IACSIT International Journal of Engineering and Technology Vol. 2, No.1, February, 2010
ISSN: 1793-8236
52
Abstract—Networks are protected using many firewalls and
encryption software’s. But many of them are not sufficient and
effective. Most intrusion detection systems for mobile ad hoc
networks are focusing on either routing protocols or its
efficiency, but it fails to address the security issues. Some of the
nodes may be selfish, for example, by not forwarding the packets
to the destination, thereby saving the battery power. Some
others may act malicious by launching security attacks like
denial of service or hack the information. The ultimate goal of
the security solutions for wireless networks is to provide
security services, such as authentication, confidentiality,
integrity, anonymity, and availability, to mobile users. This
paper incorporates agents and data mining techniques to
prevent anomaly intrusion in mobile adhoc networks. Home
agents present in each system collects the data from its own
system and using data mining techniques to observed the local
anomalies. The Mobile agents monitoring the neighboring nodes
and collect the information from neighboring home agents to
determine the correlation among the observed anomalous
patterns before it will send the data. This system was able to stop
all of the successful attacks in an adhoc networks and reduce the
false alarm positives.
Index Terms—Mobile agents, Intrusion detection system,
Adhoc networks, Network Security.
I. INTRODUCTION
Intrusion detection is an important part of computer
security. It provides an additional layer of defense against
computer is use after physical, authentication and access
control [5]. A mobile ad hoc network is a collection of
wireless mobile hosts forming a dynamic network
infrastructure without any standard infrastructure or
centralized administration. The flexibility in space and time
induces new challenges towards the security infrastructure.
The nature of mobility creates new vulnerabilities due to the
open medium, dynamically changing network topology,
cooperative algorithms, lack of centralized monitoring and
management points and yet many of the proven security
measures turn out to be ineffective. Therefore, the traditional
Manuscript received July 1, 2009.
R. Nakkeeran is with the Department of Computer Science and Engineering,
Dr Pauls engineering College (Affiliated to Anna University, Chennai), Vanur
Taluk, Villupuram -605 109, India. （Phone: +91 9787718933; +91 4142
329090）
S T. Aruldoss Albert is with the University Department of Electrical
Engineering, Anna University Coimbatore, Coimbatore - 641 013, India.
R. Ezumalai is with the Department of Computer Science and Engineering,
Dr Pauls engineering College (Affiliated to Anna University, Chennai)., Vanur
Taluk, Villupuram -605 109, India. (Phone: +91 9345486422).
way of protecting wired/wireless networks with firewalls and
encryption software is no longer sufficient [7]. Military,
University campuses and conference settings also gain on
account of these wireless networks since they allow easy
collaboration and efficient communication on the fly without
the need for costly network infrastructure.
Expectations are also high with respect to the use of these
networks in places like hotels, airports etc. But a vital problem
that must be solved in order to realize these applications of ad
hoc networks is that concerning the security aspects of such
networks [8].Intrusion detection is used in the networks by
comparing the set of baselines of the system with the present
behavior of the system [3]. Thus, a basic assumption is that the
normal and abnormal behaviors of the system can be
characterized. The intrusion detection community has been
deals mainly on wired networks, but it is lack of security in
wireless networks. Anomaly detection and misuse detection or
signature detection are the two techniques used for intrusion
detection system. Anomaly detection describes the abnormal
patterns of behavior, where "abnormal" patterns are defined
beforehand. Misuse detection relies on the use of specifically
known patterns of unauthorized behavior. Thus these
techniques rely on sniffing packets and using the sniffed
packets for analysis. In order to realize these ID techniques the
packets can be sniffed on each of the end hosts. This is called
as host intrusion detection (HID). It is also possible to sniff
these packets on certain predetermined machines in the
network. This is called as network intrusion detection (NID).
Mobile agents are a special type of agents defined as
"processes capable of roaming through large networks such as
the adhoc wireless network, interacting with machines,
collecting information and returning after executing the tasks
adjusted by the user". The nature of mobility in a wireless
networks creates an vulnerability due to the open medium,
dynamically changing networks. In order to avoid such
circumstance, to develop new architecture and mechanisms to
protect the wireless networks and mobile computing
applications [7].
