give more scores and

priorities to the nodes which participant in the net-

work, and recognize selﬁsh nodes and selective drop-

ping attacks. Nodes will consider history of other

nodes and decide to play send or discard messages

in the game.

Each node in the network has a delivery table

(DT), and each message and each node has an ID in

the network and we call them MID and NID respec-

tively. When a node receives a message, the ID of the

message (MID) and the ID of the node which directly

delivered this message (LID) and ID of the sender

of the message (SID) are saved in DT (according to

the Figure 1). DT has another column with the name

of NKN, and it saves ID of neighbors how sent this

row of DT to them recently. When a node receives

A Solution for Prevention of Selective Dropping and Selﬁsh Attacks in Opportunistic Networks

Figure 1: Different nodes from a source to the destination.

a message, the next row of the delivery table will be

DT(LID,SID,MID,-). Table 1 shows the structure of

DT. When nodes are in the communication range of

each other, they exchange some parts of their delivery

tables and after that, each node updates its DT. The

process of exchanging and updating NKN ﬁeld of DT

are done as follow:

1. When a node sends a row of its DT to a neighbor,

adds the NID of this neighbor to NKN. So they

wont send repetitive information to a neighbor.

2. When a node visits the sender of a message (SID)

and forwards/has forwarded this row of the table

to at least n neighbors, it can delete this row in its

table.

3. When there will be new information and a node

dose not have free space in its DT, it can delete

rows according to the ﬁrst come ﬁrst out algo-

rithm.

Table 1: Structure of a DT table in the receiver (RID) node.

LID SID MID NKN

Also nodes have another table which they give

score to other nodes in it and we call it Score table

(SC). Score table has two parts: Positive Score (SCp)

and Negative Score (SCn). The structure of SC table

is shown in Table 2. Score tables have two ﬁelds of

NID and Score. Nodes after exchanging their delivery

tables, they update their Score tables too.

SCp is updated as follows:

1. If NID of the LID in DT is not exist in the SCp

of a node, it adds ID of this node to the table and

gives one score to it.

2. If NID of the LID in DT is currently in the SCp of

a node, it increases this score by 1.

Table 2: Structure of a SC table in a node.

SCp SCn

NID Score NID Score

On the other hand, when they recognize a node is

lying about visiting other nodes (described in section

3), they put ID of this node in the Negative Score ta-

bles (SCn) as follows:

1. If NID of the conﬂict intermediate node is not ex-

ist in the SCn of a node, it adds this and gives one

score to it.

2. If NID of the conﬂict intermediate node is in the

SCn of a node, it increases this score by 1.

A node which is in the Negative Score table (SCn)

could not send its messages for (n · score) periods of

time (as punishment) because other nodes refuse to

carry its messages. After passing (n · score) periods of

time, ID of this violate node will omit from the SCn

of nodes, and it could try to cooperate in sending and

receiving data and increases its positive score. Actu-

ally, SCp and SCn are two lists about ID of nodes

which have good and bad history respectively and

their scores. When a node cooperates in the forward-

ing a message or lying, it increases its score in SCp

and SCn respectively. Each table has m rows records

about m nodes with higher scores. Also, nodes which

participate for sending and updating their tables re-

ceive α points as an encouragement (section 3).

These two tables, DT and SC, help nodes to learn

about environment. They can observe each other and

ﬁnd which nodes are cooperating and which nodes are

violating rules of the network.

We consider two kinds of history for each player

in a game:

Good history: each node assumes that since a

node forward my messages or messages of other

nodes (according to the SCp), I will forward its mes-

sages. In Other words, since a node plays ”Sending”

I will forward its messages and I will play ”Sending”

too. Furthermore, nodes with higher scores will have

higher priority.

Bad history: each node assumes that if a node con-

stantly discards my messages or other nodes’ mes-

sages, I wont accept to carry its messages. when a

node started to play ”Discard” in a period of a game

and its name is in SCn, I won’t forward its messages.

Best Strategy of the Game (ST): each node plays

”Sending” at a good history and plays ”Discard” at a

bad history.

In the following, we will prove that non of the

player can increase its payoff at some histories by one

step Discarding a message. We prove that if the next

period of the game (the next sending message) will

be as important as this game (sending current mes-

sage) and nodes want to continue sending and receiv-

ing messages, they do not have violation motivation.

We consider δ as this dependence of nodes to the fu-

ture. They will know that if they won’t send messages

of other nodes, their messages wont be sent. Nodes

have motivation of violation for increasing their pay-

off, but in the following we will prove that if they vi-

olate the rules of the game, they could increase their

IoTBDS 2017 - 2nd International Conference on Internet of Things, Big Data and Security

payoff only for one period of the game and generally

their overall payoff will decrease.

Table 3: Payoff for different playing games.

Send Discard

Send 1, 1 −L, (1 + G)

Discard (1 + G), −L 0, 0

According to the Table 3, when both players A

and B in the network, forward messages of each other,

they receive 1, and if both discard messages, they will

receive 0. If a node, for example node A, violates

the rule and does not send messages of B while its

messages will be sent by B, the violator node A re-

ceives 1 +G (1 means its message is sent and G could

be other advantages like saving Battery, memory etc.

which node A gain) and B receives −L (it carries and

forwards a message while its message is not sent).

