Threat Detection

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An Integrated Imbalanced Learning and Deep Neural Network Model for Insider
Threat Detection
Article in International Journal of Advanced Computer Science and Applications · January 2021
DOI: 10.14569/IJACSA.2021.0120166
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(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 12, No. 1, 2021
An Integrated Imbalanced Learning and Deep Neural
Network Model for Insider Threat Detection
Mohammed Nasser Al-Mhiqani1,
Rabiah Ahmed
2*, Z Zainal Abidin3
Center for Advanced Computing Technology
Faculty of Information Communication Technology
Universiti Teknikal Malaysia Melaka
Melaka, Malaysia
Isnin, S.N4
Faculty of Technology Management and Technopreneurship
Center for Technopreneurship Development CTeD
Universiti Teknikal Malaysia Melaka
Melaka, Malaysia
Abstract—The insider threat is a vital security problem
concern in both the private and public sectors. A lot of approaches
available for detecting and mitigating insider threats. However,
the implementation of an effective system for insider threats
detection is still a challenging task. In previous work, the Machine
Learning (ML) technique was proposed in the insider threats
detection domain since it has a promising solution for a better
detection mechanism. Nonetheless, the (ML) techniques could
be biased and less accurate when the dataset used is hugely
imbalanced. Therefore, in this article, an integrated insider
threat detection is named (AD-DNN), which is an integration
of adaptive synthetic technique (ADASYN) sampling approach
and deep neural network technique (DNN). In the proposed
model (AD-DNN), the adaptive synthetic (ADASYN) is used to
solve the imbalanced data issue and the deep neural network
(DNN) for insider threat detection. The proposed model uses
the CERT dataset for the evaluation process. The experimental
results show that the proposed integrated model improves the
overall detection performance of insider threats. A significant
impact on the accuracy performance brings a better solution in
the proposed model compared with the current insider threats
detection system.
KeywordsSecurity; insider threat; insider threats detection;
machine learning; deep learning; imbalanced data
I. INTRODUCTION
Information systems are facing a security challenge, which
comes from outside or inside of an organization. The outside
security challenge involves malware and cyber-attack penetrating the network from remote sites. The inside security issue
comes from the “trusted” employee within the organization.
In which this issue involves both a behavioral and a technical
nature [1][2]. Insider threat is commonly known as a problem
of utmost importance for information system security management [3].
The malicious insider threat has been defined in the technical report [4] by Cappelli mentioned “a current or former
employee, contractor or business partner who has or had authorized access to an organization’s network, system, or data and
intentionally exceeded or misused that access in a manner that
negatively affected the confidentiality, integrity, or availability
of the organization’s information or information systems”.
The insider threat activity was conducted by the intentional
insiders; such as sabotage of information system, classified
information disclosure and theft of intellectual property, or by
an unintentional insider, such as losing external devices that
contain sensitive information about the organization. Unlike
the tasks of the traditional intrusion detection, several insider
threat detection challenges come from the nature of the insider
where the insider has the authorization to access the computer
systems of the organization and has more knowledge about
the security levels of the organization [5][6]. Cybersecurity
reports show that 63% think insider attacks have become more
frequent in the past 12 months. In a recent survey, 53% of the
responders believe that detecting insider attacks has become
significantly to somewhat harder [7].
The detection of the insider threat is very difficult task; this
is because of many challenges. Firstly, as security mechanisms
of an organization are not mainly designed for the people who
are already inside the organization’s network, this brings a
chance for the motivated malicious insider with authorized
access to carry out the malicious actions without triggering
alerts. Secondly, majority of the attacks initiated by insider
are carried out in several phases over a long time. For this
reason, effective detection systems for insider threat have to
be designed with consideration of long-term monitoring and
wide audit data sources range [8][9].
Despite the good performance demonstrated by the current
insider threat detection approaches, the traditional machine
learning techniques are not able to utilize all the data of
user behavior because of the complexity, high-dimensionality,
sparsity, and heterogeneity of the data. ML algorithms normally assuming that the used data are balanced in their nature.
However, imbalanced data usually produce high accuracy in
detecting the majority class, while the accuracy of the minority
class is very low. This type of result is not suitable in the
situation of insider threats, where the minority class is the
important in detection [10][11].
Hence, to deal with the abovementioned challenge, this
article proposes an integrated insider threat detection model,
called (AD-DNN), which is based on adaptive synthetic sampling approach (ADASYN) and deep neural network (DNN).
