DDoS Attack Detection on Internet o Things using Unsupervised Algorithms

International Journal of Fuzzy Logic Systems (IJFLS) Vol.11, No.2/3/4, October 2021
DOI : 10.5121/ijfls.2021.11401 1
DDOS ATTACK DETECTION ON INTERNET OF
THINGS USING UNSUPERVISED ALGORITHMS
Hailye Tekleselase
Department of Information Systems, School of Informatics,
Addis Ababa University, Addis Ababa, Ethiopia
ABSTRACT
The increase in the deployment of IoT networks has improved productivity of humans and organisations.
However, IoT networks are increasingly becoming platforms for launching DDoS attacks due to inherent
weaker security and resource-constrained nature of IoT devices. This paper focusses on detecting DDoS
attack in IoT networks by classifying incoming network packets on the transport layer as either
“Suspicious” or “Benign” using unsupervised machine learning algorithms. In this work, two deep
learning algorithms and two clustering algorithms were independently trained for mitigating DDoS
attacks. We lay emphasis on exploitation based DDOS attacks which include TCP SYN-Flood attacks and
UDP-Lag attacks. We use Mirai, BASHLITE and CICDDoS2019 dataset in training the algorithms during
the experimentation phase. The accuracy score and normalized-mutual-information score are used to
quantify the classification performance of the four algorithms. Our results show that the autoencoder
performed overall best with the highest accuracy across all the datasets.
KEYWORDS
Distributed denial of Service (DDoS), Internet of Things (IoT), Machine Learning Algorithms,
Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Security.
1. INTRODUCTION
The increment of sensors and computing devices have made life easy and convenient for us due
to the fast and accurate computation of our information. However, increased integration and
deployment of connected devices also exposes essential resources to DDoS threats [1].
In 2016, the Mirai attack that destroyed many popular websites really exposed the weakness of
IoT devices [2]. Over 100,000 inadequately secured player, cameras, digital video recording and
other IoT devices were turned into botnets for starting an extraordinary Terabytes per seconds
(Tbps). DDoS attack through the Mirai. The Mirai source code that was further released resulted
in frequent additional IoT attacks. With the magnitude of attacks that have been launched,
securing IoT devices is a problem as host-centric IT security solutions cannot be totally relied
upon because most manufacturers appliances place more priority on functionality and cost over
security.
Besides, unlike servers that can undergo software update, IoT software is hardly or never
updated, hence making them more vulnerable to attackers. In view of these security problems and
resource-constrained nature of IoT devices, greater focus should be placed on packet security
within the IoT network.

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Traditional network-centred security has relied on predefined signature or system models for
known threats [3]. Recently, there has been a rising awareness in machine learning (ML) to
network security. However, many ML solutions use supervised learning i.e. they build attack
classifiers by training on known anomalies [4], which makes them ineffective against new
threats. The main aim of this work to determine the performance of unsupervised learning
algorithms in accurately classifying network packets as either benign or malicious. We achieve
this by training the algorithms on modern DDOS datasets and performing rigorous testing while
benchmarking the performance of the algorithms using standard performance metrics.
The rest of the paper is organized as follows: Section 2 presentsrelated works; Section 3
elaborates the data set and describes the methodology followed in the research; Section 4 details
the experimentation procedures, the result gotten and the observations from the results while
Section 5 presents the conclusion of the work as well as highlighting the future work.
2. RELATED WORK
According to [5]detection systems of network intrusion have traditionally been rule-based.
Nevertheless, machine learning and statistical approaches have also made major contributions
[5]. Machine learning have also proven to be effective in two main aspects of network security
which are: feature engineering (i.e. the ability to extract the most important features from
network data to assist model learning) [6] and classification. In security environment,
classification tasks usually involve training both suspicious and benign data in order to create
models that can detect known attacks [7].
The authors in [8] pointed out that steps such as collection of network information, feature
extraction and analysis, and classification detection provide a means for building efficient
software-based tools that can detect anomalies such as software-defined networking (SDN).
Another study [9] provides a thorough classification of DDoS attacks in terms of detection
technology. The study also emphasizes how the characteristics of the network security of an SDN
defines the possible approaches to setting up a defense against DDoS attacks. Similarly, [10]have
explored this area too. In other approaches to DDoS defense, [4] propose a scheduling based
SDN controller architecture to effectively limit attacks and protect networks in DoS attacks.
The growth of cloud computing and IOT has inevitably led to the migration of denial-of-service
attacks on cloud computing devices as well. Thus, cloud computing devices must implement
efficient DDOS detection systems in order to avoid loss of control and breach of security[11].
Studies such as [12] aim to tackle this problem by determining the source of a DDOS attack using
PTrace (powerful trace) source control methods. PTrace controlled such attack sources from two
aspects, packet filtering and malware tracing, to prevent the cloud from becoming a tool for
DDoS attacks. Other studies such as [13] approach the problem of filtering by using a set of
security services called filter trees. In the study, XML and HTTP based DDOS attacks are filtered
out using five filters for detection and resolution. Detection based on classification has also been
proposed and a classifier system for detection against DDOS TCP flooding attacks was created
[14]. These classifiers work by taking in an incoming packet as input and then classifying the
packet as either suspicious or otherwise. The nature of an IP network is often susceptible to
changes such as the flow rate on the network and in order to deal with such changes, self-learning
systems have been proposed that learn to detect and adapt to such changes in the network [15].
Many of the existing models for DDoS detection have primarily focused on SYN-flood attacks
and haven’t been trained to detect botnet attributes. More studies are thus needed where models
are trained to detect botnet as botnet becomes the main technology for DDoS organization and
execution [16]. Botnet DDoS attacks infect multitude of remote systems turning them to zombie

