File Name Classification Approach to Identify Child Sexual Abuse
Mhd Wesam Al-Nabki
1,2 a
, Eduardo Fidalgo
1,2 b
, Enrique Alegre
1,2 c
and Roc
´
ıo Al
´
aiz-Rodr
´
ıguez
1,2 d
1
Department of Electrical, Systems and Automation, Universidad de Le
´
on, Spain
2
Researcher at INCIBE (Spanish National Cybersecurity Institute), Le
´
on, Spain
Keywords:
Short Text Classification, File Name Classification, Active Learning, Character-level Convolutional Networks,
Child Sexual Abuse.
Abstract:
When Law Enforcement Agencies seize a computer machine from a potential producer or consumer of Child
Sexual Exploitation Material (CSEM), they need accurate and time-efficient tools to analyze its files. However,
classifying and detecting CSEM by manual inspection is a high time-consuming task, and most of the time,
it is unfeasible in the amount of time available for Spanish police using a search warrant. An option for
identifying CSEM is to analyze the names of the files stored in the hard disk of the suspect person, looking in
the text for patterns related to CSEM. However, due to the particularity of this file names, mainly its length
and the use of obfuscated words, current file name classification methods suffer from a low recall rate, which
is essential in the context of this problem. This paper presents our ongoing research to identify CSEM through
their file names. We evaluate two approaches of short text classification: a proposal based on machine learning
classifiers exploring the use of Logistic Regression and Support Vector Machine and an approach using deep
learning by adapting two popular Convolutional Neural Network (CNN) models that work on character-level.
The presented CNN achieved an average class recall of 0.86 and a recall rate of 0.78 for the CSEM class. The
CNN based classifier could be integrated into forensic tools and services that might support Law Enforcement
Agencies to identify CSEM without the need to access systematically to the visual content of every file.
1 INTRODUCTION
Child Sexual Exploitation Material (CSEM) is de-
fined as sexual abuse of a person under 18 years old,
together with producing images or videos of the abuse
and sharing the content online (Europol, 2019a). One
of the features of some Darknet networks, such as
The Onion Router (Tor)
1
or FreeNet
2
(Al-Nabki et al.,
2017; Al-Nabki et al., 2019) and also Peer to Peer
(P2P) networks, like eDonkey, (Panchenko et al.,
2012; Peersman et al., 2016) is their capability of
preserving a high level of privacy and anonymity of
their users. This characteristic allows pedophiles to
easily share CSEM far away from Law Enforcement
Agencies (LEAs) monitoring. Therefore, in 2017, the
Council of the European Union (EU) has prioritized
a
https://orcid.org/0000-0002-3975-3478
b
https://orcid.org/0000-0003-1202-5232
c
https://orcid.org/0000-0003-2081-774X
d
https://orcid.org/00000-0003-4164-5887
1
https://www.torproject.org/
2
https://freenetproject.org/
cybercrimes related to Child Sexual Abuse (CSA) to
be the most serious crime between the years 2018 and
2021 (Europol, 2019b).
CSEM producers and consumers might save this
content on their local computer machines, at least
temporary. When an LEA enters a home to inspect
a computer of a suspect, a police agent reviews the
files contained in the investigated hard drive, trying to
determine whether or not the suspected of pedophilia
has stored CSEM in the computer (Gangwar et al.,
2017). This process needs to be accomplished in a
limited time and as accurately as possible (Chaves
et al., 2019).
In this paper, we present our ongoing research
based on Natural Language Processing to identify
CSEM. More specifically, we are designing a text
classifier to decide whether a given file is CSEM
or not, according to its name. The same process,
based on the classification of the content of the files,
whether images or videos, is out of the scope of this
paper and research.
Building a supervised text classifier for CSEM
identification is a challenging task due to several rea-
228
Al-Nabki, M., Fidalgo, E., Alegre, E. and Aláiz-Rodríguez, R.
File Name Classification Approach to Identify Child Sexual Abuse.
DOI: 10.5220/0009154802280234
In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2020), pages 228-234
ISBN: 978-989-758-397-1; ISSN: 2184-4313
Copyright
c
 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

sons. First, a binary supervised algorithm requires
training samples of Non-CSEM and CSEM files.
However, there are no publicly available datasets of
the latter class, and crawling samples from a P2P net-
work or the Darknet is illegal (Garc
´
ıa-Olalla et al.,
2018). Therefore, only CSEM file names obtained
legally, i.e., provided by LEAs, could be used.
