Communication and Network, 2010, 2, 54-61
doi:10.4236/cn.2010.21008 Published Online February 2010 (
Copyright © 2010 SciRes CN
Designing Intrusion Detection System for Web
Documents Using Neural Network
Hari Om, Tapas K. Sarkar
Department of C om put er Science and Engin eeri ng , In di a n Sch o ol of Mi nes, Dhanbad, In di a
Received November 17, 2009; accepted December 29, 2009
Abstract: Cryptographic systems are the most widely used techniques for information security. These systems
however have their own pitfalls as they rely on prevention as their sole means of defense. That is why most of
the organizations are attracted to the intru sion detection systems. The intrusion detection systems can be broadly
categorized into two types, Anomaly and Misuse Detection systems. An anomaly-based system detects com-
puter intrusions and misuse by monitoring system activity and classifying it as either normal or anomalous.
Misuse detection systems can detect almost all known attack patterns; they however are hardly of any use to de-
tect yet unknown attacks. In this paper, we use Neural Networks for detecting intrusive web documents avail-
able on Internet. For this purpose Back Propagation Neural (BPN) Network architecture is applied that is one of
the most popular network architectures for supervised learning. Analysis is carried out on Internet Security and
Acceleration (ISA) server 2000 log for finding out the web documents that should not be accessed by the unau-
thorized persons in an organization. There are lots of web documents available online on Internet that may be
harmful for an organization. Most of these documents are blocked for use, but still users of the organization try
to access these documents and may cause problem in the organizati on network.
Keywords: intrusion detection system, neural network, back propagation network, anomaly detection,
misuse detection
1. Introduction
The information is the most important resource that
must be managed efficiently. Besides management, its
protection is also very important as it may lead to eco-
nomic losses in today’s electronic environment. For
example, we can control our bank accoun ts from almost
anywhere in the world using a suitab le network, such as
satellite and cellular phone networks to interact with the
bank representatives, or the specialized wired ATM
networks and the Internet for online banking services.
The services supported by networks are very much
useful and efficient, but these can be subverted by un-
scrupulous elements for their own benefits. So, suitable
mechanism needs be employed to protect the informa-
tion. In a survey of fraud against auto teller machines
[1], it is reported that the patterns of fraud depends on
those who were responsible for implementing and man-
aging the systems. In USA, if a customer disputes a
transaction, this is the responsibility of the bank to
prove that the customer is mistaken or lying. This
forced the US banks to protect their systems properly.
But, in Britain, Norw a y and the Ne therlands, th e b ur d en
of proof lies on the customer. The bank is right if the
customer could not prove it wrong. That is why the
banks in these countries became careless. Eventually,
epidemics of fraud demolished their satisfaction and in
the meanwhile the US banks suffered much less fraud.
Though they spent less money on security than their
European counterparts, yet they spent it more effec-
tively [2]. A different kind of incentive failure was also
seen in early 2000 with distributed denial of service
attacks against a number of high profile websites.
