Performance Evaluation of Classifiers used for Identification of Encryption Algorithms

ACEEE Int. J. on Network Security , Vol. 02, No. 04, Oct 2011

Performance Evaluation of Classifiers used for
Identification of Encryption Algorithms
Suhaila Omer Sharif1, Saad P. Mansoor2
1,2
School of Computer Science, Bangor University, Bangor, UK
1
s.o.sharif@bangor.ac.uk
2
s.mansoor@bangor.ac.uk

Abstract—Evaluating classifier performance is a critical for Identification of a block cipher. The identification of
problem in pattern recognition and machine learning. In this encryption method was considered as a document
paper pattern recognition techniques were applied to identify categorization task. A.Cufoglu et al. [2] used pattern
encryption algorithms. Four different block cipher algorithms recognition classifiers to identify user profiling. The results
were considered, DES, IDEA, AES, and RC2 operating in
observed that NB classifier generate the best performance
(Electronic Codebook) ECB mode. Eight different classification
techniques were used for this purpose, these are: Naïve over user related information. In [3] a study was carried out
Bayesian (NB), Support Vector Machine (SVM), neural to use classifiers to identify the cipher encryption method.
network (MLP), Instance based learning (IBL), Bagging (Ba), The simulation results show that, the RoFo classifier has the
AdaBoostM1 (MdaBM1), Rotation Forest (RoFo), and Decision highest classification accuracy attributes by using tree
Tree (C4. 5). The result shows that using pattern recognition structures obligatory on Naïve Bayesian. In this study a
is a useful technique to identify the encryption algorithm, simulation test was carried to evaluate different types of
and according to our simulation using one encryption of key classifiers in terms of classification accuracy of the encryption
provide better classification than using different keys. data.
Furthermore, increase the number of the input files will
improve the accuracy.
II. BASICS OF CLASSIFIERS

Index Terms—Pattern Recognition, Classifiers, Encryption, This section briefly explains the classifiers used in this
Cryptanalysis study.
I. INTRODUCTION A. Naive Bayesian (NB)
A typical cipher takes a plaintext message and some secret Naïve Bayesian is one of the most computationally
keying data as its input and produces an encrypted version straightforward and efficient Bayesian Classifier methods. It
of the original message known as the ciphertext. An attack has been used as an effective classifier for many years. It has
on a cipher can make use of the ciphertext alone or it can two advantages over many other classifiers. The first being,
make use of some plaintext and its corresponding ciphertext. it is easy to construct, as the structure is given a priori thus,
Cryptanalysis is the process of recovering the plaintext and/ no structure learning procedure is required. Secondly, the
or key from a ciphertext. Most encryption algorithms have a classification process is very efficient. This is due to the
finite key space and, hence, are vulnerable to an exhaustive assumption that all the features are independent of each other.
key search attack. However, it is very difficult to identify the It has surprisingly out performed many sophisticated
encryption keys because the size of the key is such that the classifiers, especially where the features are not strongly
time and resources required are not generally available. A correlated [4].
random search through a finite but large key space is not B. Support Vector Machines (SVMs)
usually an acceptable cryptanalysts tool. In cryptanalysis This is a useful technique for data classification. It is
when only the ciphertext is obtained, there are initially two based on statistical learning theory presented by V.N.Vapnik,
significant tasks, identification of the encryption technique and has been productively applied to many classification
applied, and the encryption key identification. Statistical and pattern recognition problems such as text classification
techniques and machine learning based techniques have been and image recognition. These methods do classification by
applied to identify the encryption method from the encrypted building an N-dimensional hyper plane that optimally divides
files. In this work, however, we are exploring the possibility the data into two groups: the training and test data points.
of identifying encryption algorithm by applying pattern The objective of SVM method is to produce a model (based
recognition techniques. The statistical methods use the on the training data) which predicts the target values of the
frequency at which letters of the alphabet occur in the test data knowing only the test data attributes [5].
encrypted file. While in machine learning based methods,
the task of identification of the encryption method is C. Neural Networks (MLP)
considered as a pattern classification task. There are a number These classifiers are widely used in many fields. They
of research papers that evaluate some of the classification model the concept of neurons in neuroscience and include
algorithm. Dileep [1] suggests using a Support Vector Machine sets of connected nodes along with the weights for nodes
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and arcs. Neural nets are self-organizing, adaptive and for a base classifier, the feature set is randomly split into K
nonlinear. Neural network are versatile and powerful classifiers, subsets (K is a parameter of the algorithm) and Principal
but they rely on a number of parameter choices to specify the Component Analysis (PCA) is applied to each subset. All
network architecture and to control the training process [5,6]. principal components are retained in order to preserve the
variability information in the data. Thus, K axis rotations take
D. Instance Based Learner (IBL)
place to form the new features for a base classifier. The idea
IBL generates classification predictions using only behind the rotation approach is to encourage simultaneously
specific instances. Unlike Nearest Neighbour algorithm, IBL individual accuracy and diversity within the ensemble.
normalizes its attributes’ ranges, processes instances Diversity promoted through the feature extraction for each
incrementally, and has a simple policy for tolerating missing base classifier. Decision trees were chosen here because they
values. IBL applies a simple Euclidean distance function to are sensitive to rotation of the feature axes, hence the name
supply graded matches between training instances and given “forest.” Accuracy is sought by keeping all principal
test instance [7]. Equation (1) represents the similarity that is components and using the whole data set to train each base
used within the IBL algorithm. classifier. A very useful characteristic of the proposed method
Similarity(x, y) = (1) is that RoFo can be used in conjunction with practically any
base classifies that are used in the ensemble creation process.
Where n: is the number of instance attributes The thought of the rotation approach is to encourage
simultaneously individual accuracy and diversity within the
The numeric value attributes are represented by (2). ensemble.

