Vol.5 for REFEREED SCIENTIFIC AND ACADEMIC JOURNAL PUBLISHED BY CENTER OF RESEARCH, STUDIES

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REFEREED SCIENTIFIC AND ACADEMIC JOURNAL PUBLISHED BY CENTER OF RESEARCH, STUDIES AND PUBLICATIONS AL-KUT UNIVERSITY COLLEGE FOR SCIENTIFIC AND APPLIED SPECIALIZATIONS Chairman of Board-President of the Foundation Commission Dr. Talib Zedan Al Musawi Address: Iraq Wasit Province P.O. Box: 46137, Iraq

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Chief in Editor

Managing Editor

Assist. Prof. Dr. Ahlam Hussien Al Musawi

Assist. Prof. Dr. Natiq Abdullah Ali

Editorial Board Prof. Dr. Sharov Vadim Ivanovich

Russia - Biology

Prof. Dr. Marie M. El-Ajaily

Libya - Banghazi Uni. - Chemists

Prof. Dr. Omar Shhab Hamad Al-Obaidi

Iraq - Chemists

Prof. Dr. Nabil Mohie El-Deen Abdel-Hamid

Egypt - Kafr Al-Sheikh Uni. - Pharmacy

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Lebanon - Lebanon Uni.

Iraq - President of the A. Ch.S. Chapter of Iraq - Chemists

Prof. Dr. Taghreed Hashim Al-Noor Prof. Dr. Mohammed Saleh Mahdi

Iraq - Laser Eng.

Prof. Dr. Mohammed Abdulwahhab Munshid

Iraq - Laser Eng.

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Iraq - Al-Kut Univ. College - Pharmacy

Prof. Dr. Adawih Jemaah Hiader

Iraq - Laser Phys.

Prof. Dr. Nassir Mohammed Fahad

Iraq - Al-Kut Univ. College

Prof. Dr. Hafid mohammed dhuyab

Iraq - Al-Kut Univ. College - Dentistry

Prof. Dr. Ali hussain rishak

U.K. - Derm Uni. - Physics

Assist. Prof. Dr. Nadiah Hashim Al-Noor Dr. Talib Zedan Al-Musawi

Iraq- Al-Mustansiriya Uni. – Mathematical Statistics Iraq - Al-Kut Univ. College - Laser Phys

Dr. Rabi Nori

Iraq - Al-Kut Univ. College - Laser Phys

Dr. Zaid Muslim

Iraq - Al-Kut Univ. College

Dr. Nagham Thamir Ali

Iraq - Ministry of Science & Technology - Laser Phys

Linguistic Evaluation

Prof. Dr. Fakher Jaber Mater – Arabic Language

Assist. Prof. Dr. Hasson Hashim Abbass – English Language

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‫مجلة كلية الكوت الجامعة‬

Vol. 5

Issue 1

2020

5th Year

Papers in English

No: Title and the Name of Researchers

Pages

1

Determining of Robust Factors for Detecting IoT Attacks

2

Measuring and Analyzing the Impact of Net Exports on Economic Growth in Iraq:

Rawaa Ismael Farhan, Dr.Nidaa Flaih Hassan and Dr. Abeer Tariq Maolood

A study for the Period of 2000-2018

Dr.Sami Awad, Dr.Ahmed Abdulrazaq Abdulrudha and Dr.Rasha Kahled

VI

1-19

20-28

Rawaa, Dr. Nidaa and Dr. Abeer – Determining of Robust …

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K.U.C.J

Vol. 5 Issue 1 2020 5th Year

Pages 1-19

‫مجلة كلية الكوت الجامعة‬

Vol. 5

Issue 1

2020

5th Year

Determining of Robust Factors for Detecting IoT Attacks

Rawaa Ismael Farhan

Dr. Nidaa Flaih Hassan

Dr. Abeer Tariq Maolood

Computer Science Collage

Computer Science Collage

Computer Science Collage

University of Technology

University of Technology

University of Technology

ralrikabi@uowasit.edu.iq

nidaaalalousi5@yahoo.com

110032@uotechnology.edu.iq

Abstract The detection of novel intrusion types is the target of cyber security, therefore best secured network is become very necessary. The Network Intrusion Detection Systems (NIDS) must address the real-time data, since security attacks are expected to be increased substantially in the future with the Internet of Things (IoT).

Intrusion detection approaches in this time, which depends on matching patterns of packet header information have decreased their effectiveness. This paper is focused on anomaly-based intrusion detection system, where NIDS detects normal and malicious behavior by analyzing network traffic, this analysis has the potential to detect novel attacks. Robust factors are used for evaluating these attacks by covering previous researches, these factors are: "high accuracy rate", "high detection rate"(DR) and "low false alarm report"(FAR), these factors influence on NIDS performance.

Keywords: Internet of Things (IoT), Intrusion Detection System (IDS), Deep learning (DL), Machine learning (ML).