Those attacks exploited a number of weak machines to
launch a large coordinated packet flood at a host. Since
many of them flooded the victim at the same time, the
traffic was more than the host could handle. Further-
more, because it came from many different sources, it
could be very difficult to stop. Varian [3] discusses
different kind attacks and their effects. The suggestions
made in [3] are: the costs of distributed denial-of-service
attacks should fall on the operators of the networks
from which the flooding traffic originates. And assign
legal liability to the parties that are best able to manage
the risk as they will develop expertise for computer
security and provide the required services to their cli-
ents. In next section we review the intrusion detection
Copyright © 2010 SciRes CN
2. Early Intrusion Detection System
An intrusion occurs wh en an attacker gains unauthorized
access to a valid user’s account and performs disruptive
behavior while masquerading as that user. The attacker
may harm the user’s account directly or can use it to
launch attacks on other accounts or machines. In such
scenario a useful method to detect it is to develop “pat-
terns” of users of a computer system. The early intrusion
detection efforts used to do manual review of a system
audit trail that was inefficient approach as many systems
did not collect enough data to provide an audit trail, or
failed to protect the data against modification. Studies in
[4] show that nearly all large corporations and most me-
dium-sized organizations have installed some form of
intrusion detection tool. In [5], the misuse detection
methods using mobile agents are discussed. The methods
to detecting intrusions can be anomaly detection or mis-
use detection. Misuse detection is mainly suitable for
reliably detecting known patterns, but they are hardly of
any use yet unknown attack methods. The mobile agents
provide computational security by constantly moving
around the Internet and propaga ting rules to solv e misuse
detection. The paper [6] discusses an Intrusion Detection
System (IDS) architecture integrating both anomaly and
misuse detection approaches. This architecture consists
of three main modules: an anomaly detection module, a
misuse detection module, and a decision support system
module. The anomaly detection module uses a Self-Or-
ganizing Map (SOM) structure to model normal behavior
and any deviation from the normal behavior is consid-
ered as an attack. The misuse detection module uses J.48
decision tree algorithm to classify different types of at-
tacks. The decision support system analyzes and inter-
prets the results for interpreting the results of both anom-
aly and misuse detection modules. In [7], strict anomaly
detection method is discussed that uses the neural net-
works to a great effect. Now we review the important
approaches used in the intrusion detection systems.
2.1 Rule Based Intrusion Detection Systems
The basic assumption in the rule-based intrusion detec-
tion systems is that the in trusion attempts can be charac-
terized by sequences of user activities that lead to com-
promised system states and based on that they predict
intrusion. These systems fire rules when audit records or
system status information begins to indicate illegal activ-
ity. Two major approaches are followed in rule-based
intrusion detection: state-based and model-based ap-
proach. In the former, the rule base is codified using the
terminology found in the audit trails and Intrusion at-
tempts are the sequences of system state as defined by
audit trail informatio n leading from an initial and limited
access state to a final compromised state [8]. In the later,
the known intrusion attempts are modeled as sequences
of user behavior. The intrusion detection system itself is
responsible for determining how an identified user be-
havior may manifest itself in an audit trail. These sys-
tems have many benefits, such as large data processing,
more intuitive explanations of intrusion attempts, and
prediction of future actions. The rule-based systems
however have some limitations. They lack flexibility in
the rule-to-audit record representation. Slight variations
in an attack sequence can affect the activity-rule com-
parison up to that extent that the intrusion may not be
detected. While increasing the level of abstraction of the
rule-base does provide a partial solu tion to this weakness,
it also reduces the granularity of the intrusion detection
device. A number of non-expert system-based ap-
proaches to intrusion detection have been discussed in
[9–12]. Most current approaches to detecting intrusions
utilize some form of rule-based analysis. Expert systems
are the most common form of rule-based intrusion detec-
tion approaches [13–16]. An Expert system consists of a
set of rules that encode the knowledge of a human “ex-
pert”. These rules are used by the system to make con-
clusions about the security-related data from the intru-
sion detection system. Unfortunately, the expert systems
require frequent updates to remain current. While the
expert systems offer an enhanced ability to review audit
data, the required updates may be ignored or performed
infrequently by the administrator. At a minimum, this
leads to an expert system with reduced capabilities. At
worst, this will degrade the security of the entire system
by causing the system’s users to be mislead into believ-
ing that the system is secure, even as one of the key
components becomes increasingly ineffective over the
2.2 Network-Based and Host-Based Intrusion
Detection Systems
A network-based intrusion detection system (NIDS) ob-
serves the traffic at specified points in the network and
then checks that traffic packet by packet in real time to
detect intrusion patterns. It can examine the activity at
any layer of the network su ch as network layer, transport
layer, and application layer protocol. The network-based
systems are generally best at detecting the unauthorized
outsider access and bandwidth theft/denial of service.
