Web Spam Classification Using Supervised Artificial Neural Network Algorithms

Advanced Computational Intelligence: An International Journal (ACII), Vol.2, No.1, January 2015
21
WEB SPAM CLASSIFICATION USING SUPERVISED
ARTIFICIAL NEURAL NETWORK ALGORITHMS
Ashish Chandra, Mohammad Suaib, and Dr. Rizwan Beg
Department of Computer Science & Engineering, Integral University, Lucknow, India
ABSTRACT
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are
more efficient, generic and highly adaptive. Neural Network based technologies have high ability of
adaption as well as generalization. As per our knowledge, very little work has been done in this field using
neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised
learning algorithms of artificial neural network by creating classifiers for the complex problem of latest
web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Back-
propagation learning, and Levenberg-Marquardt algorithm.
KEYWORDS
Web spam, artificial neural network, back-propagation algorithms, Conjugate Gradient, Resilient Back-
propagation, Levenberg-Marquardt, Web spam classification
1. INTRODUCTION
In this paper we are studying three artificial neural network (ANN) algorithms, which are
Conjugate Gradient learning algorithm, Resilient Back-propagation algorithm, and Levenberg-
Marquardt algorithm. All of these algorithms are supervised learning algorithms.
We are evaluating performance of these algorithms on the basis of classification results as well as
computational requirements in training. The application on which we are evaluating these
algorithms is web spam classification.
Web spam is one of the key challenges for search engine industry. Web spam are the web pages
that are created or manipulated to lead users from search engine result page to a target web page
and also manipulate search engine ranking of the target page.
2. RELATED WORK
Svore (2007) [1] devised a method for web spam detection based on content-based features and
the rank-time. They used SVM classifier with linear kernel.
Noi (2010) [2] proposed a combination of graph neural network and probability mapping graph
self organizing maps organized into a layered architecture. It was a mixture of unsupervised and
supervised learning approaches but the training time was computationally very expensive.

22
Erdelyi (2011) [3] achieved superior classification results in experiment using learning methods
LogitBoost and RandomForest with less computation hungry content features. They used 100,000
hosts from WebspamUK2007 and 190,000 hosts from DC2010 datasets and investigated the
trade-off between feature generation and spam classification accuracy. They proved that more
features improve performance but complex features such as PageRank improves the classification
accuracy marginally.
According to Biggio (2011) [4], SVM can be manipulated in adversarial classification tasks such
as spam filtering.
Similarly, Xiao (2012) [5] showed that injection of contaminated data in training dataset
significantly degrades the accuracy of the SVM.
This is well known that neural network perform better with noisy data. Similar is the case of
adversarial dataset of web spam.
3. ARTIFICIAL NEURAL NETWORK
An ANN is a collection of simple processing units which communicate with each other using a
large number of weighted connections. Each unit receives input from neighbour units or from
external source and computes output which propagates to other neighbours. There is also a
mechanism to adjust weights of the connections. Normally there are 3 types of units.
• Input Unit: which receives signal from external source.
• Output Unit: which sends data out of the network.
• Hidden Unit: which Receives and sends signals within the network.
Many of the units can work parallel in the system. ANN can be adapted to take a set of inputs and
produce a desired set of outputs. This process is known as learning or training. There are two
kinds of training in neural network.
• Supervised: The network is provided a set of inputs and corresponding output patterns,
called training dataset, to train the network.
• Unsupervised: The network trains itself by creating clusters of patterns. Here no prior set
training data is provided to the system.
3.1. Multi-Layer Perceptron (MLP)
MLP is a nonlinear feed forward network model which maps a set of inputs x into a set of outputs
y. It has 3 types of layers: input layer, output layer, and hidden layer.
Standard perceptron calculates a discontinuous function:

→ ( + ( ,
(1)
smoothing is done using a logistic function to get

→ ( + ( ,
(2)
where: ( =

Advanced Computational Intelligence: An International Journal (ACII), Vol.
MLP is a finite acyclic graph where the nodes are neurons with logistic activation. Neurons of i
layer serves as input for neurons of (i+1)
network containing many neurons. All the
next layer.
Figure 1. A Multilayer Perceptron with one input, two hidden, and one output layer.
All connections are weighted with real number. Weight of connection i
output layer neurons have a bias weight w
Figure 1. A Neuron outputs an activation function applied over weighted sum of its all inputs
Each node of the network outputs an activation function applied over weighted sum of
inputs.