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‫خالصــة‬ ‫حاث بأت من الضاااي تو ي فايافضااا شااانآم ةم من ان اع كم كشااال الت ااا‬ ‫ب ااانب ي قعأت ف ديأال الاجكأت ايم ام بشاااآ كنايف الك ااات ن‬

‫يعد اكتشاااأن اع ال الت ااا الجديدة هدفًأ لألمن ال اااانياع‬

‫) يجب ان يتعأم مع الناأعأت ف ال قت الفع‬NIDS ‫عنيالشااانآم‬ ‫)ن‬IoT ‫بأستخدام إعتيعت ايشاأء‬

‫ف هذا ال قت الت يعتكدع ى مطأب م اعكأط مع مأت تاس الحزمم ق ت فأع اتاأن ييكزهذل ال تقمع ى ع أم كشل‬

‫ا سألاب ك شل الت‬

‫هذاالتح ا لديه ال دتة‬

‫( ال ا ا العأاو الضااأتمن خالل يح ا حيكم مي تالشاانآم‬NIDS) ‫الت ا ال أ ع ع ى الحأ ت الشااأ ة حاث‬

‫ معدل اقم عألام‬: ‫هذل‬

‫ع ى اكتشااااااأن الاجكأت الجديدةن ي ااااااتخدم ع ام ق يم لت ااع هذل الاجكأت من خالل ي طام ايبحأ ال ااااااأب م‬ ‫ن‬NIDS ‫معدل اكتشأن مييفع ي يييإعذاتخأطئ م خفض يؤثيهذل الع ام ع ى اااء‬

1. Introduction

allows to physical devices to be connected to each other over the Internet [1], Figure 1 depicts

Internet of Things (IoT) is a revolutional

the main architecture of it.

development in Internet and communication, that

Application layer

Network layer

IDS

Perception layer Figure 1. Architecture of IoT [1].

Future of the IoT in the integration with the

The potential unauthorized access to information

Artificial intelligence (AI), both are making

and new attacks will increase by 2020, when up

human life more comfortable, since they made

to 50 billion devices may be connected according

everything smart and there is no need for human

to Gartner. Weaknesses in Internet protocols and

intervention [2].

the loss of sufficiently robust mathematical analysis methods have led to increased attacks as systems are adopted in IoT [3].

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Security is considered the main challenge of IoT,

to the lack of rich dataset [4]. Typically, the

and Real –time data generated from IOT need

traffic of network is picked up in both packet and

analysis. Deep learning which improves neural

stream format, traffic of network is typically

networks is considered the best for analyzing

picked up at the packet level by copying ports on

real-time solutions in (IoT), it mimics the human

network

mind

information of payload, stream -based data is

for

its

ability

to

self-learn

from

accumulated experiences.

devices,

and

its

data

contains

contained metadata of network connections only [5].

In this paper, many researches are discussed with their challenges, in addition, a research agenda is

Firewalls and authentication methods are used to

proposed to address these challenges and

protect and prevent unauthorized access to the

highlighted the robust factors in the detection of

systems, but these methods are lost the abilities

IoT attacks.

to monitor the network traffic, where most of the

2. Network

Intrusion

Detection

attacks are existing. These attacks may be created

System

by disgruntled employees who have legitimate

(NIDS)

network access then used the privilege to destruct Network Intrusion detection systems (NIDS) are

[6]. Figure 2 depicts the location of Intrusion

the first line of defense in the network, its often

Detection System in the network after firewall.

suffer from practical testing and evaluation due

Figure 2. Intrusion Detection System [6].

There are two types of IDSs, these two types are

1. Anomaly-Based Detection: ID Search network

classified according to the detection technique,

traffic to detect abnormal traffic.

they are: 3

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2.

Misuse

Detection

Or

Signature-Based

Figure 3 presents a general classification of an

Detection: It is a method that uses unauthorized

Intrusion

Detection System

behavior as known patterns that are called

Implementation

signatures to detect similar attempts [7].

Detection method [8].

method,

According

Architecture

to and

Figure 3. General Classification of Intrusion Detection System [8]

In

this

paper,

anomaly-based

NIDS

is

consist of streams with labeled anomalies, benchmark containing real-world data [10].

considered, because it is able to detect new threats which occur in IoT. The NIDS analyzed

Training and evaluating anomaly-based NIDS

network traffic and detected new and unknown

are used Labeled data sets. As in [11,12] datasets

attacks. The feature set design important to

for (NIDS) network based intrusion detection are

identify network traffic, and it is an ongoing

surveid, which presented in details with network

research problem [9].

data based on packet and flow, thesis researches

Restrictions of application systems are required

has presented 15 different

to process data in real- time, not batches. The

evaluating data sets for specific situations, the

nature of the stream data shows deviation,

presentation identified sources for network-

favoring algorithms that learn ongoing, by using

based data, like repositories of traffic with traffic

Numenta Anomaly Benchmark (NAB) which

generators.

4

properties for

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3. Techniques to Design NIDS

methods, they developed two architectures that

In this section, number of machine learning

can detect and track progress of attacks in real-

techniques are discussed, these techniques are

time, database of HMM templates is developed,

consider as most common Machine Learning

which

Techniques (ML) , such as Decision Tree (DT),

complexity. The classifier is applied by using

Support Vector Machine (SVM), Bayesian

decision tree with "Feature grouping based on

Algorithm, K-Nearest Neighbor and Principal

linear

Component Analysis (PCA).In addition, Deep

algorithm and "cuttle fish algorithm" (CFA) is

Learning (DL) are also described such as(Auto

used in [16].

