Fuzzy Based System for detecting and preventing
Flooding attacks on MANET
Sanjeet Kaur
Computer Sc. and Engg.
MATS University
Raipur,India
[email protected]
Abstract— Mobile Ad Hoc Networks (MANET’s) are formed
dynamically by a sovereign system of nodes that are connected
via wireless links without any extant infrastructure. In an ad hoc
network the nodes can communicate with any other node that
resides within its transmission range and for communication with
the nodes outside the transmission range the nodes utilize the
intermediate nodes to reach to the destination. The nodes in
MANET acts both like host and the router. MANET does not
have any clear line of defense and each and every node has to be
prepared for the encounters with the antagonist directly or
indirectly. Flooding attacks comes under the Denial of Service
(DoS) attacks as the malicious node attempts to send a large no.
of fake route request control packets inside the network thus
preventing the authenticated nodes from being using the
intended services and consuming the whole network resources. In
this paper we propose a Technique which uses Fuzzy based
system for the detection of the malicious node present inside the
network. The schemes uses Fuzzy If-Then rules and the
membership functions to define the threshold limit through
which it will identify the misbehavior of the nodes present in the
network and declares it as a flooder or malicious node . The
measurements were taken in the light of throughput, end-to-end
delay and Packet delivery ratio. Simulation is done in NS-2.
Keywords—MANET; AODV; RREQ; Flooding Attack; Fuzzy
System, Membership Function.
I.
INTRODUCTION
A mobile unintended network (MANET) could be a
unendingly self-configuring, infrastructure-less network of
mobile devices connected while not wires. Unintended is Latin
and suggests that "for this purpose". Each device during a
Manet is unengaged to move severally in any direction, and
can so modification its links to alternative devices of times.
Every should forward traffic unrelated to its own use, and so
be a router. The first challenge in building a MANET is
militarization every device to unendingly maintain the
knowledge needed to properly route traffic. Such networks
could operate by themselves or is also connected to the larger
web. They'll contain one or multiple and totally different
transceivers between nodes. This leads to a extremely dynamic,
autonomous topology.
As security is a major issue in MANET, two approaches
have been defined for protecting the network from threats. The
first one is reactive approach which seeks to detect the security
threats and react according to that. The next approach is the
Mr. Nilmani Verma
Computer Sc. and Engg.
MATS University
Raipur, India
[email protected]
proactive approach which attempts to prevent an attacker from
launching
attacks
through
various
cryptographic
techniques[16].
Fig.1 Mobile Ad hoc Network
Detection techniques in MANET’s are divided into 2
primary categories –Signature Based and Anomaly Detection
[8].Each attack has a different pattern. In Signature based, the
network traffic is compared with the predetermined patterns of
known attacks. Any pattern that is matched is treated as an
attack. In Anomaly detection, firstly a model of normal traffic
is created , if the input traffic is deviated from the created
model , it is detected as anomaly. In [11] a clustering behavior
based technique is used to avoid the impact of RREQ flooding
attack in AODV protocol. This technique used is On-Demand.
In this paper a new technique has been proposed which uses
the Mamdani based Fuzzy inference system for the detection of
flooding attacks on MANET and a prevention technique to
avoid such attacks in the network.
The rest of the paper is organized as follows, In Section II
Problem Statement is presented & impact of flooding attacks
on MANET is discussed. Section III Related work have been
discussed. In Section IV Background and Terminology ,
Section V discussion about NS-2 and Fuzzy system is
presented, Section VI consists of Proposed Methodology,
Section VII contains simulation parameters and Results &
Section VIII contains Conclusion and Future work of the paper.
II.
PROBLEM STATEMENT
In MANET security is a paramount concern, because
MANET system is more vulnerable to malicious nodes than the
wired networks. Firstly, the use of wireless links relinquishes
the network influenced to attacks ranging from passive
eavesdropping to active interference. Secondly the mobile
nodes are capable of roaming independently as they are
autonomous units, this means that if the nodes have improper
physical protection they are more receptive of being captured,
compromised and hijacked. Thirdly, decision making is
sometimes decentralized in mobile computing environment and
some of the wireless networking algorithms entrust on the
cooperative participation of all the nodes and the framework.