3
nodes that are then used for distributed attacks. In detecting botnet DDoS attacks, authors in
[17]used a deep learning algorithm to detect TCP, UDP and ICMP DDoS attacks. They also
distinguished real traffic from DDoS attacks, and conducted in-depth training on the algorithm by
using real cases generated by existing popular DDoS tools and DDoS attack modes.Also,
[18]proposed a DDoS attack model and demonstrated that by modelling different allocation
strategies. The proposed DDoS attack model is applied to game planning strategies and can
simulate different botnet attack characteristics.
According to [19] “DDoS detection approaches can operate in one of the following three modes:
supervised, semi-supervised and unsupervised mode. For the detection approach in supervised
mode, it requires a trained dataset (or a classifier) to detect the anomalies, where the trained
dataset includes input variables and output classes. The trained dataset is used to get the hidden
functions and predict the class of input variables (incoming traffic instances). This mode is
similar to a predictive model. For example, Classification techniques comes under the category of
supervised data mining” [20]. “For the Approaches that work in the semi-supervised mode,
they have incomplete training data i.e. training data is only meant for normal class and some
targets are missing for anomaly class” [21]. Unlike supervised and semi-supervised learning,
unsupervised machine learning algorithms do not have any input-output pairs but the algorithm is
trained such that it can accurately determine the unknown data point. The following subsections
further discusses the unsupervised learning algorithms we used in this work.
current effort that goes to detect IoT based attacks proposed MQTT transaction-based features
Moustafa et al. (2019). But the authors used features based on the
TCP protocol analysis, which do not provide sufficient information on the MQTT
protocolparameters. In contrast, our proposed UDP featuresare based on unsupervised machine
learning which can successfully detect and distinguish such attacks including the unknown
attacks.
2.1. Autoencoder
According to[6], Autoencoder can be defined as a neural network that detect an identity function
that can recreate the input data with high accuracy by using back propagation. The Autoencoder
has two main parts (cf. Figure 1), an encoder that compresses the input into a dimensional latent
subspace that is lower and a decoder that recreate the input from this latent subspace. The
encoder and the decoder, can be defined as transitions ϕ and ψ such that:
ϕ: X → F
ψ: F → X
ϕ, ψ = argmin ϕ, ψ‖ X − ( ψ ∘ ϕ ) X ‖2
(1)
Figure 1. A generic representation of the autoencoder