When an unauthorized user logs in successfully, or at-
tempts to log in, they are tracked with host-based IDS.
However, detecting the unauthorized users before their
logon attempt is best accomplished with network-based
IDS. The packets that initiate bandwidth theft attacks can
best be noticed with use of network-based IDS. Some of
the network-based IDS are Shadow, Dragon, NFR, Re-
alSecure, and NetProwler.
Host-based Intrusion Detection systems are first of
IDSs developed and implemented. They collect and ana-
lyze the data originated on a computer that provides a
Copyright © 2010 SciRes CN
service, such as web server. After collecting the data
from a given computer, it is analyzed. One example of
the host-based system is programs that operate on a sys-
tem and receive application or operating system audit
logs. These programs are highly effective for detecting
insider abuses. Residing on the trusted network systems
themselves, they are close to the network’s authenticated
users. If one of these users attempts an unauthorized ac-
tivity, the host-based systems usually detect and collect
the most pertinent information in the quickest possible
manner. In addition to detecting unauthorized insider
activity, the host-based systems are also effective at de-
tecting unauthorized file modification. The host-based
IDSs are Windows NT/2000 Security Event Logs,
RDMS audit sources, Enterprise Management systems
audit data (such as Tivoli), and UNIX Syslog in their raw
Graph-Based Intrusion Detection System (GrIDS) [17]
uses a graphical representation to monitor the activity of
entire network. EMERALD eXpert-BSM, a real-time
forward-reasoning expert system, uses a knowledgebase
to detect multiple forms of system misuse [18]. In [19], a
technique is discussed for detecting intrusions at the
level of privileged processes. It is reported that short
sequences of system calls executed by running programs
are a good discriminator between normal and abnormal
operating characteristics of several common UNIX pro-
grams. Analyzing the system calls made by a program is
a reasonable approach to detect intrusions based on pro-
gram behavior profiles [20].
2.3 Neural Network Based Intrusion Detection
The neural network based intrusion detection systems
have the ability to b e trained and learn patterns in a given
environment, which can be used to detect intrusions by
recognizing patterns of an intrusion. The Artificial Neu-
ral Network based methods for intrusion detection are
quite popular. Recently an investigation on the unsuper-
vised neural network models and choice for most appro-
priate one among them for evaluation and implementa-
tion is discussed in [21]. These can be used for both
host-based and network based intrusion detection sys-
tems. For the success of IDS is the failure of firewalls to
prevent many security intrusions. The intrusion detection
systems can detect many of them that slip through fire-
walls. Many Anomalies based and Misuse based intru-
sion detection techniques have been designed to detect
the abnormal behavior exhibited by the user in [22–27].
Artificial neural networks have been suggested as alter-
natives to the statistical analysis [28–30]. Statistical
Analysis involves statistical comparison of current even-
ts to a predetermined set of baseline criteria. Neural net-
works are specifically discussed to identify the typical
characteristics of system users and identify statistically
significant variations from the user’s established behav-
ior. Artificial neural networks have also been discussed
for use in the detection of computer viruses. In [31],
neural networks are discussed as statistical analysis ap-
proaches in the detection of viruses and malicious soft-
ware in computer n e tworks. The neural network intrusion
detection (NNID) system [32] uses neural networks to
predict the next comman d a user will enter based on pre-
vious commands. Now we discuss our neural network
based intrusion detection system.
3. Audit Logs Analysis U sing Neural Netw orks
In this work, we collect the data from the ISA 2000 Web
Access Log to analyze for possible intrusion attacks us-
ing the neural networks and then use the back propaga-
tion neural (BPN) network model for analyzing the input
data. Different numbers of hidden layers are considered
in the PBN algorithm.