In mobile ad hoc network there is a mutual trust between
the sender and the receiver in the network due to which any
malicious node can enter into the network and affect the
network resources. The affected resources may be the battery
and the bandwidth. The malicious nodes can also cause damage
to the network through leaking secret information,
contaminating the message and node impersonation.
III.
RELATED WORK
Fatemeh Barani and Sajjad Gerami et, al [2013] proposed a
technique named ManetSVM for detection of flooding, black
hole, neighbor, rushing, and wormhole attacks have been
evaluated with regards to the efficiency of ManetSVM. In this
paper, author tends to plan a dynamic approach supported oneclass support vector machine, known as ManetSVM, for
anomaly detection in MANETs. Our approach has 3 main
phases: initial coaching, updating, and detection. Within the
initial training section, a model is generated exploitation the
OCSVM algorithm to explain the traditional area. Within the
detection section, the generated model is employed to discover
abnormal input traffic. In the change section, the model is
updated supported network topology changes. The NS2
machine was accustomed conduct the simulation study in
MANETs and a few attacks on AODV routing protocol.
ManetSVM may be a quick approach with high accuracy that's
appropriate for mobile ad-hoc networks. Two measures of
detection rate (DR) and warning rate (FAR) square measure
used for our experiments and analysis of the planned approach.
Author tends to compare the performance of ManetSVM with
three different dynamic approaches. The results incontestible
that ManetSVM will increase the common detection rate by a
minimum of 0.53% and 8.39% and therefore the average
warning rate by a minimum of 6.61% and 3.74% that of DCAD
and WPCA. Within the different experiment, the impact of
various lengths of the time window on the performance of
ManetSVM was evaluated. The shortcoming of this technique
is that SVM uses binary classifiers, so there is a constraint on
the classifiers being used[8].
Fatehmeh Barani et, al [2014] proposed a hybrid approach
for dynamic intrusion detection using Genetic Algorithm and
Artificial Immune System called GAAIS. GAAIS is in a
position to adapting itself to topology changes victimization 2
change methods: partial and total. Each normal feature vector
extracted from network traffic is represented by a hyper sphere
with fix radius. a group of spherical detector is generated
victimization Niche MGA rule for covering the non self area.
Spherical detectors are used for detective work anomaly in
network traffic. The performance of GAAIS is evaluated for
detective work many kinds of routing attacks simulated using
the NS2 machine, like Flooding, Black hole, Neighbor,
Rushing, and Worm hole. Performance of this approach has
been evaluated by multiple experiments for detection of some
routing attacks like Flooding, Black hole, Neighbors, Rushing
and Worm hole.
Meenakshi Patel, Sanjay Sharma and Divya sharan et, al
[2013] proposed a detection and prevention scheme for
flooding attacks using SVM. In this methodology ab initio
collect the behavior of each node then mistreatment of this
knowledge to seek out out the flooded malicious node. For this
collected behavior of each node pass the support vector
machine and check this to threshold limit if the node cross the
edge limit they're discover as a malicious node through the
SVM.
Author has used this method for bar. SVM are put in on
some node for sleuthing malicious node once detection this
node broadcast acknowledgement message to all or any then all
nodes update their routing table and delete the entries of
malicious node. If the nodes modification the threshold limit
for this method it'll notice by the opposite node. In this theme
we tend to outline a standard profile of node victimization its
communication activity to alternative nodes (like PDER, CO,
PMIR) if any node deviate from their regular profile that means
the node is abnormal and additional use this bar mechanism
can go for stop such sort of activities.
In this paper author has got to discuss a Flooding attack and
there have an effect on of the network. Flooding is another kind
of attack launched victimization routing request. During this
paper author has got proposed an answer for locating and
interference of Flooding attacks. The methodology is
straightforward and quick and that it can be implement on the
planned theme victimization NS-3 machine. In future this
technique can implement to find different kind of attacks [2].
