1. Introduction

The application of artificial intelligence in the field of computer network security has become an inescapable reality. In this context, we propose an extended version of an oral communication [1] which deals with network intrusion detection based on the SVM model.

 The use of computer network services has become essential in several areas, particularly those related to daily life. The exchange of data can be very costly in the case of sometimes fatal threats, especially when it is a large-scale exchange at the level of governments or businesses [2]. These threats affect the integrity, confidentiality, and availability of these data. Possible solutions for the protection of computer networks are divided into two categories, those related to hardware or those related to software. We can cite firewalls, antivirus, cryptography, etc. as solutions. However, these solutions are insufficient to protect all networks [3]. In order to strengthen network security, another higher level of security such as IDSs is needed.

An IDS is a medium that allows us to protect our computer network from any unwanted activity as described in [4]. It filters incoming and outgoing network traffic to detect possible threats in the network. We distinguish between two types of IDS, the classical ones based on signatures as in the open-source software SNORT. They have a database of intrusion signatures. The signature of each incoming entity is compared with the signatures present in this database. Thus, the signature-based-IDS is very efficient for the detection of common threats and generates a very low FAR (False Alarm Rate). Despite this, it is unable to detect modern attacks.   The second type of IDS is based on artificial intelligence techniques using ML for the detection of new attacks (0 Day attack) as described in the work [5]. However, the only problem is the very high false alarm rate generated by this type of IDS. This is the reason why several scientific research works are interested in this topic.

In this article, we present an IDS based on the technique of ML. We have chosen to use an SVM binary classification model associated with a UNSWNB-15 dataset for training and testing the proposed model. This article is structured into seven sections: In section 2, we present the related work. In the next section, we discuss the basic principle of an IDS. Then, section 4 concerns the existing data sources and the description of the UNSWNB-15 data set that we have used in this paper. In Section 5, we present our methods. In section 6, we show the performance of our proposed model with a comparison and interpretation of the results obtained. In the last section, we have a general conclusion and the perspectives of this work.

2. Related Work

In recent years, protecting computer networks against various threats is a priority for most companies. The evolution of cyberattacks requires the use of highly developed ways for the detection of these threats. Among the first works, we can cite the work of James Canady [6] who used the Artificial Neural Network (ANN) classification model in 1998. Netssan P, et al [7] obtained a detection rate of 85.87% with the KDD-CUP 99 dataset by combining two naive Bayesian models and decision trees. Other work using the same KDD-CUP 99 dataset has been proposed by O. Deepen et al [8]. They obtained a DR of 99.9% so the FAR is too low in the order of 1.25%. Also, Divyatmika et al [9] achieved a detection rate close to 99% with the Multilayer Perceptron (MLP) classification model algorithm using KDD-CUP 99 dataset. Finally, G-H Zhou [10] proposed a very efficient model (Detection rate = 99.54%) using a KDD-CUP 99 dataset for an IDS.

In parallel to the above, several works have used different ML algorithms for the detection of threats in computer networks. We cite the work of G. Meena et al [11] who recently published a paper using some Machine Learning algorithms associated with the two datasets NSL-KDD and KDD-CUP 99. Also, L. Dhanabal et al [12] published a study evaluating the efficiency of ML algorithms with the NSL-KDD dataset.

      The two datasets KDD-CUP 99 and NSL-KDD are used in most of the previous work. They remain ineffective for the detection of recent attacks, even though the detection rate is very high for the different classification algorithms [13]. Therefore, it is necessary to use a newly updated dataset containing the behavior of modern network traffic. In Section 4, we will develop a study of the different existing datasets to show the effectiveness of a modern intrusion detection system.

3. Intrusion Detection System

An IDS allows us to protect our computer network infrastructure from any malicious activity. These activities are generally targeting the integrity, confidentiality, and availability of data in our network.