3.1 ISA 2000 Web Access Log Analysis
Internet bandwidth is consumed by a variety of internet
application protocols. The most popular application layer
protocol that accesses Internet resources is the HTTP
protocol. It is used to access the resources on the World
Wide Web. Although bandwidth cost per-kilobyte or
per-megabyte has come down over the years, yet the
amount of bandwidth consumed by users on the campus
network increases year after year. HTTP connections to
Internet resources not only lead to increase in bandwid th
usage, they also reduce the amount of bandwidth avail-
able on the Internet link for other important protocols
and applications, such as SMTP, POP3 and VPN. In or-
der to provide the desired data resources to users, it is
stored at different locations using some kind of servers.
To further help the user in computer network environ-
ment, proxy servers are employed. A proxy server is a
server (a computer system or an application program)
which provides the services to user requests by making
requests to other servers. A user connects to the proxy
server, requesting a file, connection, web page, or other
resource available from a different server. In an enter-
prise that uses the Internet, a proxy server is a server that
acts as an intermediary between a workstation user and
the Internet so that the enterprise can ensure security,
administrative control, and caching service. It can re-
ceive a request for an Internet service (such as a Web
page request) from a user. On clearing filtering require-
ments, the proxy server, assuming it is also a cache-
server, looks in its local cache of previously downloaded
Web pages. If the desired pages are there, it returns them
to the user without needing to forward the request to the
Internet. In case the required pages are not in the cache,
the proxy server, acting as a client on behalf of the user,
uses one of its own IP addresses to request the pages
Copyright © 2010 SciRes CN
Table 1. Attributes in ISA server 2000 log file
Field name Description
c-ip The Internet Protocol (IP) address of the requesting client.
cs-username The account of the user making the request. If ISA Server access control is not being used, ISA Server uses Anony-
c-agent The name and version of the client application sent by the client in t he Hypertext Transfer Protocol (HTTP)
User-Agent header.
date The date on which the logged event occurred.
time The local time when the logged event occurre d.
r-host The domain name for the remote computer that provides service to the current connection.
r-ip The network IP address of the remote computer that p r ovides service t o the current connection.
r-port The reserved port number on the remote computer that provides se rv ice t o t he c urr en t connection.
time-taken The total time, in milliseconds, that is needed by ISA Server to process the current connection
cs-bytes The number of bytes sent from the remote computer and received by the client during the current connection.
sc-bytes The number of bytes sent from the client to the remote computer during the current connection.
cs-protocol The application protocol used for the connection. Common values are http for Hypertext Transfer Protocol, https for
Secure HTTP, and ftp for File Transfer Pro toc ol.
s-operation The HTTP method used. Common values are GET, PUT, POST, and HEAD.
cs-uri The URL requested.
s-object-source The type of source that was used to retrieve the current object. A table of some possible values is provided in Obje ct
Source Values.
sc-status A Windows (Win32) error code (for values less than 100), an HTTP status code (for values between 100 and 1,000), a
Winsock error code (for values between 10,004 and 11,031), or an ISA Server error code.
from the server out on the Internet. When the pages are
received, the proxy server forwards them onto the user.
3.2 ISA Server 2000 Web Access Log
Internet Security and Acceleration (ISA) Server 2000 can
help in reducing overall bandwidth usage and cost by
caching Web contents on the ISA Server 2000. We use
Microsoft ISA Server 2000 log to monitor and analyze
the status of the Web proxy requests to find out the
documents that are worthless in an organization. Table 1
shows the attributes used in ISA Server 2000 Log file.
3.3 Experiment
The input data is collected in terms of above mentioned
attributes. Table 2 contains the values of the input data.
The data shown in Table 2 is not a valid input pattern
for BPN. Before providing the data for training to the
BPN, it needs be converted in the valid pattern. We per-
form the following steps for making a valid input for
Select the ip address part of the destination web
server and convert it in the integer number without de-
limiter. For example, the ip is converted
into 2162396383. Th is is a long nu mber which in itself is
not a valid input p a ttern for BPN.
Normalize the input pattern in real numbers. After
normalization the input data pattern is shown in Table 3.
First column shows the normalized ip addresses and the
second column shows 1 as valid ip address and 0 as in-
valid ip addresses.