The proposed of AD-DNN model contains two main parts.
Firstly, the ADASYN oversamples the low-frequency samples
of insider threats adaptively for increasing these samples,
which will lead in helping the machine learning classifiers to
learn the low-frequency insider threats attack samples characteristics. Secondly, The DNN is used to classify the samples to
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normal or malicious insider based on the generated new dataset
from the first stag. To evaluate the AD-DNN performance,
an experiment is conducted on the CERT 4.2 insider threats
dataset [12].
The rest of this paper is organized as follows. The related works is discussed in Section 2. Section 3 presents
the methodology. Section 4 discuss the Implementation and
Results. Finally, Section 5 concludes the work.
II. R
ELATED WORK
The importance of machine learning in the domain of
insider threats is growing [13]. In several earlier researches,
the use of machine learning algorithms has been used to build
a classifier that can identify threats from insiders [14][15].
A significant work have been done for the propose of
insider threats detection. The Hidden Markov Model (HMM)
is used by Wang et al. in [16] to develop an insider threat
detection approach. The HMM modeled the normal users’
behavior to identify any abnormal behaviors which may differ
from the normal behaviors. By utilizing the HMM in modeling
the insider threats, the states number of HMM have an high
impact on the effectiveness of the method. When the number
of states increases the HMM computational cost increases.
ML algorithms have a high powerful ability in improving
the insider threats detection performance and self-adaptive
capabilities in handling the environment changes of insider
threat. Nevertheless, these techniques of ML are still influenced
from the effect of imbalanced data in the insider threats domain
as well as the lack of in depth knowledge of the insider’s
behavior patterns [17].
Parveen et al. in [18] utilized the use of one-class support
vector machine (OCSVM) technique to model the time series
of the daily log, that conceptualizes the insider threat detection
issue as a stream mining problem.
Lin et al. [19] proposed a hybrid insider threat detection
model using the CERT dataset. The Deep Belief Network
(DBN) and OCSVM have been used to build the insider threats
detection model. Firstly, the unsupervised DBN is applied to
extract the raw data hidden features. And then, the OCSVM
is applied for the training of the model utilizing the extracted
features.
In recent years, DNN and RNN techniques are widely
used in the development of the detection systems of insider
threat, Tuor et al. [20] proposed an online unsupervised
deep learning approach based on DNN and RNNs to detect
anomalous insider activities in real-time from the system logs.
Their approach is containing three main parts, firstly the
feature extractor, secondly the batcher/dispatcher, and finally
the number of Recurrent Neural Networks (RNNs) or DNNs.
Long short-term memory (LSTM) techniques have been used
to model the user behaviors either alone or in combination
with other techniques, Yuan et al. [14] applied the LSTM
and Convolutional Neural Network (CNN) based model on
user behavior to model the normal users behavior and detect
anomalous user behavior. They have dealt with user activities
like the natural language modeling. Similar with the previous
work, Zhang et al. in [17] employed the LSTM for modeling
the log activity of the insider and treat these activities same
like the natural language sequences, the proposed solution is
worked by extracting the features and detecting the malicious
activities when the patterns of the log differ from the training
samples. The proposed model evaluation was carried out
on a small group of users, only eight users were selected
randomly from the CERT experimental dataset. Another work
by Sharma et al. [21] also utilized LSTM based Autoencoder
using the similar concept to the previous work which models
the user behavior using session activities and therefore detect
the abnormal data points.
A great efforts have been made by the researchers in the
previous literature, however, we believe that there are still
way to improve the insider threats detection performance by
considering the issue of imbalanced data, and deal with the
issue before proceeding the classification task.
III. M
ETHODOLOGY
In this part, the basic concepts and methodology components of the proposed AD-DNN model is discussed as shown
in Fig. 1.
Fig. 1. Proposed Model.
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A. Dataset
In this article the CERT r4.2 dataset is used to evaluate
the proposed model this is due to the fact that this dataset
contains several types of users’ event activities, including
logon/logoff, device, email, HTTP and files which capture the
activities of 1000 employees in an organization for the period
of 17 months. Additionally, CERT r4.2 have more instances of
malicious insider compared to the other CERT datasets version.
The dataset contains 32,770,222 event records generated by
the1000 normal and anomalous users. 7323 of the generated
activities are malicious insider instances that were manually
injected by experts, representing 3 different scenarios of insider
threat. The dataset is divided into two sets: the first subsets is
used for training and second subsets for testing. 80% of the
datasets is used to train the proposed model, the remaining
20% is utilized for the evaluation of the model performance.