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Autoencoder have been suggested for feature engineering in the network security environment,
e.g. to learn important features of malware classification[22]and also for detecting DDOS attacks
on application layer[23]. The linear analogue of Autoencoder is the principal component analysis
(PCA) which is based on statistical techniques.
2.2. Restricted Boltzmann Machines (RBM)
The authors in[24]stated that Boltzmann machine (BM) is a bidirectionally connected network of
stochastic processing units. BMs are commonly used to learn important features of an unknown
probability distribution based on samples from the distribution. However, the training process of
the BM is usually computationally intensive and tedious. The restricted Boltzmann machine
attempts to solve the training problem of BMs by imposing key restrictions on the architecture of
the BM. The BM is a fully connected network of bidirectional nodes where each node is
connected to every other node. The RBM on the other hand is presented as a relatively smaller
network of bidirectional nodes with the restriction that nodes on the same layer are not connected
to each other horizontally[24].
The restricted Boltzmann machine is a generative model that is used to sample and generate
instances from a learned probability distribution. Given the training data, the goal of the RBM is
to learn the probability distribution that best fits the training data. The RBM consists of 𝒎 visible
units 𝐕 = (𝐕1, 𝐕2 … . , 𝐕𝐦) and 𝒏 hidden units 𝐇 = (𝐇1, 𝐇2 … . , 𝐇𝐧) arranged in two layers.
Figure 2. Restricted Boltzmann machine showing the visible and hidden units
The visible units lie on the first layer and represent the features in the training data (see Figure 2).
Usually, one visible unit will represent one feature in an example in the training data. The hidden
units model and represent the relationship between the features in the training data. The random
variables (𝐕, 𝐇)take on values (𝐯, 𝐡) ∈ [𝟎,𝟏]𝐦
for continuous variables and the underlying
probability distribution in the training data is given by the Gibbs distribution 𝑝(𝐯, 𝐡) =
1/𝐳−𝐄(𝐯,𝐡)
with the energy function in equation 1;
𝐄(𝐯, 𝐡) = − ∑ ∑ 𝐰ij𝐡i𝐯j
𝐧
𝐦
𝐧
𝐦
− ∑ 𝐛j𝐯j
𝐦
𝐣=𝟏
− ∑ 𝐜i𝐡i
𝐧
𝐢=𝟏
(𝟐)
In equation 2, 𝐰ij are real valued weights associated with 𝐯j and 𝐡i, and 𝐛jand 𝐜i are real valued
bias terms associated with units 𝐢 and 𝐣 respectively. The contrastive divergence learning
algorithm is one of the successful training algorithms used to approximate the log-likelihood
energy gradient and perform gradient ascent to maximize the likelihood [24]. After a successful
training, the RBM should be able to represent the underlying probability distribution of the

5
training data and when presented with unseen examples, the RBM should be able to generate
similar representations to the example provided.
2.3. K-Means
The K-means algorithm takes the full dataset consisting of multiclass datapoints, then clusters the
datapoints into separate clusters to the best of its ability; this classification occurs when you feed
in the input and the model assigns the input into one of the computed cluster. Given a set of
observations (𝑥1, 𝑥2, 𝑥3, ..., 𝑥𝑛), where each observation is a d-dimensional real vector, k-means
clustering aims to partition the n observations into k (≤ n) sets S = {𝑠1, 𝑠2, ..., 𝑠𝑘} so as to
minimize the within-cluster sum of squares (WCSS) (i.e. variance).
2.4. Expectation-Maximization (EM)
The authors in [25]stated that EM algorithm is used for solving mixture models that assume the
existence of some unobserved data. Mathematically, the EM algorithm can be described as
follows; given the statistical model that generates a set of observed data 𝑿, latent data 𝒁,
unknown parameters𝜽 and the likelihood function 𝐿(𝜽;𝑿) = 𝑝(𝑿, 𝒁| 𝜽), the maximum
likelihood of the unknown data 𝜽is determined by maximizing the marginal log-likelihood of the
observed data 𝑿using equation 3:
𝐿(𝜽; 𝑿) = 𝑝(𝑿| 𝜽) = ∫ 𝑝(𝑿,𝒁| 𝜽)𝑑𝑍 (3)
The expectation and maximization steps are defined as follows:
𝑄(𝜽 |𝜽(𝒕)) = 𝑬𝒁|𝑿,𝜽(𝒕)[log 𝐿(𝜽,𝑿, 𝒁)] (4)
𝜽𝒕+𝟏
= 𝑎𝑟𝑔 𝑚𝑎𝑥𝜽Q(𝜽|𝜽(𝒕)
) (5)
In the expectation step, the likelihood of the unknown parameters is computed as the log-
likelihood of the known parameter estimates, while in equation 4 the maximization step is used to
select the new value that maximizes the log-likelihood given the estimates from equation 5.
In our research, we differ from current work (e.g.[26]) in two ways. First, we work with
unsupervised machine learning methods using both normal and suspicious network data to train.
Second, we made use of dimension reduction methods such as K-means clustering with PCA,
Expectation maximization, Restricted Boltzmann Machine and Autoencoder (where K-means and
EM are both trained using normal and suspicious data; RBM and AE was trained on only
suspicious data), all these methods were not only for feature engineering[22] but for classification
as well.
3. METHODOLOGY
A DDoS attack temporarily or indefinitely constraints the availability of a network resource to its
intended users. The challenge then for the network administrator is to deploy DDoS detection
systems that are capable of analyzing incoming packets to the transport layer. These detection
systems may then determine if these incoming packets are suspicious or benign. In the following
subsections, we present the design methodology for a DDoS detection system that makes use of
unsupervised machine learning algorithms. The problem is therefore to design and train four
efficient unsupervised machine learning systems that are capable of detecting a DDOS attack on
the transport layer.