Second, a file name typically is a text of small
length, which leads to a sparse representation of the
samples because we have a massive number of fea-
tures, while an instance is only represented with a
few of them. Finally, CSEM producers or consumers
tend to invent a personalized file name style to create
their own vocabulary, abbreviations, and acronyms to
circumvent detection tools, using a personalized ob-
fuscated style of writing. For example, in a sample
named “!!!!yoB0yXX”, the exclamation marks refer
to the age of a boy, and the letter O is replaced by the
number zero. Hence, most likely, this file could be
related to the abuse of a four years old boy.
In this paper, we present an approach to address
the aforementioned challenges by designing a super-
vised File Name Classification (FNC) system. We ex-
plore two different text classification approaches: 1)
conventional machine learning (ML) pipeline and 2)
deep learning (DL) approach using a Convolutional
Neural Network (CNN). We evaluate the classifiers
on a custom dataset of 65, 351 samples.
The rest of the paper is organized as follows: Sec-
tion 2 presents the related work. After that, Section 3
describes the designed classification pipelines. Next,
Section 4 explains how the dataset is created and what
are its main features. Then, in Section 5, we describe
the experimentation performed and discuss the results
obtained. Finally, Section 6 presents the conclusions
by pointing to our ongoing and future research.
2 RELATED WORK
The problem of short text classification is widely stud-
ied under different research lines (Sriram et al., 2010;
Sun, 2012; Shang et al., 2013; Rana et al., 2014; Lee
and Dernoncourt, 2016; Alsmadi and Hoon, 2018). In
the following, we explore two text classification prob-
lems that are close to File Name Classification (FNC).
First, news headlines classification task attempts
to group news articles based on their titles, in which
the title is made up of a few words. (Rana et al., 2014)
proposed a pipeline for news headlines classification
that consists of three stages: data pre-processing, text
representation, and classification. In the data pre-
processing step, the text was tokenized into words,
and spaces replaced special characters, stop words
were removed, and the text was stemmed. For the
text representation, the authors used Term Frequency-
Inverse Document Frequency (TF-IDF), Information
Gain (IG) (Shang et al., 2013), and Boolean Weight
(BW) (Chouchoulas and Shen, 1999). Finally, in
the classification stage, Rana et al. explored Naive
Bayes (NB) (Kim et al., 2006), Support Vector Ma-
chine (SVM) (Joachims, 1998), K-Nearest Neighbor
(KNN) (Hotho et al., 2005), and Decision Trees (DT)
(Safavian and Landgrebe, 1991). However, the core
difference between our problem and news headlines
classification is that the latter has high-quality input
text, where the punctuation marks are maintained cor-
rectly, and there are no misspelled words.
The second research line is classifying tweets
from Twitter. According to (Perez, 2019), the most
common length of a tweet is 33 characters, while the
maximum number of characters is 280. Despite the
length of the text, Perez (Perez, 2019) work also deals
with the low-quality input text as in our work. Thus,
both might use abbreviations to save space or mis-
spell some words. (Imran et al., 2016) pre-processed
the tweets by removing hyperlinks, mentions and stop
words, and then they used the N-grams and IG tech-
niques for feature extraction and Random Forest clas-
sifier (Breiman, 2001). (Alsmadi and Hoon, 2018)
addressed short noisy text classification on Twitter.
They proposed a supervised word weighting schema
to highlight essential terms in short noisy text along
with an SVM classifier.
(Chen et al., 2018) proposed a framework to iden-
tify cyberbullying on Twitter. For text representa-
tion, they compared pre-trained language models, like
Word2Vec (Mikolov et al., 2013) and GloVe (Pen-
nington et al., 2014), with traditional text encod-
ing techniques, such as TF-IDF, and they realized a
decline in the performance when embedding-based
were used. For classification, they compared tradi-
tional ML classifiers like Logistic Regression (LR)
and SVM with DL classifiers, like Long Short-Term
Memory (LSTM) (Lee and Dernoncourt, 2016) and
Convolutional Neural Network (CNN) (Zhang et al.,
2015).
Few researchers employed file names classifica-
tion approach to recognize child sexual activities.
(Panchenko et al., 2012) attempted to normalize file
names using short message service (SMS) normaliza-
tion techniques proposed by (Beaufort et al., 2010).
On the top of the normalized text, they trained an
SVM classifier and obtained an accuracy of 96.97%
on their dataset. The study of (Peersman et al., 2014)
proposed a framework called iCOP to detect CSEM
activities on P2P networks is proposed. The first stage
of their classification pipeline was a dictionary-based
File Name Classification Approach to Identify Child Sexual Abuse
229

filter that was constructed manually and held CSEM
keywords. Also, they used character n-gram of size
two, three, and four, to capture more features about
the file name. These features were used to train a bi-
nary SVM classifier. Afterward, in their recent work
(Peersman et al., 2016), they used the same represen-
tation but benchmarked more classifiers, like SVM
and NB. They evaluated their proposal on a custom
dataset, and they observed that the SVM classifier
could identify CSEM file names with a recall rate of
0.43.