B. Log Aggregator and Parser
Firstly, the process of log aggregation starts with the
collection of all insider data activity from multiple applications
to the main-storage in order to prepare it for the processing
task. After the combination of this data done by log parser, it
can be saved as a new master dataset. Secondly, to make the
data is compatible with machine learning algorithms the log
parsing or the parsing engine is created. As the CERT data that
has been aggregated in the first stage is mostly in text strings
format, which is not readable by the DNN algorithms that we
are applying here, the aggregated data need to be transformed
to the applicable formats. To transform the data for our model
the MaxAbsScaler is used to scales the data between the [-1,1]
range automatically based on the absolute maximum.
C. AD-DNN
The idea of sampling methods is either increasing or
decreasing the number of samples in the evaluation dataset.
The oversampling approach increases the records’ frequency,
which is a lower sample while under-sampling decreases the
records’ frequency, which is in a higher sample.
In this article, the oversampling method is used, since
the focus on the insider threats, where the minority class is
the important in detection, the method used called ADASYN.
ADASYN approach is an algorithm that generates synthetic
data, the ADASYN main idea is to use a weighted distribution
for different examples of minority class according to their
difficulty level in learning, the more synthetic data is mainly
generated for the examples of minority class which is difficult
to learn when it is compared to the other examples of minority
classes that are easy to learn [22].
The ADASYN firstly calculates the minority class’ Knearest neighbors of every record in the sample class. Moreover, it draws a line between the neighbors and newly generated random points on that line. Then, it adds some small
values randomly on the new point, which makes them similar
to the real point. Therefore, these added sample points have
more variance than the samples that are taken from their parent
samples.
Deep learning (DL): is another machine learning techniques that is based on the learning concept of multi-level
representations. The DL creates a hierarchy of features where
the lower the level is defining the higher levels and the features
of the lower the level helps features are defined at a higher
level. The structure of DL is extending the traditional neural
networks where more hidden layers are added to the network
architecture between the two layers of input and output for
modeling the nonlinear and complex relationships. In recent
years, this area of research has gained the concern of the
researchers due to its great performance for becoming one of
the best solutions in many problems. Many DL architectures
are existing nowadays, currently, one of the common DL architectures is the convolutional neural networks (CNN), which
can carry out complex tasks by using convolution filters. A
CNN architecture is a feed-forward layers sequence where the
convolutional filters and pooling layers are implemented. CNN
adopts many fully-connected layers after the final pooling
layer, which work on converting the previous layers 2D feature
maps to 1D vector for the classification process. Despite the
advantages of the CNN architecture where the feature extraction process is not required before the CNN being applied but
the process of CNN training from scratch difficult and timeconsuming because it requires large labeled dataset samples to
build and train the model before it is prepared for classification.
DNN is another type of DL architecture, which is widely
utilized and succeeded in both regression and classification in
various areas. DNN is a typical feed-forward network where
the input flows to the output layer from the input layer using
two or more hidden layers. Fig. 2 present the architecture of
DNN.
Fig. 2. DNN Architecture.
Fig. 2 shows the typical DNNs architecture where Ni is
representing the input layer containing neurons for the input
features, the Nh illustrates the hidden layers, and the No is the
output layer classes.
IV. I
MPLEMENTATION AND RESULTS
We have implemented the proposed system on Python with
Tensorflow as the backend. The experiment environment is a
Ubuntu 18.04.5 LTS operating system runs on a machine with
an NVIDIA 1660Ti GPU on a 3.7GHz Intel Core i7-8700HQ,
16GB RAM.
Model Parameters: The proposed model parameters
in this article includes the following: (a) The hidden layer of
the DNN network, learning rate, number of epochs, and batch
size. (b) The ADASYN algorithm oversampling rate and the
number of nearest neighbors. We tuned our model with 20
hidden layers, 1e-3 learning rate, 50 epochs, 1024 * 16 batch
size, and the Adam optimizer is used.
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Metrics: To evaluate the proposed model performance,
the parameters used are the average accuracy, average, average
false positive rate, average F-Score, average true-negative
rate and average false-negative rate. The performance of the
proposed model was compared with other classifiers using the
same parameter measurements.

