AT2A: Defending Unauthenticated Broadcast Attacks in Mobile Wireless Sensor Networks

(1)

Copyright © 2014 IJECCE, All right reserved

AT2A: Defending Unauthenticated Broadcast Attacks in

Mobile Wireless Sensor Networks

Meisam Kamarei

School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

Email: [email protected]

Ahmad Patooghy

Department of Computer Engineering, Iran University of Science & Technology, Tehran, Iran

Mahdi Fazeli

Department of Computer Engineering, Iran University of Science & Technology, Tehran, Iran

Mohammad Javad Salehi

Abstract– In a Mobile Wireless Sensor Network (MWSN), mobile sensor nodes move freely at the network environment. So, a large number of attacks threaten this network. Unauthenticated broadcast attack is a common attack which

worsens the network congestion as well as energy

consumption. In this paper, an efficient method for defending unauthenticated broadcast ATTAcks, AT2A, in MWSN has been proposed. AT2A method keeps sending history of each node in transmission table of nodes to make decision about each incoming packet, i.e., packet should be dropped or forwarded. After receiving a new packet in each node,

AT2Amethod forwards the packet with probability of − ,

i.e.,AT2Amethod detects unauthenticated packet and drops it with probability of . Indeed, AT2Amethod does not process incoming packets for unauthenticated packet detection; rather AT2Amethod considers sender node behavior to packet drop/forward. Simulation results which have been implemented using NS-2 simulator show thatAT2Amethod detects 98% unauthenticated packets by three nodes while it

increases filtering efficiency compared with previous

methods.

Keywords – Attack, Energy Efficiency, Security, Unauthenticated Packets, Wireless Mobile Sensor Networks.

I. I

NTRODUCTION

A Wireless Sensor Network (WSN) can be either

stationary or mobile[1].A Mobile Wireless Sensor Network

(MWSN) also called Wireless Mesh Network (WMN)is an

Ad-hoc network that comprises of a large number of

mobile sensor nodes which establishes in the assumed

environment to event detection and event occurrence report

transferring

through

multi-hop

and

wireless

communications to one or more base station [2-4]. The

base station is an interface between mobile sensor nodes

and network users (See Fig. 1).

After network

establishment, the mobile sensor nodes can move to

various parts of the network, freely. Due to ease of

implementation, rapid development, low cost of design and

high flexibility, these networks have been applied in a wide

range of applications such as health care[5], animal

monitoring in nature[6-7]and environment monitoring[8].

In these applications and such applications that the network

is developed in the widespread area, using mobile sensor

nodes is more efficient than stationary sensor nodes.

Because mobile sensor nodes can move in environment,

they can cover more area. They also improve the network

load- balancing [3, 9]. The mobile sensor nodes are tiny

and inexpensive devices, and they are very limited in

hardware resources such as computing resources, power

supply, bandwidth and memory [11]. Environmental

conditions and large number of mobile sensor nodes imply

that maintenance and repairing of MWSNs is impossible.

So, self-organization of mobile sensor nodes is the nature

of these networks [12].

Fig.1. Ad-hoc architecture of a mobile wireless sensor

network[10].

The network operation without any surveillance leads to

several

security

attacks

threat

MWSNs

[13].

(2)

Copyright © 2014 IJECCE, All right reserved

the malicious nodes have enough perception from the

network MAC layer [14]. On the other hand, the malicious

nodes are unable to penetrate within the network.

In this paper an efficient method for defending

unauthenticated packets broadcast ATTAcks, AT2A, to a

MWSN is proposed.AT2A method tries to recognize of the

malicious nodes instead unauthenticated packets. AT2A

method monitors to nodes behavior and it does not transfer

packets that the malicious nodes are sender of the

packet.AT2Amethod assigns a transferring timer to each

node. The transferring timer is restarted at regular time

intervals or after a packet transferring. If a mobile sensor

node excessively injects packets within the network, then

AT2Amethod blocks it and AT2Amethod considers this

node is the malicious node. AT2Amethod without the need

to

packet

processing

performs

recognition

of

unauthenticated

packets

and

it

does

not

allow

unauthenticated packets injection within a network of the

malicious nodes.