3 METHODOLOGY
In this section, we present the methodology used to
build the proposed file name classifier (FNC). In the
following, we explore two distinct approaches based
on machine learning and deep learning.
The pre-processing step is common to both ap-
proaches, where special characters and numbers are
replaced by # and $, respectively, reducing the spar-
sity of the features. For example, the input text
“!!!!yoB0yXX” will be encoded into “####yoB$yXX”.
Another benefit of this representation is the removal
of the duplicated instances that differ only by their
names. For example, a folder in a seized computer
could have more than 100 images named IMG01.png,
IMG02.png, ... IMG100.png, whereas all these names
are repeating the same information of IMG##.png.
3.1 Machine Learning Approach
The typical design of a supervised text classifier has
two main components: text representation to encode
the input samples into feature vectors and a classifier
that separates these feature vectors into CSEM and
Non-CSEM examples.
A crucial step in a text classification pipeline is
finding an adequate representation of the text. Tok-
enizing a file name on word-level may not be the most
efficient solution because the text of a file name might
be joined as a single word or using a special charac-
ter. Therefore, we tokenize the text at the character
level, following the work of other researchers (Peers-
man et al., 2014; Peersman et al., 2016).
The N-gram technique extracts all the patterns of
two to five consecutive characters of a given file name,
which form a set of tokens. In this work, we com-
bine the widely used Term Frequency-Inverse Docu-
ment Frequency (TF-IDF) technique (Aizawa, 2003)
with N-gram. TF-IDF is a statistical model that as-
signs weights to file name tokens. It accentuates those
whose frequency is higher in a given file name and, at
the same time, de-emphasizes tokens that frequently
occur in many files. This way, it overcomes the issue
of misspelled words or personalized naming style in
file names. Table 1 shows an example of two to five
grams of a file name “####yoB$yXX”. Furthermore,
to discard noisy tokens, we set thresholds for the min-
imum and the maximum term frequency.
Table 1: Example of preprocessing and tokenizing a file
name with two, three, four, and five grams.
Original
File Name
!!!!yoB0yXX
Preprocessing ####yoB$yXX
2-grams ##, ##, ##, #y, yo, oB, B$, $y, yX, XX
3-grams ###, ###, ##y, #yo, yoB, oB$, B$y, $yX, yXX
4-grams ####, ###y, ##yo, #yoB, yoB$, oB$y, B$yX, $yXX
5-grams ####y, ###yo, ##yoB, #yoB$, yoB$y, oB$yX, B$yXX
For this work, we select two commonly used super-
vised classifiers that have shown good performance
on text classification tasks (Peersman et al., 2016; Al-
Nabki et al., 2017). They are Support Vector Machine
(SVM) (Schohn and Cohn, 2000) and Logistic Re-
gression (LR) (Genkin et al., 2007).
3.2 Deep Learning Approach based on
Convolutional Neural Networks
Convolutional Neural Networks (CNN) have been
widely and successfully used for image classification
(Fidalgo et al., 2018; Fidalgo Fern
´
andez et al., 2019).
Besides the machine learning approaches mentioned
above, we employed CNN to classify file names. We
adapted the models of (Zhang et al., 2015) and (Kim
et al., 2016), as they showed promising performance
on Natural Language Processing (NLP) tasks.
The model of Kim et al. was intended for the lan-
guage modeling task and uses only character-level in-
puts but the prediction is performed at the word-level.
However, we adapt it to a text classification problem
by replacing the subsequent recurrent layers with a
dense layer to perform softmax over the classes. Sim-
ilarly, the model of Zhang et. al applied only on
characters and does not require acquiring knowledge
about the syntactic or semantic structure of the ad-
dressed language. Unlike Kim’s model, this one is
dedicated to text classification tasks.
4 DATASET CONSTRUCTION
Building a supervised file name classifier requires
collecting samples of both classes, CSEM and Non-
CSEM. For the Non-CSEM class, we refer to a dataset
published by the National Software Reference Li-
ICPRAM 2020 - 9th International Conference on Pattern Recognition Applications and Methods
230

brary (NSRL)
3
. We were able to access more than
32 million file names, but this will make our problem
very imbalanced and skewed to the Non-CSE class.
Therefore, we selected an initial subset of 64, 000
Non-CSEM examples, resulting in 56, 021 after ap-
plying the pre-processing step.