Encoder(AE),Variant Auto Encoder(VAE),Deep

To deal with heterogeneous and large-scale data,

Belief Networks (DBN), Convolutional neural

[17] proposed hybrid approach for intrusion

network (CNN), Recurrent

detection by using dimensionality reduction

neural network

(RNN), Long –Short term Recurrent

presented diverse

correlation

performance

coefficient"

and

(FGLCC)

neural

technique integrated with "information gain"

network (LSTM),Bi directional Recurrent neural

(IG) method and "principal component analysis"

network (BRNN), Gated Recurrent Units (GRU)

(PCA), classifier is ensemble by Applying

and Generative Adversarial Network (GAN)) .

"Support Vector Machine"(SVM), "Instance-

Finally, other NIDS techniques such as Data

based

mining

"Multilayer Perceptron"(MLP).

and Swarm intelligence

are also

learning

algorithms"

(IBK),

and

described.

B. Deep learning Techniques (DL)

A. Machine Learning Techniques (ML)

Deep learning is artificial neural networks with

Embedded intelligence in the IoT devices and

multi-layers to produce best accuracy in many

networks are able to be supplemented by ML and

domains such as object detection, language

DL techniques to manage many security

translation and speech recognition, in this section

problems. In [13] some of recent ML/DL

recent researches are reviewed focusing on using

techniques with IoT are reviewed from security

DL.

point view. Due to big data challenges facing

As with [18] DL differs from classical ML

Intrusion Detection, [14] provided feature

methods due to the ability to self-learn from data

selection with high classification efficiency, this

without need to human's knowledge or coded

selection is educed computational costs. In [15]

commands, thus it could understand from raw

Hidden Markov Models (HMM) is used, which

data such as text, image and video because its

is one of statistical machine learning (ML)

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flexible architectures, while DL is provided with

experiments showing the feasibility of the deep

more data, their predictive accuracy is increased.

learning in network traffic analysis.

Deep learning models can enhance performance

In [25] understanding how to use deep learning

of IDS as presented in [19], also all the IDS

are declared by overview some IDSs which

associated definitions are provided, with an

adopted deep learning approaches executed in

explanation of different IDS types, where

intrusion

detection module are puted and the used

advantages and disadvantages.

approach. According to [20] a sparse auto-

In [26,27,28], Modelling network traffic used

encoder and softmax regression based NIDS was

"long short-term memory" (LSTM) recurrent

implemented, they used benchmark network

neural networks as supervised learning method,

intrusion dataset - NSL-KDD to evaluate

used known normal and abnormal behavior,

anomaly detection accuracy.

improved intrusion detection.

In [21] SDN has given a potential to make strong

In [29], the Paper proposed a hybrid model, this

secured network and also made a dangerous

hybrid is composed from Recurrent Neural

increasing

Network (RNN) with Restricted Boltzmann

in

attacks

chances,

with

the

detection, with their

explanation of potential of using DL for anomaly

Machines

detection system based on flow. A survey about

malicious traffic detection as a classification task

IoT architecture presented in [22], emerging

without feature engineering.

security vulnerabilities with their relation to the

In [30] a method is suggested based on CNN to

layers of the IoT architecture are also presented.

execute intrusion detection, using CNN leads to

In [23] declared that deep learning techniques has

extract complex

the ability to handle big data. Big data and

continually changing environments, which is so

obtaining data reflected real challenges to IDS

necessary in network intrusion detection.

based on machine learning. It showed some IDSs

In [31] improved user trust by making the DNN-

limitations which used old machine methods

IDS more communicative, since the black-box

used to construct, extract and select features. To

nature of DNNs inhibits transparency of the

get rid these challenges, it showed some IDSs

DNN-IDS, which is essential for building trust.

with deep learning techniques.

The user declared input features which are most

An overview of the recent work of deep learning

relevant in detecting every type of intrusion by

techniques with network anomaly detection is

training DNN-IDS.

provided in [24], it also discussed their local

As in [32] described a new IDS called the "hierarchical

6

(RBM).

This

hybrid

limitations,

regarded

features automatically

spatial-temporal

in

features-based

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intrusion detection system" (HAST-IDS). Firstly,

used to monitor traffic of network in real time

the traffic of network represented spatial features

and events in host-level effectively, this SHIA is

in

alert probable cyber-attacks.

low-level

which

learned

using

deep

"convolutional neural networks" (CNNs) then temporal features in high-level is learned using

C. Other (NIDS) Techniques

"long short-term memory" (LSTM).

Other techniques such as swarm intelligence,

[33] More deep learning approaches have been

data mining techniques and genetic algorithms

used for IDS, three models are evaluated on their

are used for designing NIDS, the following

accuracy and precision, a "vanilla deep neural net"

(DNN),

"Self-Taught

section is described most recent researches:

Learning"(STL)

In [38] Swarm intelligence has been combined

approach, and "Recurrent Neural Network"

with data mining techniques to configure strong

(RNN) based "Long Short Term Memory"

methods for detecting and identifying data flow

(LSTM).

efficiently. Since, Networks of IoT have been

[34] Proposed a new deep learning technique

secured

within the youth network for detecting attacks

ways

and

attacks of cyber, therefore detection based on

Recurrent Neural Network" (BLSTM RNN).

anomaly bear the liability to decrease risk of

[35] This paper proposed optimization on

attacks types. [39] The proposed work, used

structure of DBN’s network, at first " Particle

firefly algorithm for feature selection. The

Swarm Optimization" (PSO) is designed used

resulted features are submitted to the classifier

learning factor and adaptive inertia weight. Then

then provided C4.5 and "Bayesian Networks"

the fish swarm behavior provided to develop the

(BN) for attack classification. Paper [40] showed

PSO and found the optimization solution

an intrusion detection model based on Deep

initially.