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3.1. The Datasets
In order to train the unsupervised machine learning algorithms used in this study, we sourced for
modern DDOS attack datasets including the following datasets.
1. The first dataset is the DDOS evaluation dataset (CICDDoS2019) [27]. The full dataset
consists of both reflection and exploitation based DDOS attacks in the form of both
suspicious and benign network packets. The dataset is further grouped into TCP and UDP
based attacks.
2. The second dataset is the Mirai dataset created by [28]. Mirai is specific type of botnet
malware that overrides networked Linux devices and successfully turns them into bots
used for distributed attacks such as DDOS. The Mirai dataset consists of 80,000 SYN-
flood instances and 65,000 UDP-lag attacks on a security camera IOT device.
3. Finally, the third dataset is the BASHLITE botnet attack dataset on a webcam IOT device
and is also provided by [28]. Similar to Mirai, BASHLITE is a botnet malware for
distributed attacks on networked devices. The BASHLITE dataset consists of 110,000
SYN-flood instances and 100,000 UDP-lag attacks. Both Mirai and BASHLITE are
open-source malware that can be used for academic research purposes.
3.2. Data Pre-Processing
The dataset largely consists of numerical values, so the pre-processing steps are minimal. The
most important pre-processing action taken was to normalize the values in the dataset using the
standard minmax normalization expressed below in equation 6.
𝑥′
=
𝑥−𝑚𝑖𝑛(𝑥)
𝑚𝑎𝑥(𝑥)−𝑚𝑖𝑛(𝑥)
(6)
3.3. Selected Unsupervised Learning Algorithms
The selected unsupervised machine learning algorithms employed in this work are explained
below.
3.3.1. Autoencoder
The autoencoder is made up of two feed forward neural networks that mirror each other in terms
of the number of layers and the number of nodes. Recall that the goal of the autoencoder model is
to learn the latent space of an encoded vector that can be used to efficiently reconstruct the input
vector. Thus, the goal of the encoder is to encode the input vector to a lower dimensional vector
space [3]. The goal of the decoder is to reconstruct this input vector from the lower dimensional
encoded vector provided by the encoder. Figure 3 shows the architecture for our autoencoder
model, one can see that the input vector is structured with respect to the 77 features from the
CICDDoS2019 dataset. We simply increase this number to 115 when working with the Mirai and
BASHLITE datasets, all other parameters stay the same.

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Figure 3. The Autoencoder architecture
Each neuron in the hidden layers of the encoder and decoder make use of the Rectified Linear
Unit (ReLu) activation function. The hyperparameters selected for the autoencoder model are
outlined as follows;
 Batch size: A batch size of 2048 is selected.
 Number of epochs: An epoch number ranging between 10-20 is initialized with 70
showing to be the most optimal as we shall see from the results.
 Loss: The mean squared error loss function is used.
 Optimizer: The Adaptive (ADAM) algorithm is selected. ADAM is the state-of-the-art
optimizer for deep neural networks (Schneider et al, 2019).
 Betas: These are ADAM optimizer coefficients used for computing running averages of
gradient and its square (0.5, 0.999).
 Learning rate (0.0002).
3.3.2. Restricted Boltzmann Machine
The RBM used for this project is a two-layer RBM with an architecture as described in section
2.2 and figure 2. The dataset consists of continuous variables scaled between 0 and 1 so therefore
we model the RBM as a continuous variable model with the hidden units and visible units taking
on values between (𝐯, 𝐡) ∈ [𝟎,𝟏]𝐦
where m is number of visible units. Similar to the autoencoder
algorithm, we use the reconstruction error to define the classification task for the RBM. The
parameters selected for the RBM include:
 Number of units: We always set the number of hidden and visible units to be the same
according to the number of features present in the training data. That is, for instance for
the CICDDoS2019, the number of hidden and visible units for the RBM is 77.
 Training algorithm: The k-step contrastive divergence algorithm with Gibbs sampling
is used for training the algorithm with k = 10.
 Training Epoch: An epoch of 10 is selected, experimental results show that increasing
the epoch beyond 10 does not improve training results.