Train the BPN for this input pattern by taking dif fer-
entnumber of hidden layers. We use 2, 5 and 10 hidden
layers. The number of epochs is taken as 50,000 . Results
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Table 2. ISA server 2000 web access log
c-ip cs-user-
name c-agent date time r-host r-ip r-portTime
-ta cs-
bytes sc-
bytes cs-
protocol s-op-
eration cs-uri s-objece
-source sc-
stauts anonymous Mozilla/4 12/14/2006 7:01.41 Images3.0 72.14.209 80 797 796 3053 http GET http://image VCache 30 Anonymous Mozilla/5 12/14/2006 7:01.41 www.orku 72.14.209 80 797 981 253 http GET http://www Inet 30 Anonymous Mozilla/5 12/14/2006 7:01.41 Images3.0 72.14.209 80 813 995 253 http GET http://image VCache 30 Anonymous Mozilla/4 12/14/2006 7:01.41 210.161.3280 640 1243 237 http GET http://image Inet 30 Anonymous Mozilla/4 12/14/2006 7:01.41 In.f89.mail 203.84.22280 5844 2332 79644http POST http://in.f89 Inet 20 Anonymous Mozilla/5 12/14/2006 7:01.41 www.orku 72.14.209 80 3109 1135 7267 http GET http://www Inet 20 Anonymous Mozilla/4 12/14/2006 7:01.41 Jdelivery 210.161.3280 593 1014 277 http GET http:// jesliv Inet 30 Anonymous Mozilla/4 12/14/2006 7:01.41 In.wrs.yal 216.252.1280 1359 816 601 http GET http://in,wrn Inet 30 Anonymous Mozilla/4 12/14/2006 7:01.41 Mum.inte 220.226.2080 4531 358 2312 http GET http:// mum Inet 00 Anonymous Mozilla/5 12/14/2006 7:01.41 Imagas3.0 72.14.209 80 797 995 253 http GET http://image VCache 30 Anonymous Mozilla/5 12/14/2006 7:01.41 Imagas3.0 72.14.209 80 781 1000 253 http GET http://image VCache 30 Anonymous Mozilla/4 12/14/2006 7:01.41 Imagas3.0 72.14.209 80 766 796 2257 http GET http://image VCache 30 Anonymous Mozilla/4 12/14/2006 7:01.42 Imagas3.0 72.14.209 80 781 796 2215 http GET http://image VCache 30 Anonymous Mozilla/4 12/14/2006 7:01.42 72.14.235 80 859 969 1532 http GET http://www Inet 20 Anonymous Mozilla/4 12/14/2006 7:01.42 Images3.0 72.14.209 80 1563 747 1882 http GET http://image Inet 20 Anonymous Mozilla/4 12/14/2006 7:01.42 www.orku 72.14.209 80 5312 968 18229http GET http://www Inet 20 Anonymous Mozilla/4 12/14/2006 7:01.42 Images3.0 72.14.209 80 797 994 2281 http GET http://image VCache 30 Anonymous Mozilla/4 12/14/2006 7:01.42 www.orku 72.14.209 80 3953 1013 18507http GET http://www Inet 20 Anonymous Mozilla/5 12/14/2006 7:01.42 Images3.0 72.14.209 80 796 1014 201 http GET http://image VCache 30 Anonymous Mozilla/5 12/14/2006 7:01.42 Images3.0 72.14.209 80 812 1008 201 http GET http://image VCache 30 Anonymous Mozilla/4 12/14/2006 7:01.42 jdelivery 210.161.3280 594 1030 276 http GET http://jdeliv VCache 30 Anonymous Mozilla/4 12/14/2006 7:01.42 69.25.142 80 133125 1683 1119 http GOST http://www Inet 6 Anonymous Mozilla/4 12/14/2006 7:01.42 209.85.13980 2563 1683 361 http GET http://mail Inet 20 Anonymous Mozilla/4 12/14/2006 7:01.42 www.go 72.14.235 80 703 340 234 http GET http://www VCache 30 Anonymous Mozilla/5 12/14/2006 7:01.42 Images3.0 72.14.209 80 781 1013 201 http GET http://image VCache 30 Anonymous Mozilla/5 12/14/2006 7:01.42 Images3.0 72.14.209 80 797 1014 201 http GET http://image VCache 30 Anonymous Mozilla/4 12/14/2006 7:01.42 Images3.0 72.14.209 80 766 796 2330 http GET http://image VCache 30