Belief Network improved by Genetic Algorithm

[36] A proposed system used Deep Learning

into multiple iterations of the GA, have faced

technique which applied a combination fusion of

various types of attacks. This paper [41]

Random Forest (RF) Algorithm and Decision

suggested a fuzzy aggregation method used the

Tree (DT) Classifiers, which reduced irrelevant

deep belief networks (DBNs) and modified

features and detected attacks with a better

density peak clustering algorithm (MDPCA).

accuracy.

MDPCA is used to divide the training set into

In [37] It proposed hybrid framework of DNN "Scale-Hybrid-IDS-AlertNet"

authentication

encryption ways, but they are not secured versus

using "Bi-directional Long Short-Term Memory

called

using

various subsets to reduce the size and imbalanced

(SHIA)

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Pages 1-19

Construction ……

samples with similar sets of attributes. Each

detection,

with

a

simplification

to

the

subset trained on its sub-DBNs classifier. [42]

collaboration between protocols used in IoT.

This paper showed a hybrid method for an

[47] Presented an online anomaly learning using

anomaly network-based IDS by using AdaBoost

the reversible-jump MCMC learning, forecasting

algorithms and Artificial Bee Colony (ABC), this

mechanism, then Network Utility Maximization

hybrid method gained a low false positive rate

(NUM) theory is used for structural analysis of it.

(FPR) and high detection rate (DR).

[48] Risk Analysis and examined the security threats for each layer, related to this process,

4. Intrusion Detection System (IDS) and IoT

suitable procedures and their limitations of IoT

The

protocols are specified.

implementation

of

classical

IDS

technologies on the IoT environment showed

In paper [49] a new model for intrusion detection

obvious complexity, due to the nature of

is suggested, which is used Principal Component

resources constrained in IoT devices and their

Analysis (PCA) to reduce dataset dimensions

use of special protocols. Attackers exploit the big

from a great number of features to a small

IoT potential to develop methods to threaten

number, also online machine learning algorithm

privacy and security [43].

is used as a classifier.

In [44] presented a complete study of current

According to [50] determined that current data

intrusion detection systems, according to three

sets (KDD99 and NSLKDD) do not provide

factors: cost of computation, consumption of

acceptable results, because of three main issues,

energy and privacy. [45] Based on accurate

it lost the modern attack patterns, it lost modern

analysis of the existing intrusion detection

scenarios of traffic streams and distributed sets of

methods. The paper divided into two parts: part

training and testing is difficult. Therefore,

one contained algorithm of mining anomaly to

UNSW-NB15 dataset has been generated to

detect anomalous data in perception layer, which

address these issues.

the second part contained a distributed scheme of

In [51] the system uses a structured Self-

intrusion detection of the detected anomalies.

Organizing Maps (SOM) to classify real-time

The great dynamic distribution in IoT made an

Ethernet network data. [52] Proposed that

online manner of anomaly detection so difficult,

"variant-gated recurrent units" are learned packet

thus in [46] proposed a new IDS, which is used

payload with header features of network

ML algorithms for detection anomaly in IoT , the

automatically, E-GRU and E-BinGRU are new

platform provided "security as a service " for

techniques never used for network intrusion detection previously. The E-BinGRU reduces the

8

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Construction ……

required size of memory and used bit-wise

because increasing threats on banking services

calculations in most arithmetic operations.

and financial sectors. The proposed system uses the latest IDS Dataset in 2018 which isa real time

5. NIDS on IoT using Deep Learning

dataset (CSE-CIC-IDS2018), created by the Canadian Institute for Cyber Security (CIC) on

The improvement in CPU work and neural

the environment

network algorithms made the application of DL

Services). As in [57] show a new deep learning

more practical. The use of DL for attack

technique within the youth network for detecting

detection in the IOT could be a flexible to novel

attacks using" Bi-directional Long Short-Term

attacks due to of its capability of feature

Memory Recurrent Neural Network" (BLSTM

extraction in high-level. [53] showed that

RNN). In [58] light-weight distributed security

centralized detection system is assessed versus

solution is presented to improve IoT architecture,

the distributed attack detection based IoT/Fog.

analyzing the approaches of ML and DL on the

The experiments proved that distributed attack

IoT and

detection system is better than centralized

evaluated according to Accuracy, Detection Rate

data set of German credit card, Data set of 12

(DR), False Alarm Rate (FAR).

months taken from Temperature sensor, Images of persons walkways).

According to [55] a new detection framework is presented using simulation for proving its scalability and real-network traffic for proving the concept. The detection options provided and

evaluating

explain the approach used on the data set and

the resulted data from IoT are bulk. It used UCI

service"

and

Comparative analysis of existing NIDS for IOT

problems based on a home automated systems,

a

Security,

architecture of IDS dataset. Table1. Show the

[54] this paper aims to solve some smart city

as

Cyber

Networks (LSTM and GRU) for each layer in the

detection systems using deep learning model. In

"security

of AWS (Amazon Web

simplifies

interoperability between IoT protocols.