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3.3.3. K-Means
The K-Means clustering algorithm has relatively fewer parameters to select. The default “pure”
version of the K-means algorithm is used as opposed to variants such as Elkan’s K-Means [29]
where triangle inequality is used. The parameters for the algorithm are outlined as follows;
 Number of clusters: This is the number of clusters as well as the number of centroids to
generate. Two (2) clusters are required since we will be attempting to classify and thus
cluster suspicious or benign samples.
 Number of initializations: This is the number of times the algorithm will be run with
different centroid seeds. We have the option of 20 initializations depending on the
performance.
 Precompute distance: Here the algorithm precomputes the distance between sample
points in the feature space. Enabling this parameter allows the algorithm to train faster.
3.3.4. Expectation-Maximization
The expectation-maximization algorithm is setup with similar parameters as the k-means
algorithm and in fact the software implementation of the two algorithms in the sci-kit learn
machine learning framework borrow from each other. However, the core implementations are
different and the parameters for the expectation maximization algorithm include:
 Number of components: This is the number of clusters to be estimated and is set to two
because of the binary classification task of suspicious or benign.
 Number of iterations: The number of iterations is like the epoch of the autoencoder
where they both define the number of training iterations to run the algorithm. A default
value of 300 is used.
 Covariance type: The covariance parameter defines the structure of the covariance
matrix with respect to each component or cluster. The “full” covariance is chosen where
each cluster has its own covariance matrix and has been shown to achieve the best
results.
3.4. Evaluating Model Performance
3.4.1. Accuracy
In machine learning parlance, the task of the determining whether or not an incoming packet is
suspicious or benign is known as classification. For the K-Means and EM algorithm, the
clustering of a feature point together with highly similar feature points is in itself, a form of
classification. We can evaluate these models by quantifying how accurate their classification of a
packet is with respect to its clusters. A simple net accuracy score of the predicted class compared
to the actual class gives us an empirical quantification of the model’s performance.
For the autoencoder and Boltzmann model, this classification task is non-trivial. This is because,
these algorithms are not clustering algorithm like the K-Means model and neither do they output
a single value classification like the feed forward neural network with one output neuron. We
approach the problem from the viewpoint of the properties of the autoencoder and restricted
Boltzmann machine[30] (Bhatia et al, 2019). Since the optimal performance of the deep learning
models is such that the reconstructed output vector must be very similar to the original input
vector (see Figure 3.3), the autoencoder will have learnt to reconstruct a very specific domain of
feature points. We can define a reconstruction error that describes the difference between the