The rest of the paper is organized as follows. Section Π

summarizes the background and related work. In Section

Ш the proposed unauthenticated packets detection method

is explained. Simulation results are presented and discussed

in Section IV. Finally, in Section V obtained results and

future work are explained.

II. R

ELATED

W

ORK

Confidentiality and integrity of the data in the network

are the main goal of related methods of the more

researchers to security issue in wireless sensor networks.

Due to limited energy supply of sensor nodes,

energy-efficiency plays very important role in these networks. The

main components of power consumption in MWSNs are

power consumed for packets send/receive and power

consumed for packet processing respectively. There are

various security attacks which increase in network energy

consumption. The malicious nodes infiltrate to network

MAC layer and they amplify in the network energy

consumption as well as the network congestion [18]. There

are several MAC security attacks such as collision attack

[19], unintelligent replay attack[16], unauthenticated

broadcast

attack[16],

full

domination

attack[16],

exhaustion attack[16], intelligent jamming attack[16, 20].

Unauthenticated broadcast attack is such a MAC security

attack to MWSN. In this case, the malicious nodes

broadcast a large number of unauthenticated packets within

the network to increase in network congestion as well as

the network energy consumption. Unauthenticated packets

may be created and propagated as hello message or

spurious event occurrence report[14].The base station can

distinguish unauthenticated packets, but sensor nodes are

unable to distinguish between original and unauthenticated

packets. Because wanderer packets within MWSNs are

very small data packets which contain small information

such as node ID or event occurrence report. On the other

hand, sensor nodes are very limited in computing

resources, so they cannot perform complex processing on

incoming packets to recognition of

unauthenticated

packets.

In [12] a method that called EEMDTS based on

multipath data transferring scheme to unauthenticated

packets detection and filtering has been proposed.

EEMDTS method uses redundancy in routing algorithms as

multipath routing to prevent access of event information by

the malicious node. A method based on filtering scheme

has been proposed in

[14]. This method filters

unauthenticated packets

based on the geographical

information and the neighbor nodes information. A method

for defending against denial-of-sleep attack has been

offered in [16].This method classifies denial-of-sleep attack

based on malicious node’s knowledge of MAC layer. A

Virtual Energy Based Encryption and Keying, VEBEK, has

been offered in [21]. VEBEK uses the residual energy of

sensor nodes to data encryption on forwarded packets.

Indeed,

VEBEK

encrypts

transferred

packets

to

unauthenticated packets detection by sensor nodes. In [19]

a method for collision attack has been proposed. In

collision attack, the malicious node causes network

congestion and does not allow packet transmission by its

neighbors. In [22] a secure MAC protocol has been

proposed which acts based on CTS/RTS mechanism. In

[23] a method to attack detection has been proposed which

uses neural network to attack detection. A Dynamic

En-route Filtering, DEF, has been proposed in[24]. DEF uses

redundancy along forwarded packets as authentication

keys. Then, receiver node can detect and drop

unauthenticated packet by authentication keys. Some

related methods perform complex processing on incoming

packets. Complex processing on incoming packets for

recognition of unauthenticated packet increases the sensor

node energy consumption and requires powerful computing

and processing resources. On the other hand, the more

related methods have been proposed to stationary WSNs

and they cannot be used to MWSNs.

III. U

NAUTHENTICATED

P

ACKET

D

ETECTION

M

ETHOD

(AT2A)

In this section, we introduce the mobile sensor node

structure and our method to detect unauthenticated packets

broadcast ATTAcks, AT2A, respectively. First assumption

that is considered in AT2Amethod is that each mobile

sensor node has two input/output buffers to receive and

forward the packets, according to Fig. 2. The packets after

arrive to a mobile sensor node are placed into the input

buffer. The incoming packets are serviced as First

Input-First Serviced, FCFS, policy. After forereach packet

service time, the mobile sensor node to transmit it, locates

the packet into the output buffer with

1 −

probability.

The mobile sensor node does not send the packet and drops

it with

probability[25]. Indeed,

parameter is probability

that received packet be an unauthenticated packet. Thus,

each

mobile

sensor node detects and drops an

unauthenticated packet with probability of .