The network output is given by the ai of the output neurons.
3.2. Neural Network Supervised Learning Algorithms
While implementing any learning algorithm, our objective is to reduce the Global
by:
MLP is a finite acyclic graph where the nodes are neurons with logistic activation. Neurons of i
layer serves as input for neurons of (i+1)th
layer. Very complex functions can be calculated with
network containing many neurons. All the neurons of one layer are connected to all neurons of
. A Multilayer Perceptron with one input, two hidden, and one output layer.
All connections are weighted with real number. Weight of connection i → j is wji. All hidden and
output layer neurons have a bias weight wi0.
. A Neuron outputs an activation function applied over weighted sum of its all inputs
Each node of the network outputs an activation function applied over weighted sum of
( w + w ,
!
X a
The network output is given by the ai of the output neurons.
Neural Network Supervised Learning Algorithms
While implementing any learning algorithm, our objective is to reduce the Global Error E defined
%

E(
)

2, No.1, January 2015
23
MLP is a finite acyclic graph where the nodes are neurons with logistic activation. Neurons of ith
layer. Very complex functions can be calculated with
neurons of one layer are connected to all neurons of
. A Multilayer Perceptron with one input, two hidden, and one output layer.
. All hidden and
. A Neuron outputs an activation function applied over weighted sum of its all inputs
Each node of the network outputs an activation function applied over weighted sum of its all
(3)
Error E defined
(4)

24
Where P is the total size of training dataset, and Ep is the error for training pattern p. And
also,
%

*
+ (O − t*
/

(5)
where N is total number of output nodes, Oi is output of the ith
output node and ti is the target
output at the ith
output node. Every learning algorithm tries to reduce the global error by adjusting
weights and biases. Now first we will discuss the three algorithms one by one.
3.3. Conjugate Gradient (CG) Algorithm
It is basic back-propagation algorithm. It adjusts weights in the steepest descent direction i.e. the
most negative of the gradients. This is the direction in which the function is decreasing most
rapidly. It is observed that although the function decreases most rapidly along the negative of the
gradient direction, it does not always provide the fastest convergence. In the Conjugate Gradient
algorithm a search is done in conjugate directions, which generally provides the faster
convergence than the steepest descent direction[6].
Conjugate Gradient Algorithm adjusts step size in each iteration along the conjugate gradient
direction to minimize performance function. It searches the steepest descent direction on the first
iteration.
p0 = - g0 (6)
Then it performs line search to determine the optimal distance to move along the current search
direction by combining new steepest descent direction with the previous direction.
pk = - gk + βk pk - 1 (7)
where the constant βk is
β1
23
4 23
235
4 235
(8)
It is the ratio of the norm squared of current gradient to the norm squared of the previous
gradient[7].
3.4. Resilient Back-propagation (RB) Algorithm
Multilayer Perceptron networks typically use sigmoid transfer function in the hidden layer. It is
also known as squashing function because it compresses an infinite input range into finite output
range. The sigmoid function's slope approaches to zero as input gets large. It causes problem
when we use steepest descent to train network. Because since gradient may have very small value
so it may cause small changes in weights and biases even though the weight and biases are fairly
far away from the optimal values.
The purpose of the resilient back-propagation (RB) learning is to remove the harmful effect of the
magnitude of partial derivatives. It uses only the sign of the partial derivative to determine the
direction of the weight update. The magnitude of the weight change is calculated as following
rule [8]:

25
• Values of each weight and bias are increased when the derivative of performance
function with respect to that weight has same sign for two successful iterations.
• Values of each weight and bias are decreased when the derivative with respect to weight
changes sign from the previous iteration.
• If the derivative is zero the values of the weights and biases are remain same.
• When weights oscillate, values of weight change is reduced.
• If weights continuously change in the same direction, weight change is increased.
3.5. Levenberg-Marquardt (LM) Algorithm
It provides a numerical solution to the problem of minimizing a generally non-linear function,
over a space of parameters for the function. It is popular alternative to the Gauss-Newton method
of finding the minimum of a function. It approaches second order training speed without
computing the Hessian Matrix [9],
If the performance function is of the form of a sum of squares, then Hessian matrix can be
approximated as:
H = JT
J (9)
and the gradient is:
g = JT
e (10)
where J represents the Jacobian matrix containing the first derivatives of network error with
respect to weights and biases, and e is the vector of network errors.
The Jacobian matrix can be computed through a standard back-propagation technique that is
much complex than computing Hessian matrix. This approximated matrix is used in following
Newton like update
xk + 1 = xk - [ JT
J + µ I ]-1
JT
e (11)
When the scalar µ = 0, it becomes the Newton's method on approximated Hessian matrix. When µ
is large, it becomes gradient descent with small step size. The Newton's method is faster and more
accurate so algorithm shifts towards it. So the µ is decreased with each successful step (i.e. when
performance function reduces). It is increased when a tentative step would increase the
performance function. So the performance function always reduces at each iteration. It is more
powerful than conventional gradient descent technique.
LM Algorithm is very sensitive to initial network weights. It does not consider outliers in the data
which may lead to over-fitting noise. To avoid these situations, Regularization technique is used.
One of such technique is Bayesian Regularization.
3.6. Bayesian Regularization (BR)
It is used to overcome the problem of interpolating noisy data. Mackay (1992) proposed Bayesian
framework which can be directly applied to the NN learning problem. It allows to estimate
effective number of parameters actually used by the model i.e. the number of network weights
actually needed to solve a particular problem. Bayesian Regularization expands the cost function
to search not only for minimal error but for minimal error using minimal weights. By using
Bayesian Regularization one can avoid costly cross validation. It is particularly useful for the

26
problems that cannot, or would suffer, if a portion of available data were reserved to a validation
set. Furthermore, the regularization also reduces or eliminates the need for testing different
numbers of hidden neurons for a problem. Bayesian regularized ANN are difficult to be over
trained and over-fit.
4. CONFUSION MATRIX
Confusion matrix or contingency table is used to evaluate the performance of a machine learning
classifier. We used four attributes of confusion matrix while evaluating the performance of the
algorithms. These attributes are Sensitivity, Specificity, Efficiency and Accuracy. These attributes
are defined as following:
Sensitivity or True Positive Rate (TPR), also known as Recall Rate is given by:
Sensitivity
=)
(=) /
(12)
Specificity or True Negative Rate (TNR) is given by:
SpeciAicity
=/
() =/
(13)
Efficiency is given by:
EfAiciency
CDEFGHGIC(DJAJGI
*

=)K=/K
*
(14)
Accuracy is given by:
Accuracy
=)=/
()/
(15)
where P is number of positive instances, N is number of negative instances, TP is number of
correctly classified positive instances, TN is number of correctly classified negative instances, FP
is number of incorrectly classified as positive instances and FN is number of incorrectly classified
as negative instances.
5. EXPERIMENT
We evaluated 3 supervised learning algorithm which uses back-propagation on multilayer
perceptron neural network. We checked the performance of these algorithm to automatically
detect web spam. These algorithms are Conjugate Gradient, Resilient Back-propagation and
Levenberg-Marquardt learning.
We created neural network with single hidden layer and used one neuron in the output layer. We
employed bipolar sigmoid function which has the output range of [ -1 , 1 ].
(
*
OP − 1 (16)
We selected in the experiment:
Stopping Criteria as Number of Iterations θ = 100,
Learning Rate α = 0.1