9
reconstructed output and original input vector. The reconstruction error is defined as the mean
squared error (MSE) in equation 7 as follows:
𝑀𝑆𝐸 =
1
𝑛
∑ (𝒚𝑖 − 𝒚𝒊
̂ )2
𝑛
𝑖=1 (7)
Where 𝒚𝒊 is the original input vector and 𝒚𝒊
̂ is the reconstructed output vector. The mean squared
error is computed over all the output of the model. Ideally, it is preferable to have a mean squared
error close to zero. However, depending on the size of the values in the predicted output, a mean
squared error within the range of 2-5 decimal places is acceptable. Representing the
reconstruction error as the mean squared error allows one to know when the model is presented
with an input that is very far off from what was contained in the training set. Thus, if for instance
the autoencoderis trained on a dataset comprising only of benign packets, whenever a benign
packet is presented to the autoencoder, we expect that the reconstructed output should be quite
similar and therefore the reconstruction error should be low. However, if this same model is
presented with a suspicious packet that is fairly different from the features of benign packet then
we should expect the reconstruction error to be quite high. The same logic can be applied to the
restricted Boltzmann machine
With this formulation established, it is easier to frame the classification problem using the
autoencoder and RBM. Where in our example, a low reconstruction error means the packet is
benign, while a high reconstruction error means the packet is suspicious. Using these predictions,
we can then compute the accuracy much like we did with the K-Means model. The accuracy
score simply calculates a ratio of the number of correctly classified packets over the incorrectly
classified packets.
3.4.2. Normalized Mutual Information (NMI)
The NMI provides a means of evaluating the clustering performance of the algorithm by
comparing the correlation between the predicted class and the target class. If the predicted and
target data are represented as two separate distributions, then we can also apply the NMI to
determine performance of non-clustering algorithms. Therefore, we can apply the NMI metric to
the classification performance of the autoencoder and RBM too. The Normalized mutual
information is scaled between 0 and 1. Where 1 represents perfect correlation and 0 otherwise. In
general, a higher NMI above 0 is preferable. The exact interpretations of the NMI alone may not
be sufficient to quantify the model’s performance that is why the concept of accuracy is also
reviewed. This is because the NMI is a statistical property that describes correlation between two
distributions [31]. A high NMI does not necessarily translate to a higher accuracy and vice versa.
Thus, the two metrics should be regarded independently.
4. EXPERIMENTAL RESULTS
In this session, we present the experimental results for each model across all datasets. The results
are presented in subsections, with each subsection dedicated to a model. For the Autoencoder and
Restricted Boltzmann Machine, their subsections consist of plots showing the training and test
loss, a table summarizing the performance across the datasets and a detailed discussion of the
results. For the rest of the models, they do not optimize a loss function and so only the summary
tables and a detailed discussion of the results were presented. Performance evaluations are also
carried out using the accuracy and Normalized Mutual score. The innovation of this work lies in
the exact detection of the anomality behaviour of the nodes. DDoS attacker tried to affect
network in its different forms. The basic nature of DDoS attacker is to flood the network with a
large number of packets and then exhaust the network resources. Mainly DDoS attacker attacks

10
on an individual cloud user as they have limited resources. But it can be possible to detect DDoS
attacker and then prevent the network from data loss. ThisANN-SVM hybrid approach enhanced
the classification efficiency of the proposed system in identifying thecyber-attacks.
Tables 5 and 6 also show the summary of all the results in the different models.
4.1. Comparison Result and Analysis
The efficiency of planned attack detection system is also assessed against five existing works of
[Velliangiriet al., (2020)], [Almseidin et al., (2017)], [Yan Naung Soe,2020] (NaeemFirdous
Syed,2020) and [Alsharafat et al., (2013)] who have addressed intrusion detection against DDoS
attack using KDD dataset. compares the detection accuracy ofthe proposed work against the
existing works. Recently, [Velliangiri et al., (2020)] had proposed Taylor ElephantHerd
Optimisation based Deep Belief Network to detect DDoS attacks and attained a classification
accuracy ofonly 83%. Further, [Almseidin et al., (2017)] had realised Random forest classifier to
attain a detection accuracy of 93.77% while [Alsharafat et al., (2013)] work comprising of
eXtended Classifier System with Artificial Neural Network (ANN-XCS) demonstrated a
detection accuracy of 98.1% against the proposed work that have implemented the optimization
techniques with unsupervised machine learning to achieve a detection accuracy of 99.9%.
This paper focusses on detecting DDoS attack in IoT networks by classifying incoming network
packets on the transport layer as either “Suspicious” or “Benign” using unsupervised machine
learning algorithms. In this work, two deep learning algorithms and two clustering algorithms
were independently trained for mitigating DDoS attacks. We lay emphasis on exploitation based
DDOS attacks which include TCP SYN-Flood attacks and UDP-Lag attacks. We use Mirai,
BASHLITE and CICDDoS2019 dataset in training the algorithms during the experimentation
phase.
4.2. Autoencoder Training and Test Results
Figure 4 shows the desired behaviour of the backpropagation training algorithm where the
training and validation loss decrease steadily and in unison as the training epoch increases. It is
important to point out that the autoencode ris trained to reconstruct SYN-Flood data, meaning it
should be unable to reconstruct benign data. We chose the SYN-Flood data for training because
there were more instances than the benign data. The same choice is made for the UDP-
Autoencoder model, where we train it on the UDP-Lag data instead of on benign UDP data.
(a)

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(b)
Figure 4. Autoencoder training and validation loss on the CICDDoS2019 SYN-Flood (a) and UDP-Lag (b).
(a)
(b)
Figure 5. Autoencoder training and validation loss on the Mirai SYN-flood (a) and UDP-Lag data (b)
In Figure 5a and 5b, the training and validation loss of the Autoencoder model on the MiraiSYN
and UDP-Lag data reduces steadily as the training epochs increase. It is clear that by epoch 10,
the loss starts to converge quickly to zero. Therefore 20 is the sufficient number of training
epochs in order to avoid overfitting.