(3)

Copyright © 2014 IJECCE, All right reserved

column is time passed of the last forwarded packet from

each neighborhood node. After receive a newly packet by

each node, the mobile sensor node searches its transmission

table to extract information about the packet previous hop.

The packet previous hop has been placed on packet header

and it is previous node that has created or relayed the

packet. If there is not previous node ID in transmission

table, then receiver node inserts previous hop ID and value

0 to its transmission table. It indicates that previous node

have recently been placed on neighborhood of receiver

node. Node movement changes situation of transmission

tables during the network lifetime. On the other hand, if

previous node ID be available in transmission table, then

the receiver node extracts value of timer column to

probability calculation.

AT2A method forwards or drops packets based on

transmission table. So, after packet arrival to each node,

AT2A method extracts history of previous hop from

transmission table. Then, AT2A method makes decision

about the packet i.e., packet must be forwarded or dropped.

AT2A method forwards packet with probability

1 −

anddrops it with probability

. If the incoming packet

obtains forwarding chance, AT2Amethod first restarts

timer field of previous hop in transmission table and then,

AT2A method replaces receiver node ID in previous hop

field on packet header. AT2A method calculates

parameter to each node based on Equation (1).

= 1 −

( )

₍₁₎

where

is time passed of the last packet forwarding from

neighborhood node

and

is penalty level of

neighborhood node

. Parameter

is extracted from

transmission table of receiver node.

and

are restarted

in two state: 1) at regular time intervals, 2) after each

packet forwarding from neighborhood node . Penalty level

parameter,

, is calculated based on time interval

between the last packet forwarding time and current packet

arrival time from neighborhood node . To penalty level

calculation of each neighborhood node, AT2A method

divides

Fig.2. Structure of typical mobile sensor node[25].

Fig.3. Time interval division to

slots after packet

forwarding

Table І: A Typically Transmission Table.

Timer

Node ID

4

18

8

9

0

10 forwarding time interval to

slots, according to Fig.

3.Ifimmediately after a packet forwarding, a newly packet

from neighborhood node is reached to the receiver node,

then node

with high probably is the malicious node.

Therefore, AT2A method reduces the packet forwarding

chance with increase in penalty level parameter of node .

Equation (2) calculates penalty level of neighborhood

node .

=

+ (

−

+ 1) ×

1 ≤

≤

(2)

where is number of time slot that a newly packet has been

reached to receiver node from neighborhood node . Also,

is time slot length and

is the number of all time slots.

Based on Equation (2) it can be seen that AT2A method

tends to penalty mobile nodes that injects a lot of packets

within the network.

Pseudo-code of AT2Amethod is shown in Fig.4.

According to this figure, after arrive a newly packet of an

event to a mobile sensor node, this node extracts

information

about

packet previous hop from its

transmission table. Then parameter

is calculated based on

packet arrival time, therefore penalty level of previous hop

is calculated. If packet obtains forwarding chance, the

mobile sensor node forwards packet toward base station,

also it restarts the timer field of previous hop in

transmission table. If packet does not obtain forwarding

chance, receiver node drops packet and does not send it.

Indeed, AT2A method considers the previous hop is a

malicious node, this state occurs with probability of .

WSNs are event-driven and real-time networks. Of

course, these networks are very redundant, so each point of

the network is covered by several nodes. Obviously, after

each event occurrence several sensor nodes detect and

report it to a base station. So, packets that are made and

injected within then network by a node with high probably

are unauthenticated packets. AT2Amethod does not

process incoming packets to recognize unauthenticated

packets, so AT2Amethod is an energy efficiency method

and it acts in distributed manner. Indeed, AT2A method

tries to recognize malicious nodes instead recognize of

unauthenticated packets. In AT2A method, malicious

nodes recognition is performed based on node behavior

history in the network. So, AT2Amethod monitors to

history of nodes activity within the network. Therefore,

those mobile sensor nodes that propagate a large number of

packets within the network are fined by AT2A method.

Therefore, AT2Amethod does not allow to malicious nodes

to propagate unauthenticated packets into the network to

increase in the network energy consumption.