We classify IDS according to several criteria such as the type of IDS and the associated classification method. Regarding the type, we have the (N-IDS) and the (H-IDS) (Figure 1):

• N-IDS (Network Intrusion Detection System): It secures the entire network. Its location in the network affects the false alarm rate. For example, if the NIDS is located upstream of a Firewall (Figure 1), then it generates fewer false alarms. Since the traffic is already filtered by the Firewall. Otherwise, if it is placed downstream of a Firewall (location 2). It generates more false alarms.
• H-IDS (Host Intrusion Detection System): It allows the machine to be secured on its own without the use of other systems.

      The IDSs are classified according to the detection method as in the work [14]. The first is signature-based-IDS. In this case, the IDS have a signature base compared with the signature of each incoming entity. This allows the IDS to be very favorable for the detection of already known intrusions. However, this first class of IDS only detects modern attacks (0 Day attack) i.e. threats with unknown signatures [15]. The second detection class is anomaly-based-IDS as in [16]. In this work, we have used this last class of threat detection. We have focused on new machine learning techniques. Knowing that an IDS builds a user profile based on the normal behavior of the system. It then monitors the network to identify any abnormal activity in the network. The anomaly-based- IDS can detect modern attacks, but it generates a high FAR in contrary to the signature-based-IDS.

Figure1: Architecture of an IDS

4. Datasets

4.1. IDS-Dataset

      Since the end of the 1990s, research work has been developing datasets for IDSs. These data sources are collected from network traffic simulators or by real systems, although the latter is very difficult to achieve because it may contain private data. One of the most widely used data sources is DRAPPA98 [17] published publicly in 1998 by MIT. Also, the dataset KDD-CUP 99 [18] published in 1999. This dataset contains the features of four categories of attacks (DOS, Probe, R2L, U2R). The major problem encountered is the considerable amount of redundant data which makes the use of this dataset difficult. We can also mention NSL-KDD dataset [19]. It is an updated version of the KDD-CUP 99. The NSL-KDD dataset is widely used for the network intrusion classification. Finally, we have other but less popular datasets such as CAIDA, DARPA, ISCX dataset, CICIDS.

4.2. Selecting-dataset

Previous researches are based on the two datasets KDD-CUP 99 and NSL-KDD for the classification of threats in computer networks. The authors of the works [13,20,21] have shown that these datasets encounter significant problems and their use is unreliable. These datasets suffer from a lack of diversity in traffic volumes. They contain duplicate data which does not cover real and recent attacks. To overcome these dataset issues, we have adopted the UNSWNB-15 [22] data source for training and testing our proposed model. Table 1 presents a comparison between the three datasets KDD-CUP 99, NSL-KDD, and UNSWNB-15.

Table 1: Comparison between the three datasets

RNC: Realistic Network Configuration

RNT: Realistic Network Traffic

LO: Labelled Observations

TIC: Total Interaction Capture

FPC: Full Packet Capture

MMS: Many Malicious Scenarios

4.3. UNSWNB-15 dataset description

In this work, we have used a UNSWNB-15 dataset. This dataset was developed by ACCS (Australian Centre for Cyber Security) using IXIA Perfect storm tools. It is published in 2015 by authors Mustapha & Slay [23]. The UNSWNB-15 dataset contains 49 features categorized into six data associations (See Table 2 below).

Table 2: UNSWNB-15 dataset features

The UNSW-NB15 dataset provides two label features: ‘Attck_cat’ represent the nine attack types (see table 3). It is used for a Multi-Class classification. The feature ‘label’ is used for binary classification (Normal and Attack). In this work, we choose to use a binary classification based on the second label feature.

Table 3: Types of attacks on UNSWNB-15

5. Proposed model

As we have previously shown, a signature-based-IDS is very efficient to detect the known attacks. However, it has a very low detection rate for recent attacks with unknown signatures. Figure 2 shows the Machine Learning steps followed in our model for the detection of attacks in a computer network. We used the SVM model to separate the two classes (Attack and No-Attack). Since in our case the data are non-linearly separable, we used the two functions with Polynomial and Gaussian kernel. These two functions are known by their good performances at the classification level in the SVM.