Table 3. Normalized training patterns for BPN
Normalized IP a ddresses Valid(0) / Invalid(1) Normalized IP addresses Valid(0) / Invalid(1)
0.549298 0 0.815306 0
0.57671 0 0.819334 0
0.588196 0 0.819424 0
0.753141 0 0.819514 0
0.760483 0 0.819537 0
0.780321 0 0.026241 1
0.780564 0 0.007997 1
0.791906 0 0.027761 1
0.795886 0 0.28298 1
0.803925 0 0.002624 1
0.803937 0 0.0819573 1
0.808023 0 0.000331 1
0.811643 0 0.081742 1
0.81187 0 0.076052 1
0.811933 0
Copyright © 2010 SciRes CN
for different nu mber of hidden layers are shown in Table
After training the BPN, it is tested with test patterns
as shown in Table 4.
4. Results
The training of the neural networks has been conducted
using the Back Propagation neural network algorithm for
50,000 iterations of the selected training data. After
training the BPN, the following results are obtained.
The results obtained match very closely with the de-
sired root mean square (RMS) error as shown in Table 5.
Though this method is not designed to be used as a com-
plete intrusion detection system, yet the results show the
potential of neural networks to detect individual in-
stances of possible misuse from a representative web-
based data. Graphs in Figure 1 show the results for dif-
ferent number of hidden layers used in the BPN. It is
evident from the graphs that the results are very close to
desired output values, when we use 10 numbers of neu-
rons for hidde n laye r.
5. Discussions
The above mentioned method can be used to find out the
web documents that should not be allowed in the organi-
zation. Web Server log file is divided into two parts. One
file contains only the destination ip addresses and the
second file contains the corresponding source ip and date
Table 4. Normalize testing patterns for BPN
IP Patterns
for Testing Valid(0) /
Invalid(1) IP Patterns
for Testing Valid(0) /
0.000771 0 0.00082 0
0.000776 0 0.000823 0
0.000788 0 0.000826 0
0.000793 0 0.000831 0
0.000794 0 0.819573 1
0.000795 0 0.000331 1
0.000796 0 0.081742 1
0.000798 0 0.002624 1
0.000799 0 0.076052 1
0.0008 0 0.259212 1
0.000802 0 0.008221 1
0.000813 0 0.027213 1
0.000818 0 0.000819 1
Table 5. RMS error corresponding to hidden layers
No of Hidden Layers RMS Error (Training Data)
2 0.026315
5 0.024311
10 0.023302
Figure 1. Predicted output for test patterns: in (a) 2, in (b) 5,
and in (c) 10 hidden layers are used
and time of the site being accessed. Input of the first file
having ip addresses of the sites being accessed is con-
verted into normalized ip address. This is the input pat-
tern to Neural Network for testing. For the ip addresses
having errors (invalid websites) and no errors (valid
websites) the Neural Network is already trained. When a
user tries to access a website that is in the invalid website
record, it is detected by the system. At the time there is a
Copyright © 2010 SciRes CN
Table 6. Web site address to be included in the invalid web
site record
Address of the Web Site ip address
deviation in the log files under testing it will be figured
out. Here in our case Normalized ip pattern 0.002624 is
reported as invalid and its corresponding website is
www. The corresponding source ip address,
time, and date can be fo u n d from the second file.
