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Proceedings of National Conference on Challenges & Opportunities in Information Technology (COIT-2007)
RIMT-IET, Mandi Gobindgarh. March 23, 2007.
Unsupervised Anomaly Detection In Network Intrusion
Detection Using Clusters
Satinder Singh1, Guljeet Kaur2
Computer Science Department, Lyallpur Khalsa College, Jalandhar, Near Jain Hospital Akash Avenue, Jandiala Guru, Amritsar,
Computer Science Department, Khalsa College,Amritsar, Near Jain Hospital Akash Avenue, Jandiala Guru, Amritsar,
Most current network intrusion detection systems employ
signature-based methods or data mining-based methods which
rely on labeled training data. This training data is 90typically
expensive to produce. Moreover, these methods have difficulty
in detecting new types of attack. In this paper, we have
discussed anomaly based instruction detection, pros and cons
of anomaly detection, supervised and unsupervised anomaly
Intrusion Detection, is the art of detecting inappropriate,
incorrect, or anomalous activity. ID systems that operate on a
host to detect malicious activity on that host are called hostbased ID systems, and ID systems that operate on network data
flows are called network-based ID systems.
Most current network intrusion detection systems employ
signature-based methods or data mining-based methods which
rely on labeled training data. This training data is typically
expensive to produce. Moreover, these methods have difficulty
in detecting new types of attack. Using unsupervised anomaly
detection techniques, however, the system can be trained with
unlabelled data and is capable of detecting previously “unseen"
attacks. With the increased usage of computer networks,
security becomes a critical issue.
A network intrusion by malicious or unauthorized users can
cause severe disruption to networks. Therefore the
development of a robust and reliable network intrusion
detection system (IDS) is increasingly important.
Traditionally, signature based automatic detection methods
have been widely used in intrusion detection systems. When an
attack is discovered, the associated traffic pattern is recorded
and coded as a signature by human experts, and then used to
detect malicious traffic. However, signature based methods
suffer from their inability to detect new types of attack.
Further-more the database of the signatures is growing as new
types of attack are being detected, which may affect the
efficiency of the detection.
Other methods have been proposed using machine learning
algorithms to train on labeled network data, i.e., with instances
pre-classified as being an attack or not (Lee & Stolfo 1998).
These methods can be classified into two categories: misuse
detection and anomaly detection.
Misuse detection
In the misuse detection approach, the machine learning
algorithm is trained over the set of labeled data and
automatically builds detection models. Thus, the detection
models are similar to the signatures described before.
Nonetheless these detection methods have the same weakness
as the signature based methods in that they are vulnerable
against new types of attack.
Anomaly detection
In contrast, anomaly detection approaches build models of
normal data and then attempt to detect deviations from the
normal model in observed data. Consequently these algorithms
can detect new types of intrusions because these new
intrusions, by assumption, will deviate from normal network
usage (Javitz & Vadles 1993, Denning 1987). Nevertheless
these algorithms require a set of purely normal data from which
they train their model. If the training data contains traces of
intrusions, the algorithm may not detect future instances of
these attack because it will assume that they are normal.
An IDS which is anomaly based will monitor network traffic
and compare it against an established baseline. The baseline
will identify what is “normal” for that network- what sort of
bandwidth is generally used, what protocols are used, what
ports and devices generally connect to each other- and alert the
administrator or user when traffic is detected which is
anomalous, or significantly different, than the baseline.
In most circumstances, labeled data or purely normal data is
not readily available since it is time consuming and expensive
to manually classify it. Purely normal data is also very hard to
obtain in practice, since it is very hard to guarantee that there
are no intrusions when we are collecting network traffic. To
address these problems, we used a new type of intrusion
detection algorithm called unsupervised anomaly detection. It
makes two assumptions about the data.
Assumption 1 The majority of the network connections are
normal traffic. Only X% of traffic are malicious. (Portnoy,
Eskin & Stolfo 2001)
Proceedings of National Conference on Challenges & Opportunities in Information Technology (COIT-2007)
RIMT-IET, Mandi Gobindgarh. March 23, 2007.
Assumption 2 The attack traffic is statistically different from
normal traffic. (Javitz & Vadles 1993, Denning 1987)
The algorithm takes as input a set of unlabelled data and
attempts to find intrusions buried within the data. After these
intrusions are detected, we can rain a misuse detection
algorithm or a traditional anomaly detection algorithm using
the data.
If any of the assumptions fail, the performance of the algorithm
will deteriorate. For example, it will have difficulties in
detecting a bandwidth DoS attack. The reason is that often
under such attacks there are so many instances of the intrusion
that it occurs in a similar number to normal instances.
Pros: An anomaly-based IDS examines ongoing traffic,
activity, transactions, or behavior for anomalies on networks or
systems that may indicate attack. The underlying principle is
the notion that “attack behavior” differs enough from “normal
user behavior” that it can be detected by cataloging and
identifying the differences involved. By creating baselines of
normal behavior, anomaly-based IDS systems can observe
when current behavior deviates statistically from the norm.