Figure 2: SVM binary classification proposed model

In our experience, the UNSWNB-15 dataset is composed of both CSV (Comma-separated values) files called UNSW_NB15_training_set.csv and UNSW_NB15_testing_set.csv. The first file contains 175 341 records for training the model (68%). However, the second contains 82 332 records for testing the model (32%). This dataset contains an integer, float, timestamp, and binary data type [23].

5.1. Data preprocessing

To train and test our model, a pre-processing step is necessary to select useful data ready for classification.

• Data-mapping: the first pre-processing step is data-mapping. It consists in transforming nominal data into numerical data (see figure 3). For example, the state field contains nominal data (INT, FIN, CON). In this phase, we converted these nominal values into numerical values (1,2,3).

Figure 3: Data-mapping

• Data normalization: this step consists of increasing the speed of data processing. In this work, we applied the Min-Max method for data normalization. This consists in putting the numerical data in the margin [0-1] according to the following formula (1):

5.2. The SVM classifier

      In this work, we have two observed classes (Attack and No-Attack). We used the SVM model for binary classification. This model is one of the algorithms of Machine Learning that have provided great performances either in classification or regression.  It is a supervised learning model developed by Vapnik. In the case of classification, as in our case, the SVM model consists in finding a better boundary between the two observed classes (attack and No-Attack) (see figure 4). In this case, the data are non-linearly separable. For this purpose, we propose the kernel trick to have an optimal data separation as described in the work [24]. We assign two kernels one Gaussian and the other Polynomial because the data of the UNSWNB-15 dataset are also not linearly separable.

Figure 4: SVM Model hyperplane separation

6. Results and discussions

6.1. Criteria for Evaluating an IDS

The evaluation of an IDS is dependent on two main criteria:

• The Reliability of the IDS: The IDS must produce an alert for each incoming threat. An unreported intrusion is a system failure.
• The relevance of the IDS: Each alert must relate to a real threat. If we have a normal input event and it is classified as an intrusion also considered as well as a system failure. Table 4 presents the 4 possible cases.

Table 4: The confusion matrix

TP (True Positive): Normal traffic is classified normal

FP (False positive): Normal traffic is classified abnormal.

FN (False Negative): abnormal traffic is classified normal

TN (True Negative): abnormal traffic is classified abnormal.

In our case, the confusion matrix gives the number of right and wrong predicted attacks, the same for the normal class (No-Attack).

6.2. Evaluation metrics of an IDS

To illustrate the effectiveness of our model, in this work we used six evaluation metrics.

We begin with the TPR (True Positive Rate) or Sensitivity. It describes the proportion of the positive class (Attack) correctly classified. The TPR calculates the quality of the IDS in terms of detection. To calculate the TPR, we use formula (2), note that the TPR must be close to 1 for a good IDS.

On the opposite, the FPR (False Positive Rate) or Specificity. It describes the proportion of the negative class (No-Attack) correctly classified. To calculate the FPR, we use the formula (3), note that the FPR must be close to 0 for a good IDS.

An effective IDS should provide a TPR close to 1 and a FPR close to 0.

The third evaluation metric is accuracy, which provides the true predictions for the entire test base. We calculate the accuracy according to the following formula (4):

Accuracy calculation (PR) provides the proportion of true positive predictions in the forecast class (Attack). The accuracy is shown by the following formula:

The fifth metric is RECALL (RE). It returns the proportion of correct predictions in the attack class. It is calculated according to the formula (6):

The last metric that we have used is F1-Score. This is a calculation of the harmonic average between precision (PR) and recall (RE). It is calculated according to formula (7):

6.3. The obtained results

Our model is tested with the UNSWNB-15 dataset. The SVM classifier is used with two kernel functions Polynomial and Gaussian for binary classification. With this model, we obtained good results in terms of DR for the classes normal and malicious.

The figures 5 and 6 show, respectively the confusion matrix for the two kernels SVM-Gaussian and SVM-Polynomial.