One of the major drawback of this technique is that SVM
constraints on the number of classifiers, and once the kernel
function is defined it cannot be changed in SVM, and SVM are
not having real time implementation. This leads us to propose a
new scheme based on Fuzzy system as these systems are easy
to build because of their human intuitive rules. And have real
time implementation.
IV.
BACKGROUND AND TERMINOLOGY
A. Flooding Attacks
Flooding attacks can be categorized as Denial-of-Service
(DoS) attacks. In this attack , a malicious node takes the
advantage of the route discovery phase of the AODV routing
protocol. The malicious node aims to consume the network
resources by sending a large number of fake route request
(RREQ) packets to the non-existent destinations in the
network. Since the destination does not exist in the network so
there will be no node to send the route reply (RREP) packet
and all the nodes keep on flooding the network with RREQ
packets because of this new routes can no longer be added and
the network is unable to transmit the data packets.
Thus it leads to the congestion in the network and the route
table overflows in the intermediate nodes so that the nodes are
not able to receive new RREQ packets, resulting in DoS
attacks.
IDENTITY combine. If the node is not the supposed
destination, then the reverse route for the supply node is either
designed or updated also because the RREQ package is extra
broadcasted. A node generates a RREP if it's itself this
destination of the packet or it offers a vigorous and valid path
to the holiday spot. The RREP package is unicast back on the
creator node on the reverse journey. once an honest
intermediate node receives the RREP thought, it first creates or
maybe updates forwards route access in their route table before
forwarding the concept to their next mount the supply node.
Route Maintenance Step:
Fig. 2 Flooding attack
B. AODV routing Protocol
Ad-hoc on-demand Distance Vector routing protocol uses
on-demand route discovery technique to ensure loop free,
single path, hop by hop distance vector routing. AODV
operates in 2 sub phases. Route discovery section is initiated by
a supply node not having valid route to a destination node to
that it desires to send knowledge. Route maintenance section
for handling dynamic topology in Edouard Manet changes as
the node moves or once some error persists. When a node
desires to send knowledge to some destination it floods Route
Request (RREQ) messages to any or all its neighbouring nodes.
associate degree intermediate node receiving RREQ updates its
routing table with reverse route entry to the supply node if
RREQ is exclusive. Source id and broadcast id determines
individuality of a RREQ packet. associate degree intermediate
node will more rebroadcasts RREQ to its neighbors or unicasts
RREP message back to the supply node if it already has
unexpired route to it destination in its routing table otherwise
destination node replies.
In AODV, a node will receive multiple RREP messages for
one route discovery message sent however it maintains just one
entry per destination in its routing table. associate degree
intermediate node invariably forwards initial RREP message
received when creating entry for forward path towards
destination in its routing table and second RREP for a specific
RREQ is employed for updating table and forwarded providing
RREP has higher destination sequence variety for the
destination o hop count is smaller just in case of same
destination sequence variety otherwise RREPs area unit
suppressed. Higher sequence variety ensures underclassman
route. HELLO messages area unit changed for maintaining
neighborhood property.
AODV works on 2 phases to work out a route they're as
follows:
Route Discovery Phase:
In the route breakthrough part, a node disseminates some
kind of RREQ thought once the concept determines it wants a
path to a resort area and does not have one available in its
direction-finding table. The perennial process connected with
RREQ package at intermediate nodes is prevented just by
checking for the creator informatics address and RREQ
In this route Maintenance part, a node starts a path error
(RERR) idea, if the thought detects an internet link break for
ensuing hop of an energetic path in their routing table or the
thought gets some form of information package destined into a
node that terribly straightforward have an active route and it
isn't trying any near repairing. Upon deed the RERR message
the origin node either tries a decent route throughout its
direction-finding table or perhaps reinitiates path discovery
course of action.
V.
NS-2 NETWORK SIMULATOR AND FUZZY SYSTEM
NS version two (ns-2) was initiated supported a refactoring
by Steve McCanne. Use of Tcl was replaced by MIT's Object
Tcl (OTcl), associate object-oriented non-standard speech Tcl.