The remainder of the paper is organized as follows, Section
2 contains related work, Section 3 describes our proposed
approach, and Section 4 describes the Results and conclusion.
II. RELATED WORK
Traditional security mechanism such as intrusion detection
system, firewall and encryption methods are not sufficient to
Agent Based Efficient Anomaly Intrusion
Detection System in Adhoc networks
R. Nakkeeran, T. Aruldoss Albert and R.Ezumalai
IACSIT International Journal of Engineering and Technology Vol. 2, No.1, February, 2010
ISSN: 1793-8236
53
provide security in an adhoc networks. Countering threats to
an organization's wireless adhoc network is an important area
of research. Intrusion detection means identifying any set of
actions that attempt to compromise the integrity,
confidentiality or availability of resource [3]. Many
techniques have been discussed to prevent attacks in an
wireless adhoc networks as follows.
Ricardo Puttini et al [16], propose design and development
of the IDS are considered in 3 main stages. A parametrical
mixture model is used for behavior modeling from reference
data. The associated Bayesian classification leads to the
detection algorithm [15]. MIB variables are used to provide
IDS needed information. Experiments of DoS and scanner
attacks validating the model are presented as well. João B. D.
Cabrera Et al [17], provides the solution of intrusion detection
in Mobile Ad-Hoc Networks (MANETs), utilizing ensemble
methods. A three-level hierarchical system for data collection,
processing and transmission is described. Local IDS
(intrusion detection systems) are attached to each node of the
MANET, collecting raw data of network operation, and
computing a local anomaly index measuring the mismatch
between the current node operation and a baseline of normal
operation. The complete suite of algorithms was implemented
and tested, under two types of MANET routing protocols and
two types of attacks against the routing infrastructure.
Yongguang Zhang et al [18], propose new intrusion
detection and response mechanisms are developing for
wireless ad- hoc networks. The wireless ad-hoc network is
particularly vulnerable due to its features of open medium,
dynamic changing topology, cooperative algorithms, lack of
centralized monitoring and management point, and lack of a
clear line of defense. Many of the intrusion detection
techniques developed on a fixed wired network are not
applicable in this new environment. Farroq et al [19] propose
the signature detection technique and investigate the ability of
various routing protocols to facilitate intrusion detection
when the attack signatures are completely known. We show
that reactive ad-hoc routing protocols suffer from a serious
problem due to which it might be difficult to detect intrusions
even in the absence of mobility. Mobility makes the problem
of detecting intruders harder.
Vijay Bhuse et al [10], propose lightweight methods to
detect anomaly intrusions in wireless sensor networks
(WSNs). The main idea is to reuse the already available system
information that is generated at various layers of a network
stack. This is the different approach for anomaly intrusion
detection in WSNs. Hongmei Deng et al [21], propose the
underlying distributed and cooperative nature of wireless ad
hoc networks and adds one more dimension of cooperation to
the intrusion detection process. That is, the anomaly detection
is performed in a cooperative way involving the participation
of multiple mobile nodes. Unlike traditional signature-based
misuse detection approaches, the proposed scheme detects
various types of intrusions/attacks based on the model learned
only from normal network behaviors. Without the
requirements of pre-labeled attack data, the approach
eliminates the time-consuming labeling process and the
impacts of imbalanced dataset.
Bo Sun et al [22], propose we first introduce two different
approaches, a Markov chain-based approach and a Hotelling's
T2 test based approach, to construct local IDSs for MANETs.
Then demonstrate that nodes' moving speed, a commonly used
parameter in tuning IDS performances, is not an effective
metric to tune IDS performances under different mobility
models. To solve this problem, author further propose an
adaptive scheme, in which suitable normal profiles and
corresponding proper thresholds can be selected adaptively by
each local IDS through periodically measuring its local link
change rate, a proposed unified performance metric.
Haiguang Chen et al [23], propose lightweight anomaly
intrusions detection. In the scheme, author investigates
different key features for WSNs and defines some rules to
building an efficient, accurate and effective Intrusion
Detection Systems (IDSs). We also propose a moving window
function method to gather the current activity data. The
scheme fits the demands and restrictions of WSNs. The
scheme does not need any cooperation among monitor nodes.