27
Number of neurons in hidden layers = 10 (or 20 where specified).
We created a corpus of 368 instances of manually selected web pages, in which about 30%
instances were labelled as spam and rest of the pages were labelled as ham. To create training
dataset, we randomly selected about 80% of records from the corpus and for testing we used
remaining 20% of the records.
We extracted total 31 low cost quality features of the pages and categorized them in 3 categories:
URL (10 features), Content (16 features) and Link (5 features). We call these factors low cost
because they are computationally less expensive to be extracted. These features are listed in table
1.
Table I. Low Cost Quality Features of a Web Page
URL Features
SSL Certificate
Length of URL
URL is not a sub-domain
TLD is authoritative (*.gov, *.edu, *.ac)
Domain contains more than 2 same consecutive
alphabet)
Sub-domain is more than level 3 (e.g.
mocrosoft.com.mydomain.net)
Domain contains many digits and special symbols
IP address instead of Domain Name
Alexa Top 500 site.
Domain Length
Content Features
HTML Length
Word Count
Text Character Length
Number of Images
Description Length
Existence of H2
Existence of H1
Video Integration
Number of Ads
Title Character Length
Compression Ratio of Text
Ratio of text to HTML
Presence of alt text for images
Presence of obfuscated JavaScript code (pop-ups,
redirections etc)
% of Call to Action in the Text
Percentage of Stop words in Text
Links Features
Number of Internal links
Internal link is self referential
Number of External links
Fraction of anchor text / Total Text
Word Count in Anchor Text

28
To test the performance of each algorithm, we trained, tested and obtained 10 performance result
values for each category and calculated the average.
We used Accord.net and Aforge.net libraries to create neural network.
6. RESULT
We have created tables (Table II to Table VIII) to show the performance results of each
algorithm. Consider the number of neurons in hidden layer as 10 unless specified. The values in
the tables represent average of 10 experimental readings of each category. The values in
underlined show the best results.
Table 2. Using Url Features Only (10 Features)
Algorithm Sensitivity
(TPR)
Specificity
(TNR)
Efficiency Accuracy Training Time
(seconds)
CG 0.9153 0.3088 0.6121 0.5716 0.149
RB 0.4653 0.9117 0.6885 0.7183 0.168
LM 0.6884 0.7647 0.7265 0.7316 2.723
LM+BR 0.4224 0.9264 0.6744 0.7090 5.790
Table 3. Using Content features Only (16 Features)
(TPR)
Specificity
(TNR)
(seconds)
CG 0.7384 0.8823 0.8104 0.8200 0.198
RB 0.7269 0.9205 0.8237 0.8366 0.191
LM 0.6615 0.8441 0.7528 0.7650 9.685
LM+BR 0.6116 0.9380 0.7748 0.7952 16.445
Table 4. Using Links features Only (5 Features)
(TPR)
Specificity
(TNR)
(seconds)
CG 0.9230 0.6382 0.7806 0.7616 0.113
RB 0.6692 0.9117 0.7904 0.8066 0.157
LM 0.7230 0.9088 0.8159 0.8282 1.923
LM+BR 0.7038 0.8880 0.7959 0.8080 2.722
Table 5. Using URL + Links Features (15 Features)
(TPR)
Specificity
(TNR)
(seconds)
CG 0.9538 0.7970 0.8754 0.8650 0.191
RB 0.8076 0.9823 0.8950 0.9066 0.187
LM 0.8115 0.9352 0.8734 0.8816 3.744
LM+BR 0.9115 0.9558 0.9337 0.9366 13.458