In [56] suggested system is created by applying artificial intelligence on a Detect botnet attacks

9

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Construction ……

TABLE (1): Comparative analysis of existing NIDS for IOT Ref.

Approach

Descriptive Concepts

Dataset

Accuracy

DR

FAR

25

LSTM

Network trained with 8 features

DARPA/

NA

0.993

0.072

and all attacks with the lowest

KDDCup'99

NA

DOS

MSE on test data.

27

RNN

This reference Usedrecurrent

NSL-KDD

neural network

83.49

2.06

R2L 0.80

24.69

U2R 0.07

11.50

Prop

50

23

DT

Four existing classifiers are

LR

used to evaluate the complexity

UNSW-B15

85.56

2.16

83.40

NA

15.78

83.15

18.48

NB

82.07

18.56

ANN

81.34

21.13

1.AK16a

1.use SAE for classifying and

"Aegean Wi-

(ANN)

clustering approaches.

2.AK16b

65.178

0.143

Fi Intrusion

92.674

2.500

2. Adopted a feature selection

Dataset "

92.180

4.400

(Softmax

by ANN.

(AWID)

99.918

0.012

Regression)

3. SAE extractions and

22.008

0.021

NA

NA

3.AK17

(K-

NA

weighted selection are

means

combined.

Clustering)

4. SAE improved the IDS

4.ACTYK17

performance than to KKSG15.

(SVM, DT, ANN) 5.KKSG15 24

Fully

Train+/Test+

NSL-KDD

connected

Train20/Test+

R2L 90.4

Train+/Test−

U2R 83.0

10

DOS 89.4

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Construction ……

neural network

Train20/Test−

Prob 84.2

model(FCN)

49

(PCA)&k-

This reference used Principal

Nearest

Component Analysis (PCA) to

KDDCup 99

84.406

99.312

1.116

97.618

NA

0.0257

kDDCUP99

0.97

0.9

0.99

ISCX 2012

1.IG-PCA0.988

0.011

0.986

0.011

0.987

0.014

reduce the dimensions of the datasetand to develop the classifier.Softmax regression and k-nearest applied 58

(LSTM and

Improveits architecture and

DARPA/KD

GRU)

proposed a light-weighted and

DCup '99

multi-layered design of an IoT network 9

ANN

Backpropagation algorithmis used.

17

IG-PCA

Proposed a new hybrid

Ensemble

technique for dimensionality

SVM 98.82

method

reduction that

2.IG-PCA-

combiningprincipal component

IBK 98.72

analysis (PCA) &information

3.IG-PCA-

gain (IG),(MLP) ,(SVM)&

MLP 98.66

Instance-based learning

4.IG-PCA-

algorithms (IBK) approaches .

28

NSL-KDD

ensemble

Kyoto2006+.

99.01

0.991

0.010

(RNN) &

Proposed a hybrid model that

ISCX-2012

98.61

94.90

0.07

(RBM)

combines a recurrent neural

DARPA1998

97.82

95.21

0.17

NA

NA

NA

network (RNN) with restricted Boltzmann machines (RBM) 29

RNN

Using different models of deep Recurrent Neural Network

NSL-KDD

(BLSTM, LSTM, BRNN, RNN)

11

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Construction ……

30

CNN & LSTM

Hierarchical spatial-temporal

DARPA1998

41.7

0.00

0.00

features-based on intrusion

ISCX2012

97.2

0.00

0.00

detection system (HAST-IDS) by using (CNNs) to learn the low-level spatial features of network traffic and then learns high-level temporal features using (LSTM) networks 31

(MLP)

Binary and multi-class

KDD-NSL

94.83

NA

NA

UNSWNB15

0.9571

2.19

0.00

KDDCUP 99

NA

17.24

0.00

KDDCup 99

99.85

99.84

0.19

91.50

39.60

0.40

NA

7.46X107

classification was carried out on the dataset 33

BLSTM.

It used Deep Learning Neural Network of multi-layer.

39

Firefly

The firefly algorithm to select

algorithm,(BN)

the features. Then resulted

and C4.5

features are submitted to Bayesian Networks (BN)and C4.5

15

(FGLCC)&(CF

IDS used feature grouping

A)

based on "linear correlation coefficient (FGLCC) " &"algorithm and cuttlefish algorithm (CFA)"

18

26

CorrCorr a

Features selected with a

UNSW-

feature

Principal Component Analysis

NB15

selection

(PCA) and a Pearson class label

NSL-KDD

method

correlation

LSTM

Proposed newmechanism to

ISCX2012

99.99

extract "packet semantic

USTC-

99.99

meanings "with LSTM to learn

TFC2016

"the temporal relation among fields in the packet header ".

12

1.1 X107

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Construction ……

35

DBN&PSO&

Optimizing the structure of

NSL-KDD

NA

Pro

fish algorithm

DBN(Deep BelieveNetwork).

p

Execute a PSO (Particle Swarm

3.55

Optimization) which depends on

DO

learning factor &weight. Then,

S

is used the fish swarm for

87.2

clustering.