Figure 5: Confusion Matrix for the SVM- Gaussian model

Figure 6: Confusion Matrix for the SVM-Polynomial model

To present our obtained results, we have plotted the ROC curves for the two kernels. These curves represent the TPR (Sensitivity) as a function of FPR (Specificity). They show that we have a very high rate of true positives (close to 1) against a low rate of false alarms (close to 0). This validates the performance of our model.

Figure 7: ROC curves correspond to Gaussian kernels

Figure 8: ROC curves correspond to Polynomial kernels

Tables 5 and 6 show the performance of the two kernels SVM-Gaussian and SVM-Polynomial:

• With the Gaussian kernel, we obtained a DR of 94% for the normal class and 91% for the malicious class.
• For the Polynomial kernel, we found slightly high results compared to the Gaussian function. We obtained a detection rate of 95% for the normal class and 93% for the abnormal class.

Table 5. SVM Gaussian kernel results

Table 6. SVM Polynomial kernel results

6.4. Performance evaluation of the proposed model

To evaluate the effectiveness of our model, we compared our obtained results with those of other work using the same UNSWNB-15 dataset (Table 7). The comparison results of our SVM model with other classification models are good. We obtained for the Polynomial kernel an accuracy equal to 94% as shown in the following table:

Table 7. Comparison with of other models

7. Conclusions and perspective

In this work, we have developed a novel method for detecting attacks in computer networks. For this purpose, we presented a binary model for the classification of intrusions. We used the UNSWNB-15 data source. This source is widely used in the field of cybersecurity. We based on the SVM classifier with two kernel functions SVM-Polynomial and SVM-Gaussian. The evaluation results show high performance for both Gaussian (Accuracy = 93%) and Polynomial (Accuracy = 94%) kernels. In perspective, we will opt for a multi-class classification for the detection of each type of intrusion separately.