Accuracy =
T N + TP + F N + FP
(1)

T N + TP
F
score = 2: P recision:Recall
P recision
+ Recall (2)
FPR = FP
FP
+ T N
(3)
T NR = T N
T N
+ FP
(4)
F NR = F N
F N
+ TP
(5)
where TP (True Positive), TN (True Negative), FN (False
Negative), and FP (False Positive). Additionally, to consider
the problem of class imbalance where the insider attacks often
carried out by the malicious insiders during the normal work
time, which scatters the abnormal insider behavior in large
amount of normal employees’ behavior, we use the Area
Under-Curve (AUC) measurement for evaluating the proposed
model. The AD-DN produces a better result compared to the
other single classifiers, as shown in Fig. 3 the best result that
the AD-DNN gets is AUC = 95%.
Fig. 3. AD-DNN AUC
Fig. 4. AD-DNN Accuracy
Fig. 4 presents the average accuracy of the proposed ADDNN, which shows the accuracy versus the number of epochs.
It plots the training and testing performances. As shown in
the figure, the proposed AD-DNN obtain good accuracy with
average of 96% and there is no major problems indicated with
the model since the training and testing curves are very similar
to each other and there is no possibility of overfitting.
Fig. 5. AD-DNN Loss
Fig. 5 presents the loss of the training and testing in every
epoch. In this experiment, the model was stopped after 50
epochs when there is no high testing loss was seen between
successive iterations.
Finally, in this article, the designed AD-DNN model is
compared with three common methods machine learning
techniques (SVM, DNN and LSTM), which have been used
in the field of insider threats. The Scikit-Learn library has
been implemented to execute three techniques. Additionally,
for evaluating the effectiveness of the proposed model using
all evaluation matrices, the AD-DNN is compared with some
of the recent works as shown in Table I.
TABLE I. COMPARISON SUMMARY OF PROPOSED MODEL

Model Accura
cy
F
Score
AU
C
FPR FNR TNR
SVM 70% 60% 44% 23.8% 89.10% 76%
LSTM 75% 30% 68% 23.6% 40% 76%
DNN 86% 48% 80% 12.9% 27% 87%
OCSVM
based on
DBN[19]
87.79% 12.18%
LSTM
Autoenco
der[21]
90.17% 95% 9.84% 91%
AD-DNN 96% 95% 95% 4% 5% 96%

On comparative analysis of the well-known classifiers and
some of the recent works on detection of the insider threat
using the CERT v4.2 dataset, AD-DNN produces a good and
promising results. Table I shows that AD-DNN gives the
highest accuracy with 96% and the highest F-score, AUC and
TNR with 95%, 95% and 96% respectively. Additionally, the
AD-DNN achieves the least false rate with 4% FPR and 5%
FNR only. It can be seen that AD-DNN is superior to other
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methods in almost all the evaluation metric, for example
the DNN without ADASYN that gives 86% accuracy, 48%
F-score, 80% AUC,87% FNR, 12.9% FPR and 27% FNR.
This is because AD-DNN consider and solve the imbalance
data problem before start training the classifier, and our
method can effectively improve the performance of detection.
V. C
ONCLUSION
In this article, an integrated insider threat detection model
is introduced called as AD-DNN for solving the current
challenges in the insider threat detection constructed by employing the theory of machine learning. Firstly, the ADASYN
algorithm is used to solve the imbalanced data problem in
the situation of insider threats, where the minority class is
important in detection. Then, the DNN classifier is designed
as the anomaly insider threat detection. The results of the
experimental on the CERT dataset shows that the ADASYN
algorithm solves the machine-learning algorithms imbalanced
the fitting trend of the low-frequency and high-frequency
insider data and improves the detection accuracy of the lowfrequency insider attack by generating fewer new samples.
Furthermore, compared with other recent research works and
machine learning techniques used for insider threats detection,
the proposed AD-DNN makes the insider threats detection
obtains superior and satisfactory results in all the evaluation
metrics.
A
CKNOWLEDGMENT
Thank you to Research Group of Information Security
Forensics and Computer Networking, Center for Advanced
Computing Technology (C-ACT), Fakulti Teknologi Maklumat
dan Komunikasi, Universiti Teknikal Malaysia Melaka
(UTeM). This project is funded by the Ministry of Higher
Education Malaysia under the Transdisciplinary Research
Grant Scheme (TRGS) with project Number TRGS/1/2016/
UTEM/01/3. Its reference is TRGS/1/2016/FTMKCACT/01/D00006.
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