U2 R

0.77

5.64

0.17

3.02

84.0 R2L 80.4 37

40

41

DNN

DBN & GA

Hyper parameter selection

NSL-KDD

0.789

NA

NA

methods used to select optimal

UNSW-

0.761

parameters and topologies for

NB15 Kyoto

0.885

DNNsare chosen

WSN-DS

0.982

CICIDS2017

0.931

NSL-KDD

DoS 99.45

99.7

0.8

(DBN)with

Prob99.37

99.4

0.7

improving on Genetic

R2L 97.78

93.4

7.3

Algorithm (GA).

U2R 98.68

98.2

1.8

82.08

NA

2.62

0.99975

NA

NA

Proposed Deep Belief Network

(MDPCA)and

Fuzzy aggregation approach

NSL-KDD

deep belief

using modified density peak

UNSW-

networks

clustering algorithm (MDPCA)

NB15

(DBNs).

and deep belief networks (DBNs).

56

ANN

Detect a classification of botnet

IDS2018

attack 84.0

Conclusion

applications (NIDS),0 because of its ability to analyze big data with high accuracy which

The DL outperformed traditional machine

resulting from its potential in self-learn from

learning methods in network intrusion detection

real-time data. In addition, DL mimics the ability

13

K.U.C.J

Rawaa, Dr. Nidaa and Dr. Abeer – Determining of Robust …

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Pages 1-19

Construction ……

of human mind to learn by accumulated

[3] Glenn A. Fink, Dimitri V. Z., Thomas E.

experiences, thus its enable to discover zero-day

Caroll,

attacks. However, there are challenges of (NIDS)

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make

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Approach For Intrusion Detection System

[57] Bipraneel Roy, Dr. Hon Cheung, "A Deep

(IDS) in the Internet of Things (IOT)

Learning

Approach

For

Intrusion

Network Using Gated Recurrent Neural

Detection In Internet Of Things Using Bi-

Networks (GRU)", Thesis Submitted to

Directional Long Short-Term Memory

Wright State University 2017.

Recurrent Neural Network", 2018 28th International

Telecommunication

19

Dr. Sami, Dr. Ahmed and Dr. Rasha – Measuring and ……

ISSN (E) : 2616 Construction …… – 7808

I

ISSN (P) : 2414 - 7419

K.U.C.J

Vol. 5 Issue 1 2020 5th Year

Pages 20-28

‫مجلة كلية الكوت الجامعة‬

Vol. 5

Issue 1

2020

5th Year

Measuring and Analyzing the Impact of Net Exports on Economic Growth in Iraq: A study for the Period of 2000-2018

Dr. Sami Awad

Dr. Ahmed Abdulrazaq Abdulrudha

Dr. Rasha Kahled

Wasit University

Middle Technical University

Wasit University

Faculty of Administration

Kut Technical Institute

Faculty of Administration

& Economics

& Economics

Abstract: The research aims to estimate the impact of exports on the economic growth of Iraq, as previous studies are still controversial about the relationship between export performance and economic growth, especially in the analysis of time series, so this study used time series data for the period 2000-2018 to estimate the relationship . ARDL estimate is used To diagnose this relationship, the data series were chosen on the basis of data availability as the results of the investigation showed a positive long-term relationship between economic growth and export performance, this research confirms the use of the necessary measures to increase the export of products with added value to the Iraqi economy.

Keywords: Export, Economic Growth, GDP, Iraq.

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2018 - 2000 ‫ دراسة للمده‬:‫قياس وتحليل صافي الصادرات على النمو االقتصادي في العراق‬ :‫المستخلص‬ ‫ للثرا ت ايث ا تااا الدعابااااتى اللااااتحقل ير لل دا اقا‬، ‫يهدف البحث إلى تقدير تأثير الصاااات عاى الى الاالق ااديصاااات‬ ‫ت وختصل في تحليل اللالبل الا ايلت لذلك ابيخد ت هذه الدعابل حيتنتى اللالبل الا ايل‬، ‫الثالدل حين أ اء الصت عاى والاالق ااديصت‬ ‫تقافر‬

‫ ليشااخيه هذه الثالدلت ودد تا اخييتع بااللاالل البيتنتى الى أباات‬ARDL ‫ يلاايخدت تقدير‬.‫ ليقدير الثالدل‬2000 - 2018 ‫للفير‬

‫ وأ اء اليصاااديرت ويا د هذا البحث ابااايخدات‬، ‫البيتنتى ايث أظهرى نيتئج اليح قيق وجق االدل إي تحيل طقيلل األجل حين الاالق ااديصااات‬ .‫اليداحير الالز ل لايت تصدير الالاي تى ذاى القيالل الالضتفل لالديصت الثرادي‬ . . ‫ت الاتتج الالحلي اإلجالتليت الثرا‬، ‫ الصت عاىت الاالق ااديصت‬: ‫الكلمات المفتاحية‬

Introduction: Exports contribute significantly to building the

become urgent today in relation to The national

economy of any country, and it is one of the

economy in order to cause a fundamental change

important mechanisms for increasing the rates of

in the structure of exports outside the oil sector,

GDP

general

as the sharp decline recorded in the proportion of

frameworks of the market range (Khan and

non-oil exports and the imbalance of the export

Saqib, 1993;). which is the most important

structure is still a source of concern and

element of access to abroad (Michaely, 1977).

insecurity for the state, which requires urgent

The expansion of exports in general helps to

measures to develop these exports and advance

reduce the many obstacles that stand in the way

the economy Iraqi forward.

growth

by

expanding

the

of the development process (Tyler, 1981).