29
Table 6. Using URL + Content features (26 Features)
(TPR)
Specificity
(TNR)
(seconds)
No. of neurons in hidden layer=10
CG 0.8846 0.9264 0.9055 0.9083 0.266
RB 0.8538 0.9764 0.9151 0.9233 0.190
LM 0.8807 0.8823 0.8815 0.8816 10.743
LM+BR 0.7115 0.9911 0.8513 0.8700 59.957
CG 0.8730 0.9558 0.9144 0.9200 0.539
RB 0.8576 0.9823 0.9200 0.9283 0.312
LM 0.8884 0.9205 0.9045 0.9066 32.789
LM+BR 0.7692 1.000 0.8846 0.9000 304.134
Table 7. Using Content + Links features (21 Features)
(TPR)
Specificity
(TNR)
(seconds)
CG 0.7269 0.8705 0.7987 0.8083 0.230
RB 0.7593 0.9088 0.8340 0.8438 0.188
LM 0.7038 0.8441 0.7739 0.7833 7.940
LM+BR 0.6846 0.9470 0.8158 0.8333 36.674
Table 8. Using URL + Content + Links features (31 Features)
(TPR)
Specificity
(TNR)
(seconds)
CG 0.8500 0.9676 0.9088 0.9166 0.297
RB 0.8769 0.9735 0.9252 0.9316 0.214
LM 0.8653 0.9029 0.8841 0.8866 13.146
LM+BR 0.6923 0.9852 0.8388 0.8583 92.248
CG 0.8384 0.9823 0.9104 0.9200 0.575
RB 0.8807 0.9588 0.9197 0.9250 0.377
LM 0.8653 0.9264 0.8959 0.9000 45.499
LM+BR 0.8115 0.9882 0.8998 0.9116 357.580
Data from the tables suggest that Conjugate Gradient (CG) Algorithm gave best TPR (Sensitivity)
in most of the categories, whereas Levenberg-Marquardt algorithm with Bayesian Regularization
(LM+BR) gave best TNR (Specificity) in most of the categories.
The overall best classification performance was achieved in most of the categories by Resilient
Back-propagation (RB) algorithm in both Efficiency and Accuracy measures.
The training time was found lowest in most of the cases for Resilient Back-propagation (RB) so
we can say that it is not only best in classification, it is fastest algorithm as well. Conjugate

30
Gradient (CG) works fast when number of inputs are less, but when number of inputs are
increased, its becomes slower than RB. The slowest algorithm observed was Levenberg-
Marquardt with Bayesian Regularization (LM+BR) in all of the cases in the experiment.
The overall classification performance of each algorithm improved with increased number of
page quality factors but training time also increased. The cases where the number of quality
factors were high, increasing number of neurons improved the classification performance of
algorithms but it also increased the training time.
7. CONCLUSION
In the experiment we can conclude that the Resilient Back-propagation algorithm is fastest and
performs best in both Efficiency and Accuracy measures. Conjugate Gradient algorithm gives
best sensitivity, whereas Levenberg-Marquardt algorithm with Bayesian Regularization gives best
specificity but it is the slowest when training time is considered.
Classification performance of each algorithm improves with increased input factors. If the
number of factors are high, increased number of neurons improves the performance of algorithm.
The training time increases for all algorithms when either number of inputs are increased or
number of neurons in hidden layer are increased.
REFERENCES
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graphsoms and graph neural networks, in Proc. of the 20th ICANN, Thessaloniki, Greece (2010)
372–381.
[3] M. Erdelyi, A. Garzo, and A. A. Benczur, Web spam classification: a few features worth more, in
Proceedings of the 2011 Joint WICOW/AIRWeb Workshop on Web Quality, WebQuality'11,
Hyderabad, India, 2011.
[4] B. Biggio, B. Nelson, and P. Laskov, Support vector machines under adversarial label noise, in
JMLR: Workshop and Conference Proceedings 20, Taoyuan, Taiwan, 2011, pp. 97–112.
[5] H. Xiao, H. Xiao, and C. Eckert, Adversarial label flips attack on support vector machines,
presented at the 20th European Conference on Artificial Intelligence (ECAI), Montpellier, France,
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[6] Adeli H Hong SL, Machine learning neural networks genetic algorithms and fuzzy systems (John
Wiley Sons Inc., New York, NY, USA) 1995.
[7] Fletcher R Reeves CM, Computer J, 7 (1964) 149-153.
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[9] More JJ, in Numerical Analysis, edited by Watson GA, Lecture Notes in Mathematics 630,
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