This capability theoretically gives anomaly-based IDSs
abilities to detect new attacks that are neither known nor for
which signatures have been created.
Cons: Because normal behavior can change easily and readily,
anomaly-based IDS systems are prone to false positives where
attacks may be reported based on changes to the norm that are
“normal,” rather than representing real attacks. Their intensely
analytical behavior can also impose sometimes-heavy
processing overheads on systems where they’re running.
Furthermore, anomaly-based systems take a while to create
statistically significant baselines (to separate normal behavior
from anomalies); they’re relatively open to attack during this
period.Today, many antivirus packages include both signaturebased and anomaly-based detection characteristics, but only a
few IDSs incorporate both approaches. Most experts expect
anomaly-based detection to become more widespread in IDSs,
but research and programming breakthroughs will be necessary
to deliver the kind of capability that anomaly-based detection
should be, but is currently not, able to deliver.
Finally, some IDSs are capable of responding to attacks when
they occur. This behavior is desirable from two points of view.
For one thing, a computer system can track behavior and
activity in near-real time and respond much more quickly and
decisively during early stages of an attack. Since automation
helps hackers mount attacks, it stands to reason that it should
also help security professionals fend them off as they occur.
For another thing, IDSs run 24/7, but network administrators
may not be able to respond as quickly during off hours as they
can during peak hours (even if the IDS can page them with an
alarm that an attack has begun). By automating a response to
block incoming traffic from one or more addresses from which
an attack originates, the IDS can halt an attack in process and
block future attacks from the same address.
Unlike signature or misuse based intrusion detection
techniques, anomaly detection is capable of detecting novel
attacks. However, the use of anomaly detection in practice is
hampered by a high rate of false alarms. Specification-based
techniques have been shown to produce a low rate of false
alarms, but are not as effective as anomaly detection in
detecting novel attacks, especially when it comes to network
probing and denial-of-service attacks. This paper presents a
new approach that combines specification-based and anomalybased intrusion detection, mitigating the weaknesses of the two
approaches while magnifying their strengths. Our approach
begins with state-machine specifications of network protocols,
and augments these state machines with information about
statistics that need to be maintained to detect anomalies. We
present a specification language in which all of this information
can be captured in a succinct manner. We demonstrate the
effectiveness of the approach on the 1999 Lincoln Labs
intrusion detection evaluation data, where we are able to detect
all of the probing and denial-of-service attacks with a low rate
of false alarms (less than 10 per day). Whereas feature
selection was a crucial step that required a great deal of
expertise and insight in the case of previous anomaly detection
approaches, we show that the use of protocol specifications in
our approach simplifies this problem. Moreover, the machine
learning component of our approach is robust enough to
operate without human supervision and fast enough that no
sampling techniques need to be employed. As further evidence
of effectiveness, we present results of applying our approach to
detect stealthy email viruses in an intranet environment.
Applying unsupervised anomaly detection in network intrusion
detection is a new research area that have already drawn
interest in the academic community. Eskin, et al. (Eskin et al.
2002) investigated the effectiveness of three algorithms in
intrusion detection: the fixed-width clustering algorithm, an
optimized version of the k-nearest neighbor algorithm, and the
one class support vector machine algorithm. Old-meadow, et
al. (Oldmeadow et al. 2004) carried out further research based
on the clustering method in (Eskin et al. 2002) and showed
improvements in accuracy when the clusters are adaptive to
changing traffic patterns. A different approach using a quartersphere support vector machine is proposed in (Laskov, Schafer
& Kotenko 2004), with moderate success. In (Eskin 2000), a
mixture model for explaining the presence of anomalies is
presented, and machine learning techniques are used to
estimate the probability distributions of the mixture to detect
anomalies. In (Zanero & Savaresi 2004), a novel two-tier IDS
is proposed. The first tier uses unsupervised clustering to
classify the packets and compresses the information within the
payload, and the second tier used an anomaly detection
algorithm and the information from the first tier for intrusion
Proceedings of National Conference on Challenges & Opportunities in Information Technology (COIT-2007)
RIMT-IET, Mandi Gobindgarh. March 23, 2007.
detection. Lane and Brodley (Lane & Brodley 1997) evaluated
unlabelled data by looking at user profiles and comparing the
activity during an intrusion to the activity during normal use.
Supervised anomaly detection in network intrusion detection,
which uses purely normal instances as training data, has been
studied extensively in the academic community. A
comprehensive survey of various techniques is given in
(Lazarevic, Ertoz, Kumar, Ozgur & Srivastava 2003). An
approach for modelling normal traffic using self-organising
maps is presented in (Gonzalez & Dasgupta 2002), while
another one uses principal component classifiers to obtain the
model (Shyu, Chen, Sarinnapakorn & Chang 2003). One
approach uses graphs for modelling the normal data and detect
the irregularities in the graph for anomalies (Noble & Cook
2003). Another approach uses the normal data to generate
abnormal data and uses it as input for a classification algorithm
(Gonzalez & Dasgupta 2003).