Economic and the importance of export is clear The Importance:

from his ability to repair the deficit in the balance of payments and work to attract domestic and foreign

investment

and

create

new

The importance of the study is embodied in

job

clarifying the positive relationship between

opportunities (Balassa, 1985 ). achieving high

economic growth and exports in pushing forward

growth rates (Krueger, 1990). the necessity has

21

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Construction ……

the Iraqi economy forward through diversifying

Economist Ekanayake (1999) explained in one of

its sources of income.

his research how exports affect economic growth in

underdeveloped

countries.

The

results

revealed a significant correlation between export The Problem:

growth and economic growth in South Asian countries. As the study results showed no strong

The main research problem is embodied in

evidence of causation was found in the short

reducing the level of exports of goods and

term.

services very seriously in the Iraqi economy, and

An

observational

estimate

of

the

investigation between export performance and

the dependence of its exports on the rentier

economic growth using the Johansen co-

resource (oil),which caused a large deficit in the

integration test and the test of the causal

trade balance, and thus caused a decline in the

relationship to the Grager found that there was a

level of economic growth of the country in

one-way relationship

general.

between exports and

turbulent economic performance (2004). Qadous and Saeed (2005) illustrate the relationship between exports and economic growth and

The Aim:

emphasized that the results indicate a positive The research aims to study the relationship

causal

between

economic growth. Export growth could expand

economic

growth

and

export

relationship

between

exports

and

performance of the Iraqi economy, and gross

economic

domestic product has been used as an indicator to

currencies. In this regard, previous studies on the

measure economic growth in the country.

"subjective" growth hypothesis that focuses on

returns

by

multiplying

foreign

the dynamic flow of exports and increasing return to volume have confirmed. Exports may Previous studies:

increase long-term growth by enabling the economy to specialize in export products with

Export growth may increase the degree of

economies

economies of scale. From the above, we review

of scale.

despite

the positive

relationship between economic growth and high

the most important previous studies in this

export

regard: -

rates,

the

empirical

evidence

by

researchers (Greenaway and Sapsford, 1994a, 1994b; Khan and Saqib, 1993; Ahmad and

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GDP = f(E,I,P,T)

Kwan, 1991; Ghartey, 1993; Jung and Marshall, 1985; Love, 1994; Edwards, 1993). More

from 2000 to 2018 extracted from World Bank

export in providing uniform results confirmed relationship’s

failure

while

(1)

Annual time series data for all these variables

recently on the relationship between growth and the

……………….

open database and WIND database. The ARDL

important

model is applied on this study to investigate the

literature on cross-sectional studies promotes a

proposed framework proposed by Pesaran et al.

relationship between exports and economic

(2001). This approach is useful for small samples

growth. Conclusively, therefore, this proposal

and variables with heterogeneous order on

must be validated to demonstrate the legitimacy

integration for regresses.

of the impact of export performance on economic

Furthermore, an

unbiased results are estimated using ARDL

growth (Ghatak et al., 1995; Kugler, 1991; Dutt

method for long run relationship (Odhiambo,

and Ghosh, 1994; Afxentiou and Serletis,

2009).

1991b). In this vein, past results generally do not suggest a real correlation between long-term export performance and economic growth. The main

Results and discussion:

prerequisite for causality testing is Before applying ARDL framework, a standard

verification of a common integration proposal.

procedure has followed to estimate the level of integration. For this purpose, Augmented Dickey Fuller has applied. The unit root test is applied

Methodology:

using Baum’s (2015) modified methodology for To look at the connection among exports and

time series data. The results are presented in

economic growth, the present paper utilizes

Table 1. It is note that GDP is integrated at I(1)

ARDL model. The model to determine the

while

relationship of economic growth is determined

independent

variables

have

missed

integration levels I(0) and I(1). Hence, the first

by Gross Domestic Product (GDP), Exports (E),

condition has fulfilled that none of the variable is

Imports (I), Consumer Price Index (P) and Terms

integrated at I(2) (Kouakou, 2011).

of Trade (T). The model can be written in the following form

23

Dr. Sami, Dr. Ahmed and Dr. Rasha – Measuring and ‌‌

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Construction ‌‌

Table 1: ADF Estimation

*, ** and *** denote significance level at 1%, 5%

This procedure is useful to determine the long run

and 10%, respectively.

relationship using unrestricted ECM. This helps to estimate lagged level of variables and their respective significance. The ECM equation is

To estimate the relationship among variables the

given below to estimate the relationship among

preliminary step is ADL bound test estimation.

economic growth and exports.

đ?‘?