Regarding the CSEM class, we were able to col-
lect these examples thanks to the collaboration be-
tween the Spanish National Cybersecurity Institute
(INCIBE)
4
and Spanish LEAs. This latter provided
us a list with dumps of hard disks that had been seized
from criminals’ computers. The list had 64, 857
CSEM samples. However, after pre-processing them,
the number decreased to 9, 330 unique instances.
Table 2: Description of the used dataset to train the file
names classifier.
File Name Class CSEM Non-CSEM Total
Before preprocessing 64,857 64,000 128,857
After preprocessing 9,330 56,021 65,351
5 EMPIRICAL EVALUATION
5.1 Experimental Setting
The experiments were conducted on a PC with an In-
tel(R) Core(TM) i7 processor with 32 GB of RAM
under Windows-10. We used Python3 with Keras
framework
5
for the CNN implementation and Scikit-
Learn
6
for the machine learning classifiers.
Regarding the configuration of the ML approach,
we used character n-grams, extracting patterns from
two to five grams. Also, we set thresholds for the
minimum and the maximum gram proportion to 0.995
and 0.001, respectively. For the LR, we set the param-
eter C to 10, empirically, which refers to the inverse
of regularization strength. Concerning the SVM clas-
sifier, we used a linear kernel, and we left all the other
parameters to their default values, as set by the Scikit-
Learn library.
Concerning the neural network model of (Zhang
et al., 2015), we used the same model hyperparame-
ters as described in their paper, except for the input
feature-length. We set it to 128 instead of 1014 be-
cause the longest file name we had was 125 charac-
ters. Likewise, for the CNN model of (Kim et al.,
2016), we left the same hyperparameters as described
3
https://www.nist.gov/software-quality-group/about-
nsrl/nsrl-introduction
4
In Spanish, it stands for the Instituto Nacional de
Ciberseguridad de Espa
˜
na
5
https://github.com/fchollet/keras
6
https://scikit-learn.org/stable/
in their paper. The only change was made to the out-
put layer when we replaced the recurrent layers with
a dense layer with softmax function.
In order to estimate the model performance, we
used 70% of the samples to train the model, and 30%
to test the models. The validation set forms 10% of
the training set. Table 3 describes the dataset used to
train and test the model.
Table 3: A description of the dataset used to train, validated,
and test the file name classifier.
File Name Class Train Validation Test
Number of Samples 36,548 9,137 19,666
To control the number of iterations while training the
neural network, we set an early stopping criterion,
which is triggered if there is no further improvement
on the validation set during 10 epochs of training.
5.2 Evaluation Metric
The principal objective of this work is to assist LEAs
in the detection of CSEM through their file names,
avoiding the exposure of an agent to CSEM. In this
context, it is desirable to have a low number of false
negatives - a file named with CSEM content identified
as a Non-CSEM - than a low number of false posi-
tives, i.e., Non-CSEM file name wrongly categorized
as a CSEM. Therefore, we need to pay more atten-
tion to the recall of the CSEM class rather than the
Non-CSEM class.
Recall metric for a class is calculated as the total
number of samples correctly classified for that class
(the True Positives TP), over the total number of sam-
ples of that class (the True Positives TP and the False
Negatives FN). Equation (1) shows how Recall is es-
timated for a given class.
Recall =
T P
T P + FN
. (1)
Nevertheless, the precision of a classifier is also a cru-
cial factor in measuring its performance, as it shows
the proportion of samples that were correctly identi-
fied. Class precision is calculated as a ratio of cor-
rectly classified file names of that class (the True Pos-
itives TP) to the total number of predicted positive
samples of that class (the True Positives TP and the
False Positives FP), and it is given in Equation (2).
Precision =
T P
T P + FP
. (2)
Finally, the F1 score of a class summarizes the two
before mentioned metrics as it refers to the harmonic
File Name Classification Approach to Identify Child Sexual Abuse
231

mean of the precision and recall and it is calculated
following to Equation (3).
F1 =
2 ∗ (Recall ∗ Precision)
(Recall + Precision)
. (3)
Additionally, it has been proved that the accuracy
metric is not reliable when the dataset is imbalanced
(Chen et al., 2017), as in our case, where the majority
of the samples are Non-CSEM file names. An alter-
native metric is to use average class accuracy, which
refers to the average recall of the CSEM and the Non-
CSEM classes, rather than using overall dataset level
accuracy.