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29. Radwan Qasrawi, Stephanny VicunaPolo, Diala Abu Al-Halawa, Sameh Hallaq, Ziad Abdeen, "Predicting School Children Academic Performance Using Machine Learning Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 08–15, 2021. doi: 10.25046/aj060502
30. Zhiyuan Chen, Howe Seng Goh, Kai Ling Sin, Kelly Lim, Nicole Ka Hei Chung, Xin Yu Liew, "Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 376–384, 2021. doi: 10.25046/aj060442
31. Hathairat Ketmaneechairat, Maleerat Maliyaem, Chalermpong Intarat, "Kamphaeng Saen Beef Cattle Identification Approach using Muzzle Print Image", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 110–122, 2021. doi: 10.25046/aj060413
32. Sékou Togola, Sountongnoma Martial Anicet Kiemde, Ahmed Dooguy Kora, "Real Time and Post-Processing Flight Inspection by Drone: A Review", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 92–99, 2021. doi: 10.25046/aj060310
33. Md Mahmudul Hasan, Nafiul Hasan, Dil Afroz, Ferdaus Anam Jibon, Md. Arman Hossen, Md. Shahrier Parvage, Jakaria Sulaiman Aongkon, "Electroencephalogram Based Medical Biometrics using Machine Learning: Assessment of Different Color Stimuli", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 27–34, 2021. doi: 10.25046/aj060304
34. Dominik Štursa, Daniel Honc, Petr Doležel, "Efficient 2D Detection and Positioning of Complex Objects for Robotic Manipulation Using Fully Convolutional Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 915–920, 2021. doi: 10.25046/aj0602104
35. Md Mahmudul Hasan, Nafiul Hasan, Mohammed Saud A Alsubaie, "Development of an EEG Controlled Wheelchair Using Color Stimuli: A Machine Learning Based Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 754–762, 2021. doi: 10.25046/aj060287
36. Antoni Wibowo, Inten Yasmina, Antoni Wibowo, "Food Price Prediction Using Time Series Linear Ridge Regression with The Best Damping Factor", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 694–698, 2021. doi: 10.25046/aj060280
37. Javier E. Sánchez-Galán, Fatima Rangel Barranco, Jorge Serrano Reyes, Evelyn I. Quirós-McIntire, José Ulises Jiménez, José R. Fábrega, "Using Supervised Classification Methods for the Analysis of Multi-spectral Signatures of Rice Varieties in Panama", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 552–558, 2021. doi: 10.25046/aj060262
38. Phillip Blunt, Bertram Haskins, "A Model for the Application of Automatic Speech Recognition for Generating Lesson Summaries", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 526–540, 2021. doi: 10.25046/aj060260
39. Sebastianus Bara Primananda, Sani Muhamad Isa, "Forecasting Gold Price in Rupiah using Multivariate Analysis with LSTM and GRU Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 245–253, 2021. doi: 10.25046/aj060227
40. Byeongwoo Kim, Jongkyu Lee, "Fault Diagnosis and Noise Robustness Comparison of Rotating Machinery using CWT and CNN", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1279–1285, 2021. doi: 10.25046/aj0601146
41. Essamudin Ali Ebrahim, Nourhan Ahmed Maged, Naser Abdel-Rahim, Fahmy Bendary, "Open Energy Distribution System-Based on Photo-voltaic with Interconnected- Modified DC-Nanogrids", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 982–988, 2021. doi: 10.25046/aj0601108
42. Mandlenkosi Shezi, Abejide Ade-Ibijola, "Deaf Chat: A Speech-to-Text Communication Aid for Hearing Deficiency", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 826–833, 2020. doi: 10.25046/aj0505100
43. Md Mahmudul Hasan, Nafiul Hasan, Mohammed Saud A Alsubaie, Md Mostafizur Rahman Komol, "Diagnosis of Tobacco Addiction using Medical Signal: An EEG-based Time-Frequency Domain Analysis Using Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 842–849, 2021. doi: 10.25046/aj060193
44. Reem Bayari, Ameur Bensefia, "Text Mining Techniques for Cyberbullying Detection: State of the Art", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 783–790, 2021. doi: 10.25046/aj060187
45. Inna Valieva, Iurii Voitenko, Mats Björkman, Johan Åkerberg, Mikael Ekström, "Multiple Machine Learning Algorithms Comparison for Modulation Type Classification Based on Instantaneous Values of the Time Domain Signal and Time Series Statistics Derived from Wavelet Transform", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 658–671, 2021. doi: 10.25046/aj060172