đ?‘ž

∆đ??şđ??ˇđ?‘ƒđ?‘Ą = đ?œ—1 + đ?œ—2 đ??żđ?‘›đ??¸đ?‘Ąâˆ’1 + đ?œ—3 đ??żđ?‘›đ??źđ?‘Ąâˆ’1 + đ?œ—4 đ??żđ?‘›đ?‘ƒđ?‘Ąâˆ’1 + đ?œ—5 đ??żđ?‘›đ?‘‡đ?‘Ąâˆ’1 + ∑ đ?œ—đ?‘– ∆đ??żđ?‘›đ??¸đ?‘Ąâˆ’đ?‘– + ∑ đ?œ—đ?‘— ∆đ??żđ?‘›đ??źđ?‘Ąâˆ’đ?‘— đ?‘–=1 đ?‘&#x;

đ?‘

+ ∑ đ?œ—đ?‘˜ ∆đ??żđ?‘›đ?‘ƒđ?‘Ąâˆ’đ?‘˜ + ∑ đ?œ—đ?‘™ ∆đ??żđ?‘›đ?‘‡đ?‘Ąâˆ’đ?‘™ + đ?œ‡1đ?‘Ą đ?‘˜=0

đ?‘—=0

đ?‘™=0

24

‌ ‌ ‌.

(2)

K.U.C.J

Dr. Sami, Dr. Ahmed and Dr. Rasha – Measuring and ‌‌

Vol. 5 Issue 1 2020 5th Year

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Construction ‌‌

∆đ??şđ??ˇđ?‘ƒđ?‘Ą = ď ¤1 + ď ¤2 đ??żđ?‘›đ??¸đ?‘Ąâˆ’1 + ď ¤3 đ??żđ?‘›đ??źđ?‘Ąâˆ’1 + ď ¤4 đ??żđ?‘›đ?‘ƒđ?‘Ąâˆ’1 + ď ¤5 đ??żđ?‘›đ?‘‡đ?‘Ąâˆ’1 đ?‘?

đ?‘ž

đ?‘&#x;

đ?‘

+ ∑ ď ¤đ?‘– ∆đ??żđ?‘›đ??¸đ?‘Ąâˆ’đ?‘– + ∑ ď ¤đ?‘— ∆đ??żđ?‘›đ??źđ?‘Ąâˆ’đ?‘— + ∑ ď ¤đ?‘˜ ∆đ??żđ?‘›đ?‘ƒđ?‘Ąâˆ’đ?‘˜ + ∑ ď ¤đ?‘™ ∆đ??żđ?‘›đ?‘‡đ?‘Ąâˆ’đ?‘™ + đ?œ‡2đ?‘Ą ‌ ‌ ‌ đ?‘–=1

đ?‘—=0

đ?‘˜=0

(3)

đ?‘™=0

In bound procedure, the joint significance of the

accepted. In contrast, if the computed F-statistic

lagged levels is tested using F-test that has non-

falls within the bounds, inference would be

standard asymptotic distribution. If the computed

inconclusive. Following Lee (2010), maximum

F-statistic falls above the upper value bound, the

lag length is determined by the Breusch-Godfrey

null is rejected, indicating co-integration. If the

LM test where it fails to reject the null hypothesis

computed F-statistic falls below the lower bound,

of serially uncorrelated residuals at 5% level of

the null hypothesis of no co-integration is

significance.

Table 2: ARDL Bound Test Estimation

Tables 3 illustrate the results of bound testing

relationship between economic growth and

approach. It is clearly detected from the results

exports. Further we estimate the long run

that it fails to reject the null hypothesis of no co-

coefficients for our model. The long run

integration. Hence the results state a long run

coefficient estimation is run by using the equation estimate co-integration.

25

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Table 3. Coefficient Estimation for Long Run Relationship

*, ** and *** indicate significant at 1%, 5% and

bring 2.02% increase in economic growth. The

10% level respectively. Numbers in parenthesis

relationship is strong as well as significant.

are the t-statistics. The optimal lags are selected

Further the relationship among log imports,

based on AIC.

consumer price index and term of trade is negative. Henceforth, the results indicate that

The above result in table 3 confirms a positive relationship

among economic

exports growth has a strong influence on the

growth and

economic growth of Iraq.

exports. The 1% increase in log exports will

Table 4. Diagnostic Tests

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Diagnostic test presented in table 4 supports the

Through these results, the researcher provides

model. JB test for normality failed to reject

some recommendations for policy makers to

normally distributed residuals. Further LM test is

focus on increasing the level of exports through:

applied autocorrelation and does not present any

1- Diversification of oil commodity production

problem with auto correlated residuals.

As long as the country is an oil-based economy, it is necessary to switch from crude oil exports to exports of value-added products.

Conclusions and Recommendations:

2. Reforming the banking sector: - Through

The study explained the relationship between

various monetary policy tools in order to

economic growth and export performance for

stimulate non-oil exports while providing the

Iraq. GDP is used as an agent of economic

necessary funding to support the industrial

growth. A binding ARDL test framework was

sector.

applied to estimate the long-term relationship between export performance and economic

3- Establishing export-free zones and industrial

growth. ECM was applied to determine the

zones: - The main objective of establishing these

common complementarity between the proposed

zones is to attract investment directed to export.

variables.

After

confirming

the

joint

4- Stimulating investment in industry, by linking

complementarity of the variables, determine the

grants

long-term relationship parameter. The results

and

incentives

provided

by

the

government to industries that enjoy special

confirmed a positive and significant long-term

advantages, such as the rise of the technological

relationship between economic growth and

component, or the increase of its production from

export performance. These findings mean some

production abroad.

suggestions for policymakers to pay attention to increasing the level of exports by producing more oil supplies. Since the country is an oil-based

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Vol. 5 Issue 1 2020 5th Year

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