5.3 Empirical Results
In this section, we compared the performance of the
four pipelines described in Section 3 in the dataset
of CSEM. Table 4 shows that both CNN models out-
weigh the machine learning classifier. Furthermore,
by comparing the average class recall of both CNN
models, we observe that the model of Zhang et al.
has obtained an average class recall of 0.86, while
the model of Kim et al. has scored 0.84. Also, by
comparing the performance on class level, we can see
that the model of Zhang et al. has the best recall rate
for the CSEM class of 0.78, while the model of n-
grams with SVM classifier has the best recall rate for
the class Non-CSEM of 0.98. It is noteworthy that
the SVM classifier obtained the lowest performance
in terms of the F1 score and the average class recall
metrics.
Table 4: A comparison between four algorithms to classify
file names. The values in bold refer to the best result ob-
tained.
Method Name Category Precision Recall F1
N-gram
LR
Non-CSEM 0.95 0.97 0.96
CSEM 0.78 0.65 0.71
Average 0.86 0.81 0.84
N-gram
SVM
Non-CSEM 0.94 0.98 0.96
CSEM 0.81 0.61 0.70
Average 0.88 0.79 0.83
Zhang et al.
model
Non-CSEM 0.96 0.95 0.96
CSEM 0.71 0.78 0.74
Average 0.84 0.86 0.85
Kim et al.
model
Non-CSEM 0.95 0.97 0.96
CSEM 0.78 0.70 0.74
Average 0.86 0.84 0.85
In addition to the analyzed measures, the required
time to predict the legality of a file name plays a vi-
tal role when police forces investigate a hard disk of a
suspect. Therefore, we compare the aforementioned
classification methods in terms of the time needed to
predict the category of one million file names. We
carried out this experiment on a consumer-level com-
puter with 16GB RAM, an Intel Core i7 processor and
a GPU Nvidia GeForce GTX 1060. Regarding the
CNN-based methods, we set the batch size to 10, 000
examples and we tried to predict the file names twice,
once using the GPU and another using the CPU.
While for the machine learning methods, we exam-
ined them on the CPU only. Table 5 shows that the
logistic regression classifier along with char n-gram
for text representation achieves the highest prediction
speed using the CPU. Meanwhile, the CNN-based
models were enormously time-consuming. However,
when the GPU is used, the model of (Zhang et al.,
2015) surpasses the other proposals in terms of the
prediction time of 59 seconds.
Table 5: A comparison between four algorithms in terms
of the required time (in seconds) to predict one million file
names using a GPU or a CPU processor. The values in bold
refer to the best prediction time.
Method Name CPU GPU
N-gram LR 106 -
N-gram SVM 852 -
Zhang et al. model
1022 59
Kim et al. model
1172 62
6 CONCLUSIONS AND FUTURE
WORK
In this paper, we explored two machine learning clas-
sifiers (Support Vector Machine and Logistic Re-
gression), versus two Convolutional Neural Network
(CNN) models to classify file names related to Child
Sexual Exploitation Material (CSEM). We compared
these models on a dataset of 65, 351 samples dis-
tributed over two classes (64, 857 Non-CSEM and
9, 330 CSEM file names) concerning the average class
recall metric.
Our results strengthen the superiority of CNN over
regular machine learning classifiers to categorize file
names. Incorporating the CNN model by Zhang et al.,
we were able to identify CSEM file names with a re-
call rate of 0.78, and an average class recall of 0.86.
Also, we demonstrated how the adapted CNN model
outperforms the model by Kim et al. (Kim et al.,
2016) and two supervised machine learning classifiers
(Support Vector Machine and Logistic Regression).
The presented results were conducted on a dataset
extracted from the file names only. However, there
is still further work to be investigated in the future
during our ongoing research. In particular, we are
planning to include orthographic features extracted
from file names, as it showed promising performance
ICPRAM 2020 - 9th International Conference on Pattern Recognition Applications and Methods
232

on other NLP tasks, such as Named Entity Recogni-
tion (NER) (Aguilar et al., 2017). Furthermore, the
presented work did not investigate paths of the files,
which could be pivotal evidence when the file name
is meaningless, such as a file with a name made up of
numbers or random characters (e.g., kf3kfk3985.png).
Also, the metadata of the file, such as its header, size,
extension, could provide further clues to predict its
class correctly.
The assessment of transformer-based models,
such as BERT (Luo et al., 2018), RoBERTa (Liu et al.,
2019), and XLNet (Yang et al., 2019) for text classifi-
cation is part of our immediate future research, as they
have shown promising results on various NLP tasks.
ACKNOWLEDGEMENTS
This research has been funded with support from the
European Commission under the 4NSEEK project
with Grant Agreement 821966. This publication re-
flects the views only of the author, and the Euro-
pean Commission cannot be held responsible for any
use which may be made of the information contained
therein.
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