46. Carlos López-Bermeo, Mauricio González-Palacio, Lina Sepúlveda-Cano, Rubén Montoya-Ramírez, César Hidalgo-Montoya, "Comparison of Machine Learning Parametric and Non-Parametric Techniques for Determining Soil Moisture: Case Study at Las Palmas Andean Basin", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 636–650, 2021. doi: 10.25046/aj060170
47. Ndiatenda Ndou, Ritesh Ajoodha, Ashwini Jadhav, "A Case Study to Enhance Student Support Initiatives Through Forecasting Student Success in Higher-Education", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 230–241, 2021. doi: 10.25046/aj060126
48. Lonia Masangu, Ashwini Jadhav, Ritesh Ajoodha, "Predicting Student Academic Performance Using Data Mining Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 153–163, 2021. doi: 10.25046/aj060117
49. Sara Ftaimi, Tomader Mazri, "Handling Priority Data in Smart Transportation System by using Support Vector Machine Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1422–1427, 2020. doi: 10.25046/aj0506172
50. Othmane Rahmaoui, Kamal Souali, Mohammed Ouzzif, "Towards a Documents Processing Tool using Traceability Information Retrieval and Content Recognition Through Machine Learning in a Big Data Context", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1267–1277, 2020. doi: 10.25046/aj0506151
51. Puttakul Sakul-Ung, Amornvit Vatcharaphrueksadee, Pitiporn Ruchanawet, Kanin Kearpimy, Hathairat Ketmaneechairat, Maleerat Maliyaem, "Overmind: A Collaborative Decentralized Machine Learning Framework", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 280–289, 2020. doi: 10.25046/aj050634
52. Ihsan Mizher Baht, Petre Marian Nicolae, Ileana Diana, Nameer Baht, "Analysis of Green Building Effect on Micro grid Based on Potential Energy Savings and BIM", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 30–35, 2020. doi: 10.25046/aj050604
53. Pamela Zontone, Antonio Affanni, Riccardo Bernardini, Leonida Del Linz, Alessandro Piras, Roberto Rinaldo, "Supervised Learning Techniques for Stress Detection in Car Drivers", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 22–29, 2020. doi: 10.25046/aj050603
54. Kodai Kitagawa, Koji Matsumoto, Kensuke Iwanaga, Siti Anom Ahmad, Takayuki Nagasaki, Sota Nakano, Mitsumasa Hida, Shogo Okamatsu, Chikamune Wada, "Posture Recognition Method for Caregivers during Postural Change of a Patient on a Bed using Wearable Sensors", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1093–1098, 2020. doi: 10.25046/aj0505133
55. Khalid A. AlAfandy, Hicham Omara, Mohamed Lazaar, Mohammed Al Achhab, "Using Classic Networks for Classifying Remote Sensing Images: Comparative Study", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 770–780, 2020. doi: 10.25046/aj050594
56. Khalid A. AlAfandy, Hicham, Mohamed Lazaar, Mohammed Al Achhab, "Investment of Classic Deep CNNs and SVM for Classifying Remote Sensing Images", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 652–659, 2020. doi: 10.25046/aj050580
57. Rajesh Kumar, Geetha S, "Malware Classification Using XGboost-Gradient Boosted Decision Tree", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 536–549, 2020. doi: 10.25046/aj050566
58. Nghia Duong-Trung, Nga Quynh Thi Tang, Xuan Son Ha, "Interpretation of Machine Learning Models for Medical Diagnosis", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 469–477, 2020. doi: 10.25046/aj050558
59. Oumaima Terrada, Soufiane Hamida, Bouchaib Cherradi, Abdelhadi Raihani, Omar Bouattane, "Supervised Machine Learning Based Medical Diagnosis Support System for Prediction of Patients with Heart Disease", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 269–277, 2020. doi: 10.25046/aj050533
60. Haytham Azmi, "FPGA Acceleration of Tree-based Learning Algorithms", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 237–244, 2020. doi: 10.25046/aj050529
61. Hicham Moujahid, Bouchaib Cherradi, Oussama El Gannour, Lhoussain Bahatti, Oumaima Terrada, Soufiane Hamida, "Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 167–175, 2020. doi: 10.25046/aj050522
62. Young-Jin Park, Hui-Sup Cho, "A Method for Detecting Human Presence and Movement Using Impulse Radar", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 770–775, 2020. doi: 10.25046/aj050491
63. Adonis Santos, Patricia Angela Abu, Carlos Oppus, Rosula Reyes, "Real-Time Traffic Sign Detection and Recognition System for Assistive Driving", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 600–611, 2020. doi: 10.25046/aj050471