f (xi, yi) = (xi – yi)2 (2) H. C4.5 Classification Algorithm
The Boolean and symbolic attributes are represented by (3). This algorithm constructs a decision tree (DT) starting
from a training set, which is a set of cases. Each case specifies
f (xi, yi) = (xi yi) (3) values for a collection of attributes and for a class. The
decision tree is used to classify (assign) a class value to a
E. Bagging (Ba) case depending on the values of the attributes of the case.
This ensemble approach uses a predefined number of The class specified at the leaf is the class predicted by the
classifier, each one trained on a bootstrap sample of training decision tree. A performance measure of a decision tree over
data. Each classifier is trained on a set of n training examples, a set of cases is the classification error which is the percentage
drawn randomly with replacement from the original training of the misclassification cases whose predicted class differs
set of size n. Such a training set is called a bootstrap replicate from actual class [12].
of the original set. Each bootstrap replicate contains, on
average, 63.2% of the original training set, with many examples III. EXPERMINT SETUP
appearing multiple times. [8, 9].
Numbers of experiments were carried out to establish the
F. AdaBoost Algorithm Boosting classifiers accuracy to identifying the following encryption
The most popular classifier is AdaBoost, a powerful algorithms AES, RC2, DES and IDEA, operating in electronic
classifier built with linear combination of member classifiers. codebook (ECB) mode. In the simulation, different instances
The idea behind it is obtaining a highly accurate classifier by were used (120, 240, and 400); these represent the total number
combining many weak classifiers. Each weak classifier is of input files (ciphertext) used. The plaintext was encrypted
required to be moderately accurate, i.e. better than random using 1, 3, 5, and 30 keys for each algorithm. Matlab® was
guessing. The classifiers in the ensemble are added one at a used to extract an 8-bits histogram of the encrypted data;
time so that each subsequent classifier is trained on data, this was then submitted to WEKA (Weikato Environment for
which have been “hard” for the previous ensemble members. Knowledge Analysis) machine learning platform that supplies
A set of weights is maintained across the objects in the data a work bench consisting of a collection of learning schemes,
set so that objects that have been difficult to classify acquire which are applied for data mining and machine learning. The
more weight, forcing subsequent classifiers to focus on them. primary learning schemes in Weka are “classifiers” and they
The ensemble construction through AdaBoost comes from tempt a rule set or decision tree that models the data, in
theory and is, in fact, equivalent to a logistic regression model addition Weka has different algorithms for learning
by a fitting an additive stage-wise estimation procedure. One association rules clustering data, visualization, regression,
of the reasons why AdaBoost algorithm yields good results and data processing.
is the diversity among the weak classifiers. Still, there is no
specific benchmark to determine diversity in the process [10, IV. SIMULATION RESULTS
11]. In this section the results of eight classifiers are com-
G. RotationForest (RoFo) pared, based on the accuracy of the identification. The simu-
lations conducted using four different block cipher algorithms
This classifier is based on feature extraction and was
with different key sizes. Each dataset have the same number
proposed by Rodriguez et al [10]. To create the training data
© 2011 ACEEE 43
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of attributes and different number of instances, which varies 64-bits, IDEA 128-bits, RC2 128-bits we used 30, 60 and 100