Simulation results show that the proposed IDSs are efficient
and accurate in detecting different kinds of attacks.
Gabriela F. Cretu et al [24], propose the use of model
exchange as a device moves between different networks as a
means to minimize computation and traffic utilization. Any
node should be able to obtain peers’ model(s) and evaluate it
against its own model of “normal” behavior.
Yu Liu et al [25], propose game theoretic framework to
analyze the interactions between pairs of attacking/defending
nodes using a Bayesian formulation. We study the achievable
Nash equilibrium for the attacker/defender game in both static
and dynamic scenarios. The dynamic Bayesian game is a more
realistic model, since it allows the defender to consistently
update his belief on his opponent's maliciousness as the game
evolves. A new Bayesian hybrid detection approach is
suggested for the defender, in which a lightweight monitoring
system is used to estimate his opponent's actions, and a
heavyweight monitoring system acts as a last resort of defense.
Many authors proposed different techniques to prevent
attacks in wireless adhoc networks. But all these methods
reported to have a lot of pros and cons of its own proposal. The
authors mainly classified their mechanism as signature method,
anomaly method. In Signature Based Method, a threat is
always be stored in database. New threat being discovered in
the wild and the signature for detecting that threat. This
Mechanism would be unable to detect the new threat. In
Anomaly Based Method, it monitors system and its network
behaviors. It set the baseline of network and system. This
mechanism work effectively against wireless networks but it
generates some false positive results. In this paper, a new
attempt has been made and worked out effectively against
attacks in wireless networks. This paper incorporates agent
and data mining method to provide solution against security
issues in MANET networks. With the help of home agent and
mobile agents, it gathers information from its own system and
IACSIT International Journal of Engineering and Technology Vol. 2, No.1, February, 2010
ISSN: 1793-8236
54
neighboring system to identify any attack and through data
mining techniques to find out the attacks has been made in that
networks.
III. OUR APPROACH
Our approach is entirely based on anomaly based method,
which has been used to address security problems related to
attacks in a wireless networks. This paper incorporates new
methodology such as mining and agents to provide solutions
against wireless networks. Our Proposal provides the three
different techniques to provide suffice security solution to
current node, Neighboring Node and Global networks. The
following figure clearly depicts the architecture of the system
to prevent the attacks in wireless networks. The following
section outlines each module’s work in detail.
Fig 1: Proposed System Architecture Outlines
Fig 2: Proposed System Architecture
A. Home agent
Home agent is present in each system and it gathers
information about its system from application layer to routing
layer. Our proposed system provides solution in three
techniques. 1. It monitors its own system and its environment
dynamically. It uses classifier construction to find out the
local anomaly. 2. Whenever the node want to transfer the
information from the node F to B. It broadcast the message to
E and A. Before it sends the message, it gathers the
neighboring nodes (E &B) information using mobile agent. It
calls the classifier rule to find out the attacks with help of test
train data. 3. It provides same type of solution through out the
global networks. It has been explained in the following
section.
1) Current node - Home Agent is present in the system and it
monitors its own system continuously. If an attacker
sends any packet to gather information or broadcast
through this system, it calls the classifier construction to
find out the attacks. If an attack has been made, it will
filter the respective system from the global networks.
2) Neighbouring node - Any system in the network transfer
any information to some other system, it broadcast
through intermediate system. Before it transfer the
message, it send mobile agent to the neighboring node
and gather all the information and it return back to the
system and it calls classifier rule to find out the attacks. If
there is no suspicious activity, then it will forward the
message to neighboring node.
3) Data collection - Data collection module is included for
each anomaly detection subsystem to collect the
values of features for corresponding layer in an system.
Normal profile is created using the data collected during
the normal scenario. Attack data is collected during the
attack scenario.
4) Data preprocess - The audit data is collected in a file and
it is smoothed so that it can be used for anomaly detection.
Data preprocess is a technique to process the information
with the test train data. In the entire layer anomaly
detection systems, the above mentioned preprocessing
technique is used.