64. Amar Choudhary, Deependra Pandey, Saurabh Bhardwaj, "Overview of Solar Radiation Estimation Techniques with Development of Solar Radiation Model Using Artificial Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 589–593, 2020. doi: 10.25046/aj050469
65. Maroua Abdellaoui, Dounia Daghouj, Mohammed Fattah, Younes Balboul, Said Mazer, Moulhime El Bekkali, "Artificial Intelligence Approach for Target Classification: A State of the Art", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 445–456, 2020. doi: 10.25046/aj050453
66. Shahab Pasha, Jan Lundgren, Christian Ritz, Yuexian Zou, "Distributed Microphone Arrays, Emerging Speech and Audio Signal Processing Platforms: A Review", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 331–343, 2020. doi: 10.25046/aj050439
67. Ilias Kalathas, Michail Papoutsidakis, Chistos Drosos, "Optimization of the Procedures for Checking the Functionality of the Greek Railways: Data Mining and Machine Learning Approach to Predict Passenger Train Immobilization", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 287–295, 2020. doi: 10.25046/aj050435
68. Yosaphat Catur Widiyono, Sani Muhamad Isa, "Utilization of Data Mining to Predict Non-Performing Loan", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 252–256, 2020. doi: 10.25046/aj050431
69. Hai Thanh Nguyen, Nhi Yen Kim Phan, Huong Hoang Luong, Trung Phuoc Le, Nghi Cong Tran, "Efficient Discretization Approaches for Machine Learning Techniques to Improve Disease Classification on Gut Microbiome Composition Data", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 547–556, 2020. doi: 10.25046/aj050368
70. Ruba Obiedat, "Risk Management: The Case of Intrusion Detection using Data Mining Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 529–535, 2020. doi: 10.25046/aj050365
71. Krina B. Gabani, Mayuri A. Mehta, Stephanie Noronha, "Racial Categorization Methods: A Survey", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 388–401, 2020. doi: 10.25046/aj050350
72. Amal Hadri, Khalid Chougdali, Raja Touahni, "Improved Nonlinear Fuzzy Robust PCA for Anomaly-based Intrusion Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 249–258, 2020. doi: 10.25046/aj050332
73. Dennis Luqman, Sani Muhamad Isa, "Machine Learning Model to Identify the Optimum Database Query Execution Platform on GPU Assisted Database", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 214–225, 2020. doi: 10.25046/aj050328
74. Gillala Rekha, Shaveta Malik, Amit Kumar Tyagi, Meghna Manoj Nair, "Intrusion Detection in Cyber Security: Role of Machine Learning and Data Mining in Cyber Security", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 72–81, 2020. doi: 10.25046/aj050310
75. Ahmed EL Orche, Mohamed Bahaj, "Approach to Combine an Ontology-Based on Payment System with Neural Network for Transaction Fraud Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 551–560, 2020. doi: 10.25046/aj050269
76. Cheng Chuko, Fang-Lin Chao, Hsin-Yu Tsai, "Design of Interactive Aids for Children’s Teeth Cleaning Habits", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 494–499, 2020. doi: 10.25046/aj050263
77. Bokyoon Na, Geoffrey C Fox, "Object Classifications by Image Super-Resolution Preprocessing for Convolutional Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 476–483, 2020. doi: 10.25046/aj050261
78. Asmae Bengag, Amina Bengag, Omar Moussaoui, "Attacks Classification and a Novel IDS for Detecting Jamming Attack in WBAN", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 80–86, 2020. doi: 10.25046/aj050210
79. Johannes Linden, Xutao Wang, Stefan Forsstrom, Tingting Zhang, "Productify News Article Classification Model with Sagemaker", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 13–18, 2020. doi: 10.25046/aj050202
80. Michael Wenceslaus Putong, Suharjito, "Classification Model of Contact Center Customers Emails Using Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 174–182, 2020. doi: 10.25046/aj050123
81. Rehan Ullah Khan, Ali Mustafa Qamar, Mohammed Hadwan, "Quranic Reciter Recognition: A Machine Learning Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 173–176, 2019. doi: 10.25046/aj040621
82. Mehdi Guessous, Lahbib Zenkouar, "An ML-optimized dRRM Solution for IEEE 802.11 Enterprise Wlan Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 19–31, 2019. doi: 10.25046/aj040603