Node A receives an advantage, but only for one period

of the game. According to the strategy of the game, it

wont receive services in the next periods and its total

payoff (u) will fall down, so it won’t have violation

motivation.







Send t = 1

Send ST

t−1

(Send, Send)&t > 1

Discard otherwise







It means that when node A is in the communi-

cation range of another node B, if it is the ﬁrst time

which they want to send and receive a message, they

play send, and for (t > 1), if the node B played send

in the previous time (t − 1), node A plays send in this

period of time (t) and otherwise node A discards node

B’s messages.

(Send

∞

, Send

∞

) = 1 + δ + δ

+ δ

+ · · · (1)

0 < δ < 1 (2)

(DiscardDiscard

, SendDiscard

) = 1 + G + 0 + 0 + · · ·

| {z }

(3)

a node wont have violation motivation if:

(DiscardDiscard

, SendDiscard

) ≤ u

(Send

∞

, Send

∞

)

(4)

1 + G ≤

1 − δ

(5)

1 − δ ≤

1 + G

(6)

δ ≥ 1 −

1 + G

(7)

The equation of δ ≥ 1 −

1+G

is a Nash Equilib-

rium and nobody has motivation of violation this. It is

the state which both players could achieve their high-

est payoff and with violation won’t achieve more.

So nodes with high contributions will receive high

scores and their messages will be accepted with more

nodes, and they have more motivation for accepting,

carrying and sending of messages of other nodes.

when node A has n neighbors, node A can play

this game with them separately.

3 EVALUATION

In this section, we analyze the proposed algorithm

from some perspectives, and we will discuss that our

algorithm is resist against some attacks and it has high

performance.

1. We have considered a network with 100 nodes

and evaluated behavior of nodes in two situations:

in a normal network, and in a network with the

proposed structure. Also we assumed that nodes

could remember their previous activities and any

new node was added to the network during this

simulation. We have calculated the performance

of the network according to the following equa-

tion:

Performance =

All nodes in the network - Selﬁsh nodes

All nodes in the network

(8)

According to the Figure 2, in the proposed algo-

rithm nodes will learn that if they will be selﬁsh,

their messages wont be sent. As a result, they

wont have motivation for violating the rules and

be selﬁsh after some periods of time while in a

normal network around 30 percent of nodes pre-

fer to be selﬁsh.

According to the Figure 3, the proposed algorithm

has better performance, and after a while any node

has motivation for violation.

2. According to the Table 1, when two nodes are in

the communication range of each other and ex-

change their DT tables and one of them ﬁnds that

the another node is the sender of one of its mes-

sages, the following items could happen.

(a) both SID and MID are correct and there is not

a problem. Then the source will know its mes-

sage received and this row of the DT could be

omitted from the table of the sender.

(b) SID is correct but MID is not correct. In this

situation the sender declares that the message is

changed or he did not send this message. In this

A Solution for Prevention of Selective Dropping and Selﬁsh Attacks in Opportunistic Networks

Table 4: Summery of different states for a row in a DT.

Sender Message Cause Action

SID X MID X This row can omit from the table -

SID X MID ×

The SID did not send this message

The message was changed

The sender signs the message with its private key

SID × MID X It dosen’t happen -

SID × MID ×

The sender is not exist in the network

The message was produced by a fake SID

The LID is suspect to be a malicious node

Time Slot

0 5 10 15 20 25 30 35 40 45 50

Selfish Nodes (Percent)

Usual Network

Network in Proposed Algorithm

Figure 2: Amount of selﬁsh nodes in a normal network and

in a network with the proposed structure.

Time Slot

0 5 10 15 20 25 30 35 40 45 50

Network Performance

100

105

Usual Network

Network in Proposed Algorithm

Figure 3: Performance of the network in a normal situation

and with the proposed structure.

case, the source node and LID are suspected to

lying, but also it is possible that other interme-

diate nodes caused this problem. If every node

signs a message with its private key, this prob-

lem wont happen.

whether SID existed and produced this mes-

sage and then left the network, or this message

was produced by a fake NID which never ex-

isted in the network. In both situations, after n

periods of time, the message is not valid any-

more. In this state LID is a suspect node. If

every node be care to accept messages from

the trusted nodes, this option wont happen. So

when they wont visit a sender for n periods of

time (each node will visit most of the nodes in

the network after n periods of time), we can ask

others about this sender, if nobody knows about

this SID, we consider LID as a malicious node,

because it didn’t care to accept a message from

a trusted node or it made a fake message.

the summery of mentioned items are in Table 4

3. Another type of malicious activity relates to the

nodes which disobey to give scores to other nodes

as follows:

(a) a node sends some rows of its DT but it’s neigh-

bor does not update its score table.

(b) a node does not send some rows of it’s DT for

neighbors for updating.