The core of ns-2 is additionally written in C++, however the
C++ simulation objects are coupled to shadow objects in OTcl
and variables may be coupled between each language realms.
Simulation scripts are written within the OTcl language,
associate extension of the Tcl scripting language.
Fuzzy abstract thought is that the method of formulating the
mapping from a given input to associate output victimization
formal logic. The mapping then provides a basis from that
selections may be created, or patterns discerned. The method of
fuzzy abstract thought involves all of the items that square
measure delineates in Membership Functions, Logical
Operations, and If-Then Rules. The point of symbolic logic is
to map associate degree input area to associate degree output
area, and therefore the primary mechanism for doing this is
often a listing of if-then statements known as rules.
The basic structure of Fuuzy consists of 3 conceptual
components:
A. Membership Functions:
A membership function (MF) may be a curve that defines
however every purpose within the input house is mapped to a
membership price (or degree of membership) between zero and
one.
B. Logical Operations:
The most necessary issue to appreciate concerning fuzzy
logical reasoning is that the undeniable fact that it's a superset
of normal Boolean logic. In alternative words, if you retain the
fuzzy values at their extremes of one (completely true), and
zero (completely false), customary logical operations can hold.
C. If-Then Rules:
A single fuzzy if-then rule assumes the shape ,
if x could be A then y is B
where, A and B are unit linguistic values outlined by fuzzy
sets on the ranges (universes of discourse) X and Y, severally.
The if-part of the rule "x is A" is termed the antecedent or
premise, whereas the then-part of the rule "y is B" is termed the
resultant or conclusion.
Mamdani Fuzzy Inference System:
Mamdani's fuzzy abstract thought technique is that the most
typically seen fuzzy methodology. Mamdani's technique was
among the primary management systems engineered
victimisation fuzzy pure mathematics. It had been planned in
1975 by Ebrahim Mamdani as a trial to manage a externalcombustion engine and boiler combination by synthesizing a
group of linguistic control rules obtained from intimate with
human operators. Mamdani's effort was supported Lotfi
Zadeh's 1973 paper on fuzzy algorithms for advanced systems
and call processes. though the abstract thought method
represented within the next few sections differs somewhat from
the ways represented within the original paper, the fundamental
plan is far constant.
2) The communication between these nodes is possible by
using some roouting protocol, here in this proposed
methodology we have used AODV routing protocol.
3) The next step is to pass this network to a Fuzzy Based
System, which consists of a set of fuzzy rules which will
prevent the entry of any malicious nodes inside the network
before communication and once detected it prevents the whole
network from being affected by that node.
4) The Fuzzy Based Prevention System identifies the nodes in
the network to be Authenticated node or Flooded node. If the
node is Authenticated based on the defined parameters &
threshold limit , the node is allowed to go through the route
selector and participate in communication.
5) Otherwise if the node is malicious or flooded node it is
reported to the sender for taking appropriate action.
PHASE-2: Consists of Fuzzy based system which includes a
set of Fuzzy rules which will detect the node to be an Intruder
or Non-Intruder.
Mamdani-type abstract thought, as outlined for the tool
cabinet, expects the output membership functions to be fuzzy
sets. When the aggregation method, there's a fuzzy set for
every output variable that desires defuzzification.
VI.
PROPOSED METHODOLOGY
As MANET has some vulnerabilities owing to that it's
extremely vulnerable to security threats likewise as routing
attacks. The planned intrusion detection system uses formal
logic as a result of formal logic is ready to handle the
uncertainity and provides the selections supported assumptions
and unsure values. The planned work is being enforced in NS-2
that ends up in increase the performance of network. The
information is analyzed by MATLAB fuzzy tool case.
Fig.4 Fuzzy based Detection System(Phase-2)
Steps to be followed:
The work has been divided into 2 phases:
PHASE-1: Consists of Fuzzy based prevention system which
consists of set of Fuzzy rules.
1) First step is to construct a MANET simulation with attack
using NS-2.
2) This simulation will generate some trace files which
consists of all the information about the network.
3) The next step is the preprocessing of Trace files which
perform the data cleaning that is it extracts the required file
from the generated trace files.