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(a)
(b)
Figure 6. Autoencoder training and validation loss on the BASHLITE SYN-flood
(a) and UDP-Lag data (b)
The training and validation loss for the BASHLITE loss shown in Figure 6 is less steep than that of the
Mirai loss. Table 1 shows the accuracy of the autoencoder model across all datasets, here we can see that
the model has the highest accuracy on the BASHLITE dataset hence the reason why the loss is less steep
and flattens out quickly by epoch 10 and 14 respectively.
Table 1. Test Accuracy and Normalized Mutual Information score for the autoencoder models on the SYN-
Flood and UDP-Lag across all datasets.
Data Accuracy (%) NMI
CICDDoS2019 SYN-Flood 0.8945 0.5363
CICDDoS2019 UDP-Lag 0.8617 0.4216
Mirai SYN-Flood 0.9744 0.6211
Mirai UDP-Lag 0.9621 0.5733
BASHLITE SYN-Flood 0.9933 0.9927
BASHLITE UDP-Lag 0.9921 0.9822
In table 1, we present the accuracy and NMI scores for the autoencoder model. These scores were
determined based on the formulation described in section 3.4. The result show that the
autoencoder model performs best on the BASHLITE SYN-Flood data with a higher accuracy of
99%. In general, the autoencoder performs better on the Mirai and BASHLITE datasets than that
of the CICDDoS2019 dataset. Does this mean the model is better suited to detect botnet attacks?
We suspect that this is due to the feature selection process for the datasets, the Mirai and
BASHLITE datasets show less variance across the features when compared to the CICDDoS2019

13
dataset. The NMI scores show higher correlations where the accuracy is higher, indicating a
correlation between the target predictions and predicted value.
4.3. Restricted Boltzmann Machine
The restricted Boltzmann machine performed poorly on the CICDDoS2019 dataset but the
training process evolved smoothly with the loss dropping as the epoch increased.
Table 2. Test Accuracy and Normalized Mutual Information score for the Restricted Boltzmann machine
model on the SYN-Flood and UDP-Lag across all datasets.
CICDDoS2019 UDP-Lag 0.5089 0.1103
The RBMs performance on the BASHLITE dataset is similar toits performance on the Miraidata,
still, the overall performance is much lower than that of the Autoencoder. The results indicate
that the RBM is less suited for the kind of precise reconstruction of the continuous input value
that is easily achieved by the autoencoder. The stochastic property of the RBMs hidden units
makes it difficult to accurately reconstruct the continuous input from a large unknown latent
space. The autoencoder solves this problem by first encoding the input vector into a lower
dimensional space thus reducing the dimensionality of the latent space, making the resampling
process more accurate and less computationally intensive. The NMI across the RBMs
performance is low, showing poor correlation between the target and predicted value.
4.4. K-Means Training and Test Results
The K-Means algorithm is not a gradient based learner so we cannot bother ourselves with
iterative plots such as those presented for the autoencoder and RBM models. Also, the K-Means
algorithm is trained on a distribution that contains a mixture of both suspicious and benign
features. The model’s accuracy and NMI are shown in Table 3.
Table 3. Test Accuracy and Normalized Mutual Information score for the K-Means model on the SYN-
Flood and UDP-Lag training and validation data
CICDDoS2019 UDP-Lag 0.7139 0.1427
From the results, we observe that the K-Means model performs relatively poorer as compared to
the autoencoder model but it performs better on average whencompared to RBM. However, and
once again, one can see some slight disparity in performance on the SYN-Flood data compared to
UDP-Lag data owing to the variance amongst the features in these datasets.
The NMI scores for the K-Means model are relatively low too with well below average
correlations. Although one should interpret the accuracy and NMI scores independently, the low