A node may do these activities with the hope that

when others don’t achieve more scores, its score

will increase more than them during the time. In

order to omit this motivation, we give a point to

the nodes which send some rows of their tables

and also we give the scores to nodes which update

their tables. We consider this point as α which

0 < α < 1 , and according to the Table 5, if α will

be higher than the score which a node receives

during time (

), nodes do not have motivation of

discarding some rows of their DT or do not update

their SC tables.

Table 5: Received scores during a period of time for coop-

erated nodes and non cooperated nodes.

update SC don’t update SC

send DT α, α α, (

) − α

don’t send DT (

) − α, α (

) − α, (

) − α

When (

) < α, nodes do not have violation moti-

vation. After updating their SC tables, they should

exchange their tables to be sure that update is done

and sign SC of each other with their private keys.

4. A node can receive a message and does not give

a point to LID. In order to prevent this, each node

can know its score, and when it sends a message to

a destination, increases its score and asks receiver

to sign it with its private key. When a node ﬁnds

IoTBDS 2017 - 2nd International Conference on Internet of Things, Big Data and Security

that a neighbor didn’t give score to it in its SC, it

can complain and introduce this node as a liar.

5. A node may broadcast a null message with the

aim of receiving more scores. For solving this

problem, when a node receives a null message or

meaningless message, it will consider this SID as

a liar.

4 CONCLUSION AND FUTURE

WORKS

In this paper we proposed a new method based on

Game theory. We deﬁned some tables for each node

and gave positive and negative scores to the nodes in

the network. Nodes will receive priority for sending

their messages according to their positive scores and

they make a good history for themselves, and nodes

with negative scores do not have allowance to send

their messages for some periods of time and they will

have bad history. When a node is in the communi-

cation range of another node, it plays sending in the

good history and plays discard in the bad history. We

proved that this strategy is a Nash equilibrium and

non of the players have violation motivation. Also,

we discussed about various attacks against the net-

work according to the various ﬁelds of tables and we

proved that our algorithm is resistance against attacks.

Furthermore, we showed that during some periods of

time, nodes wont have motivation to be selﬁsh and

the performance of the network will be in the higher

position.

In our algorithm, we have assumed that the Opp-

Net is implemented in a limited area like a conference

in a department of a university which all of the nodes

are registered in the network, so they can receive pub-

lic and private keys. In a large area OppNet, like a

city, it is almost impossible for nodes to connect to a

third party for receiving public and private keys. De-

veloping a method for sharing a public key in OppNet

and as a result extend our algorithm in a large area is

a part of our work in the future.

Furthermore in this paper, we have considered se-

lective dropping and selﬁsh attacks, but this work can

be developed to detect other attacks. We intend to

study other kinds of attack against opportunistic net-

works and complete our intrusion prevention plan in

the future.

REFERENCES

Alajeely, M., Doss, R., and Ahmad, A. (2015). Security

and trust in opportunistic networks–a survey. In IETE

Technical Review, pages 1–13. Taylor & Francis.

Hoang, T. H. and Huh, E. (2008). Detecting selective

forwarding attacks in wireless sensor networks using

two-hops neighbor knowledge. In Seventh IEEE Inter-

national Symposium on Network Computing and Ap-

plications, 2008. NCA’08, pages 325–331. IEEE.

Jo, M., Han, L., Kim, D., and In, H. (2013). Selﬁsh attacks

and detection in cognitive radio ad-hoc networks. In

IEEE network, volume 27, pages 46–50. IEEE.

Kargl, F., Klenk, A., Schlott, S., and Weber, M. (2004). Ad-

vanced detection of selﬁsh or malicious nodes in ad

hoc networks. In Security in Ad-hoc and Sensor Net-

works, pages 152–165. Springer.

Kumari, S. and Paramasivan, B. (2015). Ant based defense

mechanism for selective forwarding attack in manet.

In 31st IEEE International Conference on Data En-

gineering Workshops (ICDEW), 2015, pages 92–97.

IEEE.

Liao, H. and Ding, S. (2015). Mixed and continuous strat-

egy monitor-forward game based selective forward-

ing solution in wsn. In International Journal of Dis-

tributed Sensor Networks, pages 1–13. Hindawi.

Mittal, S. (2015). Identiﬁcation technique for all passive

selﬁsh node attacks in a mobile network. In Interna-

tional Journal of Advance Research in Computer Sci-

ence and Management Studies, volume 3, pages 46–

51. IJARCSMS.

Rashidibajgan, S. (2016). A trust structure for detection of

sybil attacks in opportunistic networks. In 11th Inter-

national Conference for Internet Technology and Se-

cured Transactions (ICITST), pages 347–351. IEEE.

Sujitha, R., Scholar, P., and Poornima, N. (2015). Efﬁcient

detection of selﬁsh attacks in cognitive radio networks

using coopon algorithm. In International journal of

advanced research trends in Engineering and technol-

ogy, volume 2, pages 84–91. IJARTET.

Sun, H., Chen, C., and Hsiao, Y. (2007). An efﬁcient coun-

termeasure to the selective forwarding attack in wire-

less sensor networks. In TENCON 2007-2007 IEEE

Region 10 Conference, pages 1–4. IEEE.

A Solution for Prevention of Selective Dropping and Selﬁsh Attacks in Opportunistic Networks