from 120 to 400 instances. A 10 fold cross-validations was input files, thus the total numbers of the instances are 120,
used as a test mode where 10 pair of training sets and testing 240 and 400 respectively. Fig 2 shows that for all classifiers
sets is created. All the classifiers run on the same training sets using 400 input files achieve high accuracy and using 100
and have been tested on the same testing sets to obtain the input files achieved lowest accuracy. It is also evident that
classification accuracy. To estimate a value on training data RoFo classifier produces the best results for all instances,
set, we define an accuracy measure as shown in equation (4). and IBL perform very badly when operating with 100 and
240 instances. In final simulation the effect of the number of
Accuracy (%) = (4)
keys used to encrypt the plaintext was investigated for the
In the first simulation, we have been using 240 input files following encryption algorithm AES, RC2, and IDEA with fixed
with AES (128,192, and 256-bits), DES 64-bit, IDEA 128-bit, 128-bit encryption key and DES with 64-bits. The number of
and RC2 (42, 84, and128-bits) where each algorithm has 30 input files used was 30 and the number of keys was 1, 3, 5 and
input files. The simulation was conducted using different 30. Here, Fig 3 shows that RoFo classifier has better overall
numbers of keys for each algorithm; Fig 1 shows the calculated performance and AdaBM1 achieved lowest accuracy. Fur-
accuracy for different set of data. It can be seen that, using 1 thermore, and as expected all the classifiers (apart from
encryption key produces high classification accuracy (100%) AdaBM1) produce good accuracy when using 1, 3 and 5
meaning that the 240 instances (files) were correctly classified. encryption keys. It can be seen as expected that the accu-
In contrast using 30 different encryptions keys (one for each racy drops to 25 –55 when using one key per each file (30
file) result in lower classification accuracy. This is expected keys).
as the keys are generated randomly and will affect the pattern
of test data. The results show that the accuracy of the
classification will be reduced with increased the number of
the encryption keys. The second simulation, deals with the
effect of increasing the number of input files (instances) on
overall accuracy. Here for each algorithm, AES 128-bits, DES

Figure 3. Encryption accuracy for different key seizes

Figure 1. Classification accuracy for each algorithm

(a) All data

(b) Class centers
Figure 2. Classification accuracy for each algorithm Figure 4. Scatter plot for 240 data points

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Fig 4, shows data scatter-plots and the centres of the 240 REFERENCES
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CONCLUSIONS a Quarterly Journal in Modern Foreign Literatures, 2007.
In this paper, a simulation experiment was conducted to
identify encryption algorithm of encrypted data using variety
of classifies. The results show that pattern recognition
techniques, are useful tools for cryptanalysis as a means of
identifying the type of encryption algorithm used to encrypt
the data. This work shows that increasing the number of
encryption keys will result in reducing the classification
accuracy. Nevertheless, the result shows that it is possible
to achieve accuracy above 40% with some classifiers when
each file was encrypted with different key. It was also clear
that increase the number of files used will improve accuracy.

© 2011 ACEEE 45
DOI: 02.ICT.2011.02. 60

Performance Evaluation of Classifiers used for Identification of Encryption Algorithms

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Performance Evaluation of Classifiers used for Identification of Encryption Algorithms