We have manually analyzed Web Server log for dura-
tion of 15 minutes after the first detection is reported in
the system. This is because there is a probability that the
user on the system may try to access some similar sites
that should be in the invalid web site record, but are not
included in the invalid website record previously. This
analysis gives us positive results and two sites have been
included in the invalid website record as mentioned in
Table 6.
There are lots of web documents which provide
anonymous downloads of the files of larger size like
movie and songs files. If a user is allowed to access these
sites, then a large portion of the network bandwidth will
be wasted. Many of the sites are already blocked by the
Network Administrator, but some sites are still in use.
When a user is stopped to access a web document he/she
will try to access another web document with similar
facility that is missed to block by the Network Adminis-
trator. The analysis discussed above can be used to block
these types of Web docum e nt s.
6. Conclusions
Research and development of intrusion detection system
has been ongoing last couple of decades and the chal-
lenges faced by designers have increased many fold.
Misuse detection is particularly difficult problem because
of the extensive number of vulnerabilities in computer
systems and the creativity of the attackers. Neural net-
works provide a number of advantages in the direction of
these attacks. The results of our tests for the Proxy
Server (Microsoft ISA Server 2000) log show that this
technique can be applied for detecting worthless web
document access to save the network bandwidth.
[1] R. J. Anderson, “Why cryptosystems fail,” In Communi-
cations of the ACM, Vol. 37, No. 11, pp. 32–40, Novem-
ber 1994.
[2] workshop-final.html.
[3] H. Varian, “Managing online security risks,” Economic
Science Column, The New York Times, June 2000.
[4] SANS Institute staff, “Intrusion detection and vulnerabil-
ity testing tools: what works?” 101 Security Solutions
E-Alert Newsletters, 2001.
[5] T. K. Kim, D. Y. Lee, and T. M. Chung, “Mobile agent-
based misuse intrusion detection rule propagation model
for distributed system,” Lecture Note in Computer Sci-
ence, Vol. 2510, pp. 842–849, 2002.
[6] O. Depren, M. Topallar, E. Anarim, and M. K. Ciliz, “An
intelligent intrusion detection system (IDS) for anomaly
and misuse detection in computer networks,” Expert Sys-
tems with Applications, Vol. 29, No. 4, pp. 713–722, No-
vember 2005.
[7] T. Konno and M. Tateoka, “Accuracy improvement of
anomaly-based intrusion detection system using taguchi
method,” Proceeding of Symposium on Applications and
the Internet Workshops (SAINT-W’05), 0-7695-2263-
7/05, 2005.
[8] K. Ilgun, “USTAT: A real-time intrusion detection system
for UNIX,” Proceeding of the 1993 Computer Society
Symposium on Research in Security and Privacy, Oak-
land, California, Los Alamitos, pp. 16–28, May 1993.
[9] K. Fox, R. Henning, J. Reed, and R. Simonian, “A neural
network approach towards intrusion detection,” Proceed-
ing of 13th National Computer Security Conference, Bal-
timore, MD, pp. 125–134, 1990.
[10] J. Frank, “Artificial intelligence and intrusion detection:
current and future directions,” Computers and Security,
Vol. 14, No. 1, pp. 31–31(1), 1995.
[11] L. Fu, “A neural network model for learning rule-based
systems,” Proceeding of the International Joint Confer-
ence on Neural Networks, pp. 343–348, 1992.
[12] D. Hammerstrom, “Neural networks at work,” IEEE
Spectrum, pp. 26–53, June 1993.
[13] J. Zimmermann, L. Mé, and C. Bidan, “An improved
reference flow control model for policy-based intrusion
detection,” Proceeding of the 8th European Symposium
on Research in Computer Security (ESORICS), pp. 291–
308, October 2003.
[14] G. J. Nalepa, “Application of the XTT rule-based model
for formal design and verification of internet security
systems,” Lecture Notes in Computer Science, Vol. 4680,
pp. 81–86, 2007.