Input

: A received packet from neighborhood node

Output

: Packet forwarding or packet dropping

←

λ

← Select from transmission table where

equal

(4)

Copyright © 2014 IJECCE, All right reserved

If

=

Ø

then

Insert into transmission table ( ,0)

Forward Packet

Else

If

.

>λ

.

← 0

←0

Else if

.

≤

then

←

+

×

Else if

.

≤ 2 ×

then

←

+ (

− 1) ×

.

Else if

.

≤

×

then

←

+

←

1 −

( . )

← Create Random number between 0 & 1

If

>

then

Forward Packet

Else

Drop packet

End

Fig.4. Pseudo-code of AT2Amethod.

IV. E

XPERIMENTAL

R

ESULTS

In this section, AT2A method performance has been

evaluated via NS-2.35 simulator. Different parameters of

simulation are presented in Table.2. Communication

parameters for MWSN are adopted based [12]. Also, the

general parameters required for energy consumption are

adopted from [12]. In Table.2,

is the energy consumed

by electronic circuit for receiving or forwarding one bit of

data,

is the energy consumed by sender node for

forwarding one packet and

is energy consumption for a

live state of sensor nodes.

is energy consumption of

sensor nodes for

unauthenticated packet detection.

Because, AT2A method does not process packets to

unauthenticated packet detection, so

in AT2Amethod

is equal to

0 .AT2Amethod has been proposed to MWSN

with mobile sensor nodes, so mobile nodes can move at

environment freely. Of course, mobile nodes move to

across the network with random speed in assumed range of

node speed. MWSNs are event driven network which after

each event occurrence the network traffic increases. Thus,

the network traffic is dependent on event occurrence rate.

In Table.2,

is event occurrence rate and adopted based on

[14]. Of course, we consider amount of regular time

intervals of transmission table which sensor node timer is

restarted is equal with event occurrence rate. Thus,

forwarding time of each node is divided to

time slot as

Equation (3).

=

×

(3)

Table II: Simulation Parameters.

1000

m

×1000

m

Terrain

500 Node Number

250 Radio Range

100 Sim Time

1000

Initial Node Energy

Propagation Model

20-30

Number of

the

malicious

nodes

/ 802_15_4

MacType

0

10 /

Speed of the mobile sensor

nodes

2

0.1

0.33

0.05

0 Filtering efficiency

is defined as the percentage of

unauthenticated packets which to be filtered within a

specified

number of nodes[12].AT2Amethod

detects

unauthenticated packets based on history of previous hop

activity.In AT2A method, if the malicious node broadcasts

a lot of unauthenticated packets, first its one-hop

neighborhood detects it. Therefore, one-hop neighborhood

does not allow to be propagated a lot of unauthenticated

packets. Fig. 5, shows filtring efficiency of AT2A method

with

some related

methods EMDTS[12], DEF[24],

VEBEK[21]. According to Fig. 5, it can be seen that

AT2A method filters 98% unauthenticated packetsby 3

nodes.EEMDTS filters 95% of unauthenticated packets by

5 nodes, VEBEK filters 90% of unauthenticated packets

by10nodes and DEF filters 90% of unauthenticated

packets by 12 nodes.

Fig. 6, shows the average number of hops that

unauthenticated packets are traversed within the network

before AT2A method detects them versus number of the

malicious nodes. Because in AT2A method one-hop

neighborhood of malicious node plays very important role

to unauthenticated packet detection, so increase in number

of the malicious nodes no effect on number of traversed

hops by unauthenticated packet.

Fig. 7, shows effect of node pause time on amount of

unauthenticated packets detection. Node pause time has

inverse relationship with the network dynamically, i.e., the

lower the node pause time, the higher the network

dynamically as well as the higher the mobility of sensor

node[26]. AT2Amethod detects 95% unauthenticated

packets by one-hop neighborhood of malicious node. So,

based on Fig. 7, variations in the node pause time have no

effect on percentage of detected unauthenticated packets.

According to Fig. 7, AT2A method can be used in

stationary or mobile sensor network and its function does

not change relative to the mobility of sensor nodes.