83. Toshiyasu Kato, Yuki Terawaki, Yasushi Kodama, Teruhiko Unoki, Yasushi Kambayashi, "Estimating Academic results from Trainees’ Activities in Programming Exercises Using Four Types of Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 321–326, 2019. doi: 10.25046/aj040541
84. Woochun Jun, "Development of Teaching Aids Selection Standards in Maker Education", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 139–142, 2019. doi: 10.25046/aj040518
85. Aashir Waleed, Umar Siddique Virk, Muhammad Tanveer Riaz, Shaikh Bilal Mehmood, Saeed Ahmad, Muhammad Rameez Javed, Ali Raza, "Effectiveness and Comparison of Digital Substations Over Conventional Substations", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 431–439, 2019. doi: 10.25046/aj040452
86. Nindhia Hutagaol, Suharjito, "Predictive Modelling of Student Dropout Using Ensemble Classifier Method in Higher Education", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 206–211, 2019. doi: 10.25046/aj040425
87. Fernando Hernández, Roberto Vega, Freddy Tapia, Derlin Morocho, Walter Fuertes, "Early Detection of Alzheimer’s Using Digital Image Processing Through Iridology, An Alternative Method", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 126–137, 2019. doi: 10.25046/aj040317
88. Abba Suganda Girsang, Andi Setiadi Manalu, Ko-Wei Huang, "Feature Selection for Musical Genre Classification Using a Genetic Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 162–169, 2019. doi: 10.25046/aj040221
89. Konstantin Mironov, Ruslan Gayanov, Dmiriy Kurennov, "Observing and Forecasting the Trajectory of the Thrown Body with use of Genetic Programming", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 248–257, 2019. doi: 10.25046/aj040124
90. Bok Gyu Han, Hyeon Seok Yang, Ho Gyeong Lee, Young Shik Moon, "Low Contrast Image Enhancement Using Convolutional Neural Network with Simple Reflection Model", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 159–164, 2019. doi: 10.25046/aj040115
91. Zheng Xie, Chaitanya Gadepalli, Farideh Jalalinajafabadi, Barry M.G. Cheetham, Jarrod J. Homer, "Machine Learning Applied to GRBAS Voice Quality Assessment", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 329–338, 2018. doi: 10.25046/aj030641
92. Richard Osei Agjei, Emmanuel Awuni Kolog, Daniel Dei, Juliet Yayra Tengey, "Emotional Impact of Suicide on Active Witnesses: Predicting with Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 501–509, 2018. doi: 10.25046/aj030557
93. Faizan Dastgeer, Hassan Erteza Gelani, Faisal Ali, Zahir Javed Paracha, "Enhancing the Energy/Power Efficiency of a DC Distribution Grid for Residential Buildings via Modular Architecture of DC/DC Solid State Transformers", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 288–297, 2018. doi: 10.25046/aj030534
94. Sudipta Saha, Aninda Saha, Zubayr Khalid, Pritam Paul, Shuvam Biswas, "A Machine Learning Framework Using Distinctive Feature Extraction for Hand Gesture Recognition", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 72–81, 2018. doi: 10.25046/aj030510
95. Charles Frank, Asmail Habach, Raed Seetan, Abdullah Wahbeh, "Predicting Smoking Status Using Machine Learning Algorithms and Statistical Analysis", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 2, pp. 184–189, 2018. doi: 10.25046/aj030221
96. Sehla Loussaief, Afef Abdelkrim, "Machine Learning framework for image classification", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 1, pp. 1–10, 2018. doi: 10.25046/aj030101
97. Ruijian Zhang, Deren Li, "Applying Machine Learning and High Performance Computing to Water Quality Assessment and Prediction", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 6, pp. 285–289, 2017. doi: 10.25046/aj020635
98. Batoul Haidar, Maroun Chamoun, Ahmed Serhrouchni, "A Multilingual System for Cyberbullying Detection: Arabic Content Detection using Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 6, pp. 275–284, 2017. doi: 10.25046/aj020634
99. Yuksel Arslan, Abdussamet Tanıs, Huseyin Canbolat, "A Relational Database Model and Tools for Environmental Sound Recognition", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 6, pp. 145–150, 2017. doi: 10.25046/aj020618
100. Loretta Henderson Cheeks, Ashraf Gaffar, Mable Johnson Moore, "Modeling Double Subjectivity for Gaining Programmable Insights: Framing the Case of Uber", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1677–1692, 2017. doi: 10.25046/aj0203209