14
NMI scores for the K-Means discourages one from being too optimistic about the model’s
performance.
4.5. Expectation-Maximization Training and Test Results
The Expectation-Maximization algorithm performs better than the k-means algorithm on average
with its highest accuracy being 80% on the Mirai UDP-lag data (Table 4).
Table 4. Test Accuracy and Normalized Mutual Information score for the EM model on the SYN-Flood
and UDP-Lag training and validation data
CICDDoS2019 UDP-Lag 0.6759 0.1446
Although, the EM algorithm performs better than the k-means algorithm the performance is still
poor compared to the autoencoder. The clustering algorithms struggle with large high-
dimensional and continuous data. The maximization step in the EM algorithm gives it an edge
over the k-means algorithm in this aspect. Where the k-means algorithm struggles to compute a
centroid from high dimensional continuous data, with low variance as is the case with Mirai and
BASHLITE, the EM algorithm models the problem probabilistically instead, optimizing for the
log-likelihood of the latent variables.
Tables 5 and 6 below summarize the accuracy and NMI scores across all datasets for all models,
highlighting the autoencoder’s superiority for machine learning task.
Table 5. Summary of the Accuracies across all datasets and all models
Dataset/Model Autoencoder Restricted
Boltzmann
machine
K-Means Expectation-
Maximization
CICDDoS2019 SYN-
Flood
0.8945 0.5651 0.7538 0.7096
CICDDoS2019 UDP-
Lag
0.8617 0.5089 0.7139 0.6759
Mirai SYN-Flood 0.9744 0.6067 0.7636 0.7030
Mirai UDP-Lag 0.9621 0.7797 0.7478 0.8051
BASHLITE SYN-
Flood
0.9933 0.6709 0.6451 0.7636
BASHLITE UDP-
Lag
0.9921 0.6210 0.6823 0.7575

15
Table 6. Summary of the Normalized Mutual Information score across all datasets and all models.
Dataset/Model Autoencoder Restricted
Boltzmann
machine
K-Means Expectation-
Maximization
CICDDoS2019
SYN-Flood
0.5363 0.1919 0.1949 0.1144
CICDDoS2019
UDP-Lag
0.4216 0.1103 0.1427 0.1446
Mirai SYN-Flood 0.6211 0.1639 0.0912 0.2771
Mirai UDP-Lag 0.5733 0.3895 0.1387 0.2901
BASHLITE SYN-
Flood
0.9927 0.2506 0.1059 0.3074
BASHLITE UDP-
Lag
0.9822 0.1007 0.1306 0.2678
5. CONCLUSIONS
The unsupervised machine learning models have been developed and trained on both SYN-Flood
and UDP-Lag DDOS datasets. The training and test results both show that the deep learning
autoencoder model performs better in the classification of incoming packets as suspicious or
benign. Over the past decade, deep learning algorithms have established themselves as the state-
of-the-art machine learning algorithms. Our results show that in the unsupervised machine
learning space, the deep learning algorithm also outperforms traditional clustering algorithms
such as the K-Means and Expectation-Maximization algorithm as well as other generative deep
learning models such as the Restricted Boltzmann machine. However, when comparing
unsupervised machine learning algorithms, one must be careful to formalize the performance
evaluation problem properly. The project shows that it is possible to frame the autoencoder model
as a classification algorithm using the value of the reconstruction error and that it is possible to
apply this formulation efficiently to difficult problem domains such as network packet analysis.
Once proper formulations are established, the accuracy score can then be used to evaluate both
models fairly. Although the autoencoder model is clearly the superior model, the DDOS-
Detection class we developed provides methods that allow one to perform network packet
classification using either the autoencoder model or the Expectation-Maximization model. The
simulation results show that the DDOS-Detection tool built around these models can achieve a
net accuracy of as high as 99%. Future studies should aim to replicate results in a larger system to
detect compromised end-points and also ensure that algorithms are current by possible retraining
approaches to handle abnormalities in network performance.
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AUTHOR
Hailye Tekleselase Michael holds a BSc degree in Information Systems from University of Gondar, and
MSc degree in Information Systems from Addis Ababa University. His current research interests are:
Cyber Security, Big Data, AI, ICT and Mobile Computing. He currently Lecturer at WolaitaSodo
University, He is a member of the Ethiopian Space Science Society (ESSS) and the Institution of Electrical
and Electronics Engineers (IEEE)

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