(5)

range of regular time intervals versus percentage of

unauthenticated packets detection and packets delivery

rate. Based on this figure, it can be seen increase in length

of time intervals leads to decrease packet delivery rate.

Increase in time intervals leads to increase in parameter .

So, inordinate elimination of incoming packets result to

decrease in delivery rate as well as increase in incoming

packets elimination. According to Fig. 8, when time

interval length is equal to event occurrence rate, , AT2A

method has the best performance(See Equation (3)). Due

to, mobile sensor nodes are permissible for sending a

packet of newly event occurred at each time interval.

Fig.5. Unauthenticated packet filtering efficiency.

Fig.6. Number of hops traveled by unauthenticated packet

vs the malicious node

Fig.7. Node pause time vs. Unauthenticated packets

detection.

Fig.8. Length of time interval vs. Unauthenticated packets

detection & Delivery rate.

V. C

ONCLUSION

Mobile wireless sensor networks are threatened by

various security attacks. Due to, these networks are

established in harsh environment and mobile wireless

sensor networks operate without any surveillance. In this

case, an unauthenticated packet broadcast within the

network to increase in network energy consumption is a

common attack to mobile wireless sensor networks.In this

paper, an efficient method, AT2A, for unauthenticated

packets

recognition

and

dropping

was

proposed.

AT2Amethod tries to recognize of the malicious nodes that

propagate the more packets within the network. Hence,

AT2A method keeps packets forwarding history of each

node in a transmission table. Then, AT2A method uses the

transmission table to the malicious nodes identification i.e.,

the more the packets transferring, the more the probability

ofthe malicious node.AT2A method drops or forwards

packet based on behavioral history of mobile sender node

of the packet which is available on the transmission table.

AT2A method preventsofpenetration of packetsthat have

been created or relayed by the malicious nodes within the

network.AT2A method is a distributed and flexible method

for wireless sensor networks and mobile wireless sensor

networks.

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A

UTHOR

’

S

P

ROFILE

Mahdi Fazeli

received the M.Sc. and Ph.D. degree in computer engineering from the Sharif University of Technology, Tehran, Iran, in 2005 and 2011, respectively. He has been with the department of computer engineering, Iran University of Science and Technology (IUST), since 2011, where he is currently an Assistant Professor. He has established and chaired the Networked & Embedded System Laboratory (NESL) at IUST, since 2012. He has authored or co-authored more than 70 papers in reputable journals and conferences. His current research interests include reliable issues in VLSI circuits and emerging technologies, hardware security, dependable embedded systems, Low power circuits and systems, fault-tolerant computer architectures, and reliability modeling and evaluation.

Ahmad Patooghy

received his M.Sc. and Ph.D. in computer engineering from Sharif University of Technology, Tehran, Iran, in 2005 and 2011, respectively. He is currently an assistance processor at department of computer engineering, Iran University of Science &Technology,Tehran, Iran. His research interests include hardware design and test, architectural design of multi- and many-core chips, dependability and security evaluation of VLSI circuits, fault injection,, and analytical modeling. Dr. Patooghy initiated the "Dependable Systems & Architectures Laboratory" at Iran University of Science and Technology in 2012 and has chaired the Laboratory since then.

Meisam Kamarei

received his M.Sc. in computer engineering from Islamic Azad University, Arak Branch, Arak, Iran, in 2013.He is currently a faculty member at University of Applied Science and Technology (UAST), Iran. Hiscurrent research interests include data aggregation and data collection in wireless sensor networks, wireless sensor networks modeling & evaluation, security in wireless sensor networks, learning automata.

Mohammad Javad Salehi

received his M.Sc. in computer engineering from Islamic Azad University, Khomein Branch, Khomein, His current research interests include distributed systems, cloud computing, trust management, service-oriented architecture.

A

UTHOR

’

S

P

ROFILE

Mahdi Fazeli

Ahmad Patooghy

Meisam Kamarei

Mohammad Javad Salehi

received his M.Sc. in computer engineering from Islamic Azad University, Khomein Branch, Khomein, His current research interests include distributed systems, cloud computing, trust management, service-oriented architecture.