Clustering is a well known and studied problem. There exist a
large number of clustering algorithms in the literature. These
methods can be categorized as: partitioning methods,
hierarchical methods, density-based methods and grid-based
methods. We shall concentrate on algorithms that closely
related to our investigation.
Partitioning Methods
Xu 1996) and OPTICS (Ankerst, Breunig, Kriegel & Sander
Grid-based Methods
Grid-based methods divide the object space into a finite
number of cells that form a grid structure. All of the clustering
operations are performed on the grid structure. The main
advantage of this approach is its fast processing time, which is
typically dependent mainly on the number of cells in each
dimension in the quantised space. Some examples of gridbased methods are STING (Wang, Yang & Muntz 1997),
WaveCluster (Sheikholeslami, Chatterjee & Zhang 1998),
CLIQUE (Agrawal, Gehrke, Gunopulos & Raghavan 1998)
and pMAFIA (Nagesh, Goil & Choudhary 2000).
CLIQUE (CLustering In QUEst)
Our work builds upon the CLIQUE and pMAFIA algorithms.
CLIQUE (CLustering In QUEst) (Agrawal et al. 1998) is a
hybrid clustering method that combines the idea of both gridbased and density-based approaches. CLIQUE first partitions
the n-dimensional data space into non-overlapping rectangular
units. It attempts to discover the overall distribution patterns of
the data set by identifying the sparse and dense units in the
space. The identification of the candidate search space is based
on the following monotonicity principle: if a k-dimensional
unit is dense, then so are its projections in (k - 1) dimensional
Given a database of n objects, a partitioning method constructs
k partitions of data where each partition represents a cluster.
One partitioning method that is of interest to our study is the
fixed width clustering algorithm. It is one of the algorithms
used in the studies by Stolfo, et al. (Eskin et al. 2002) and
Oldmeadow, et al. (Oldmeadow et al. 2004) which we compare
our results against.
The main advantage of the fixed width algorithm is that it
scales linearly with the number of objects in the data set and
the number of attributes of the objects. Nevertheless the quality
of the clusters is sensitive to the definition of the width of
cluster w. Often the user needs several repetitions of the
algorithm to choose the best value of w in any particular
Density-based Methods
Mining Frequent Itemsets is an intermediate step in anomaly
detection algorithm. Two well known methods, Apriori
(Agrawal & Srikant 1994) and FP-growth (Han, Pei & Yin
2000), in the following section.
Density-based methods are based on a simple assumption:
clusters are dense regions in the data space that are separated
by regions of lower density. Their general idea is to continue
growing the given cluster as long as the density in the
neighbourhood exceeds some threshold. In other words, for
each data point within a given cluster, the neighborhood of a
given radius has to contain at least a minimum number of
points. These methods are good at filtering out outliers and
discovering clusters of arbitrary shapes. Some examples of
density-based methods are DBSCAN (Ester, Kriegel, Sander &
pMAFIA (Nagesh et al. 2000) is an optimized and improved
version of CLIQUE. There are two main differences between
them. First, pMAFIA used the adaptive grid algorithm to
reduce the total number of potential dense units by merging
small 1-dimensional partitions that have similar densities.
Second, it parallelized the operation of the generation and
population of the candidate dense units using a computer
cluster. However, they both scale exponentially to the
dimension of the cluster of the highest dimension in the data
6. Mining Frequent Itemsets
Apriori algorithm
The Apriori algorithm is a well known method for mining
frequent itemsets. It employs an iterative approach known as a
level-wise search. The finding of each level of itemsets
requires one full scan of the database. To reduce the search
space, candidate k-itemsets are generated only if all of their (k -
Proceedings of National Conference on Challenges & Opportunities in Information Technology (COIT-2007)
RIMT-IET, Mandi Gobindgarh. March 23, 2007.
1)-subsets are frequent. Although this approach guarantees to
find all of the frequent itemsets, the computational complexity
is exponential to the number of maximally frequent itemsets 1 .
It may also need to repeatedly scan the database and check the
large set of candidate itemsets by pattern matching. This is
infeasible for large databases containing millions of records
with long patterns.
Frequent-pattern growth (FP-growth)
Frequent-pattern growth (FP-growth) is an efficient method for
mining frequent itemsets. It avoids the cost of generating a
huge set of candidate itemsets by building a compact prefixtree data structure, the frequent-pattern tree (FP-Tree). First,
the algorithm scans the database and derives the set of frequent
items and their support counts. The set is sorted in the order of
descending support count. Let L denote the resulting set. To
construct the FP-Tree, the algorithm scans the database and
processes the items in each record in L order (i.e., sorted
according to descending support count). The processed itemset
represents a branch in the tree, with each frequent item
represented by a node. Add the branch to the tree if it does not
exist. If any prefix of the branch already exists in the tree, then
increment the count of each node along the common prefix by
one and extend the branch. The algorithm mines the frequent
itemsets from the FP-Tree from nodes deep inside the tree. As
shown in (Hipp, Guntzer & Nakhaeizadeh 2000), the FPgrowth algorithm is about an order of magnitude faster than
Apriori in large databases.
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