B. Cross feature analysis for classifier sub model
construction
1) Each feature or character vector f in the training data set,
calculate classifier C, for each feature fi using {f1, f2... fi
-I, fi+.-f k} - Ci is learned from the training data set using
Naïve Bayesian classification algorithm. The probability
P. (filfl, f2 ...,I fi- i, f+1, ..., fk) is learned.
2) 2. Compute the average probability for each feature vector
f, and save in a probability distribution matrix M. A
decision threshold 0 is learned from the training data set.
Normal profile is created using the threshold value. If the
probability is greater than threshold value it is labeled as
normal, otherwise it is labeled as abnormal. Anomaly
detection
Input: Preprocessed train data, preprocessed test data
Output: Percentage of anomaly
1) Read processed data set file
2) Call Bayesian classifier program for training the classifier
for anomaly detection
3) Read the test data file
4) Test the classifier model with the test data file
5) Print the confusion matrix to show the actual class vs
predicted class
6) Percentage of anomaly is calculated as follows
Percentage = Number of predicted abnormal class X 100
Total number of traces
B
A
F
E
D
C
Mobile
agent
A
Home
agent
classifier
B
Home
agent
classifier
C
Home
agent
classifier
Global Integration
Mobile agent gathers information
Integration
Local Integration
IACSIT International Journal of Engineering and Technology Vol. 2, No.1, February, 2010
ISSN: 1793-8236
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C. Local integration
Local integration module concentrate on self system and it
find out the local anomaly attacks. Each and every system
under hat wireless networks follows the same methodology to
provide a secure global networks.
D. Global integration
Global integration module is used to find the intrusion
result for entire network. The aim of global integration is to
consider the neighbor node(s) result for taking decision
towards response module
IV. EXPERIMENTAL RESULTS
There are many number of attacks has been tested to prevent
attacks in wireless network. This system not only blocks the
application oriented issues and it stops some of the network
security issues. Consider this limited number of attacks and
tested with this proposed system to find out the attacks and got
a encourage results. These are the parameters has been take to
analyze the proposed system to find out the efficiency.
TABLE 1: INPUT PARAMETER CONSIDERATION
This system can tested with limited number of attacks
present in the wireless networks. It shows the encouragement
results to support the proposed system. Detection rate of
anomaly rate in our proposed system is high and it encourages
the system.
TABLE 2: DETECTION RATE OF THE PROPOSED SYSTEM
Detection Module
Detection
Rate
Anomaly Detection (A)
80%
Local Integration (D)
95.41%
Global Integration (E)
94.33%
This system act as an Intrusion prevention system to detect
and prevent the attacks. But the drawback of existing Intrusion
prevention system can generate the more false alarms, but it
may work efficiently. This system can able to stop the attacks
as well as it could not generate the false alarms and it work
effectively against the web parameter attacks. Consider this
limited number of access and tested with this proposed system
to find out the alarm rates.
TABLE 3: ALARM RATE OF THE PROPOSED SYSTEM
Detection Module
False positivity
Anomaly Detection
1.0%
Local Integration (D)
0.8%
Global Integration
0.75%
V. CONCLUSION
In this work, an anomaly detection system comprises of
detection modules for detecting anomalies in each layer. This
system is cooperative and distributive; it considers the
anomaly detection result from the neighbor node(s) and sends
the current working node's result to its neighbor node(s).
Experimental results show that detection rate is increased
when compared to the other mechanism. False positive rate is
also reduced in this mechanism. Traditional security
mechanism such as IDS and firewall have not been sufficient
to provide the security of wireless networks, however, this
mechanism is able to block abnormal approach to wireless
networks and to detect previously unknown attacks as well as
variations of known attacks.
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30
2000x 2000 Meters
DSR
Random way point
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Mr. R. Nakkeeran pursuing PhD in Anna
University Coimbatore and received the M.E
Degree in Computer Science and Engineering
from Jayam College of Engineering and
Technology , Anna University, Chennai. His
current research involves In Data Mining and
mobile Computing. Presently working with Dr.
Pauls Engineering College (Affiliated to Anna
University, Chennai) as Professor in the
Department of Computer Science and
Engineering.