4) Next these extracted files are saved as a sample input
network behavior file.
5) This sample input network behavior file is fed into the
Fuzzy Based System, which matches the input nodes with the
sample and based upon this matching concludes that input
node to be an Intruder or Non-Intruder.
Fig.3 Fuzzy based Detection System(Phase-1)
Fuzzy inference components:
This paper used Mamdani type fuzzy
inference system. This system used the three input parameters
i.e. Total no. of roes in the trace file, total no. of packets sent
by the node, total no. of packets received by the node and
output parameters are With attack and without attack, the
input parameters are responsible to check the behavior of the
Steps to be followed:
1) A network topology is constructed which defines the
logical connection between the nodes participating in the
MANET.
node and based on that behavior it is concluded that which
node is Intruder node. For the good performance, the
membership functions are presented below:
1.
Average End-to-End Delay(Seconds)
End to End Delay vs No. of Nodes
3.5
Flooded AODV
Normal AODV
3
End to End Delay in Secs
2.5
2
1.5
1
Fig.5 Input Membership Functions
0.5
0
50
2.
70
No. of Nodes
120
Throughput(KBPS)
Throughput vs No. of Nodes
60
Flooded AODV
Normal AODV
50
40
Throughput in KBPS
Fig.6 Output Membership Functions
Fuzzy Rules:
30
20
1. If input1 is TOTAL then output1 is WOATTACK
10
2. If input1 is SEND then output1 is WOATTACK
0
50
70
No. of Nodes
3. If input1 is RECEIVE then output1 is WOATTACK
4. If input1 is not TOTAL then output1 is ATTACK
3.
120
Packet Delivery Ratio(%)
PDR vs No. of Nodes
5. If input1 is not SEND then output1 is ATTACK
100
Flooded AODV
Normal AODV
90
6. If input1 is not RECEIVE then output1 is ATTACK
We will use this technique for prevention. Fuzzy system
will be installed on some node for detecting malicious node,
after detection this node will broadcast acknowledgement
message to all, and then all nodes update their routing table and
delete the entries of malicious node. In this scheme we define a
normal profile of simulation using its trace file entries
generated from the simulation using NS-2, if any of the trace
file deviate from their regular entries (such as total no. of rows,
max no. of packets sent and max no. of packets received by a
node) that means the file is abnormal and further use this
prevention mechanism will take to stop such type of activities.
VII. SIMULATION PARAMETERS & RESULTS
Simulation Parameters
Simulator
NS-2.35
Routing Protocol
AODV
Number of Nodes
50,70 or 120
Number of Attackers
1,2
Transmission Range
250m
Simulation Time
20 Secs
Movement Model
Random way point
Packet Size
1500 bytes
Traffic
Constant Bit Rate (CBR)
Terrine area
1000 m2
70
Packet Delivery Ratio (%)
Prevention Technique :
80
60
50
40
30
20
10
0
50
70
No. of Nodes
120
VIII. CONCLUSION AND FUTURE WORK
Flooding attack is one of the major security challenges for
MANETs. The proposed solution can be applied to protect the
network from security threats by applying Fuzzy based system
to detect the malicious nodes causing flooding in the network
and protect the network from being flooded with fake packets
which consumes all the network’s bandwidth and resources. It
is concluded that the fuzzy systems are very easy to build. The
inference rules built are taken from human intuitions and are
used to describe the behavior of the system. Fuzzy logical
thinking systems are with success applied in fields like
automatic management, knowledge classification, call analysis,
professional systems, and laptop vision. due to its
multidisciplinary nature, fuzzy logical thinking systems square
measure related to variety of names, like fuzzy-rule-based
systems, fuzzy professional systems, fuzzy modeling, fuzzy
associative memory, mathematical logic controllers, and easily
(and ambiguously) fuzzy systems.
In future, the proposed methodology identify other malicious
attacks such as Worm Hole, Sink Hole & Black Hole attack
and also compare their performances metrics with other routing
protocol such as DSDV, DSR & TORA.
[9]
[10]
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