[15] D. Dorothy, “An intrusion-detection model,” IEEE Trans-
actions on Software Engineering, Vol. 13, No. 2, pp. 222–
232, February 1987.
[16] M. M. Sebring, E. Shellhouse, M. E. Hanna, and R. A.
Whitehurst, “Expert systems in intrusion detection: a case
study,” Proceeding of the 11th National Computer Security
Conference, Baltimore, MD, pp. 74–81, October 1988.
[17] S. Staniford-Chen, S. Cheung, R. Crawford, M. Dilger, J.
Frank, J. Hoagland, K. Levitt, C. Wee, R. Yip, and D.
Zerkle. “GrIDS, a graph based intrusion detection system
for large networks,” Proceeding of the 20th National In-
formation Systems Security Conference, Vol. 1, pp. 361–
370, October 1996.
[18] P. A. Porras and P. G. Neumann, “Emerald: event moni-
Copyright © 2010 SciRes CN
toring enabling responses to anomalous live distur-
bances,” Proceeding of the 20th National Information
systems Security Conference, pp. 35–365, October 1997.
[19] S. Freeman, “Host based intrusion detection using user
signatures,” Computer Science Master’s project, May
[20] A. K. Ghosh, A. Schwartzbard, and M. Schatz, “Learning
program behavior profiles for intrusion detection,” Pro-
ceeding of the 1st Workshop on Intrusion Detection and
Network Monitoring, pp. 51–62, April 1999.
[21] A. ¨Oks¨uz, “Unsupervised intrusion detection system,”
Master Thesis, Technical University of Denmark, 2007.
[22] A. Boukerche, K. R. Lemos Juc, J. B. Sobral, and M.
Sechi Moretti Annoni Notare, “An artificial immune
based intrusion detection model for computer and tele-
communication systems,” Parallel Computing, Vol. 30,
No. 5–6, pp. 629–646, 2004.
[23] R. Beghdad, “Modelling and solving the intrusion detec-
tion problem in computer networks,” Computers and Se-
curity, Vol. 23, No. 8, pp. 687–696, 2004.
[24] T. F. Lunt and R. Jagannathan, “A prototype real-time
intrusion-detection system,” Proceeding of the Sympo-
sium on Security and Privacy, New York, pp. 59–66,
April 1988.
[25] T. D. Garvey and T. F. Lunt, “Model based intrusion de-
tection,” Proceeding of the 14th National Computer Se-
curity Conference, pp. 372–385, October 1991.
[26] K. Ilgun, “Ustat: A real-time intrusion detection system
for UNIX,” Master’s thesis, Computer Science Dept,
UCSB, July 1992.
[27] S. Kumar and E. H. Spafford, “A pattern matching model
for misuse intrusion detection,” The COAST Project,
Purdue University, 1996.
[28] J. Ryan, M. Lin, and R. Miikkulainen, “Intrusion Detec-
tion with Neural Networks,” AI Approaches to Fraud De-
tection and Risk Management: Papers from the 1997
AAAI Workshop (Providence, Rhode Island), pp. 72–79,
[29] H. Debar and B. Dorizzi, “An application of a recurrent
network to an intrusion detection system,” Proceeding of
the International Joint Conference on Neural Networks,
pp. 478–483, 1992.
[30] A. Abraham, C. Grosan, and C. Martin-Vide, “Evolution-
ary design of intrusion detection programs,” International
Journal of Network Security, Vol. 4, No. 3, pp. 328–339,
March 2007.
[31] M. Denault, D. Gritzalis, D. Karagiannis, and P. Spirakis,
“Intrusion detection: approach and performance issues of
the securenet system,” Computers and Security, Vol. 13,
No. 6, pp. 495–500, 1994.
[32] S. E. Smaha, “Haystack: an intrusion detection system,”
Proceeding of the Fourth AeroSpace Computer Security
Applications Conference, Orlando, FL, pp. 37–44, De-
cember 1988.