Pub Date : 2022-09-21DOI: 10.1109/ICOASE56293.2022.10075608
L. Mohammed, Salsabeel H. Taha
This work uses green energy to solve the problem of lacking electrical energy in agricultural areas. Thus, it suggests using photovoltaic systems to supply the engines used in irrigation in these areas where the load is variable. In addition, this work uses a three-phase inverter to achieve the lowest possible total harmonic distortion. Furthermore, the motor's torque is fixed at all speeds, while the volt-to-hertz ratio speed control of the induction motor is discussed in this paper. By varying the modulation index of the space-vector-pulse-width, the stator voltage of the induction motor can differ correspondingly. The time duration of gate pulses is changed to maintain the constant voltage-to-hertz ratio. Space-vector-pulse-width-modulation technique delivers the simulation and theoretical analysis. Moreover, PV cells drive the induction motors with a wide speed range. Simulation results were validated with practical outcomes.
{"title":"PV System Based Speed Control of Induction Motor by Space Vector Pulse Width Modulation","authors":"L. Mohammed, Salsabeel H. Taha","doi":"10.1109/ICOASE56293.2022.10075608","DOIUrl":"https://doi.org/10.1109/ICOASE56293.2022.10075608","url":null,"abstract":"This work uses green energy to solve the problem of lacking electrical energy in agricultural areas. Thus, it suggests using photovoltaic systems to supply the engines used in irrigation in these areas where the load is variable. In addition, this work uses a three-phase inverter to achieve the lowest possible total harmonic distortion. Furthermore, the motor's torque is fixed at all speeds, while the volt-to-hertz ratio speed control of the induction motor is discussed in this paper. By varying the modulation index of the space-vector-pulse-width, the stator voltage of the induction motor can differ correspondingly. The time duration of gate pulses is changed to maintain the constant voltage-to-hertz ratio. Space-vector-pulse-width-modulation technique delivers the simulation and theoretical analysis. Moreover, PV cells drive the induction motors with a wide speed range. Simulation results were validated with practical outcomes.","PeriodicalId":297211,"journal":{"name":"2022 4th International Conference on Advanced Science and Engineering (ICOASE)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123593191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-21DOI: 10.1109/ICOASE56293.2022.10075591
B. H. Ali, N. Sulaiman, S.A.R. Al-Haddad, R. Atan, S. L. Hassan
Distributed Denial of services (DDoS) attack is one of the most dangerous attacks that targeted servers. The main consequence of this attack is to prevent users from getting their legitimate services by bringing down targeted victim. CICFlowMeter tool generates bi-directional flows from packets. Each flow generates 83 of different features. The research focuses on 8 features which are active min (f1), active mean (f2), active max (f3), active std (f4), idle min (f5), idle mean (f6), idle max (f7), and idle std (f8). CICFlowMeter tool has several problems that affected on the detection accuracy of DDoS attacks. The idle and active based feature of Shannon entropy and sequential probability ratio test (SE-SPRT) approach was implemented in this research. The problems of original CICFlowMeter were presented, and the differences between original and revised version of CICFlowMeter tool were explored. The DARPA database and confusion matrix were used to evaluate the detection technique and present the comparison between two versions of CICFlowMeter. The detection method detected neptune and smurf attacks and had higher accuracy, f1-score, sensitivity, specificity, and precision when revised version of CICFlowMeter used to generate flows. However, the detection method failed to detect neptune attack and had higher miss-rate, lower accuracy, lower f1-score, and lower specificity, and lower precision when original version used in generating flows.
{"title":"DDoS Detection Using Active and Idle Features of Revised CICFlowMeter and Statistical Approaches","authors":"B. H. Ali, N. Sulaiman, S.A.R. Al-Haddad, R. Atan, S. L. Hassan","doi":"10.1109/ICOASE56293.2022.10075591","DOIUrl":"https://doi.org/10.1109/ICOASE56293.2022.10075591","url":null,"abstract":"Distributed Denial of services (DDoS) attack is one of the most dangerous attacks that targeted servers. The main consequence of this attack is to prevent users from getting their legitimate services by bringing down targeted victim. CICFlowMeter tool generates bi-directional flows from packets. Each flow generates 83 of different features. The research focuses on 8 features which are active min (f1), active mean (f2), active max (f3), active std (f4), idle min (f5), idle mean (f6), idle max (f7), and idle std (f8). CICFlowMeter tool has several problems that affected on the detection accuracy of DDoS attacks. The idle and active based feature of Shannon entropy and sequential probability ratio test (SE-SPRT) approach was implemented in this research. The problems of original CICFlowMeter were presented, and the differences between original and revised version of CICFlowMeter tool were explored. The DARPA database and confusion matrix were used to evaluate the detection technique and present the comparison between two versions of CICFlowMeter. The detection method detected neptune and smurf attacks and had higher accuracy, f1-score, sensitivity, specificity, and precision when revised version of CICFlowMeter used to generate flows. However, the detection method failed to detect neptune attack and had higher miss-rate, lower accuracy, lower f1-score, and lower specificity, and lower precision when original version used in generating flows.","PeriodicalId":297211,"journal":{"name":"2022 4th International Conference on Advanced Science and Engineering (ICOASE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121438597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-21DOI: 10.1109/ICOASE56293.2022.10075588
F. E. Samann, Shavan K. Askar
Selecting the correct cluster number for K-Clustering algorithms such as K-Medoids is essential for optimal output. The Elbow and Silhouette methods are usually used to select the optimal K number for clustering. However, the high computational complexity makes these methods inefficient in Vehicular Network (VN) environment. Therefore, an efficient K estimating technique is essential for an effective VN clustering scheme. K-medoids algorithm is a Machine Learning clustering algorithm usually implemented by the road infrastructure in the VN. The algorithm selects cluster medoids that minimize the sum of dissimilarities between cluster members and their respective medoids. This paper proposes using Scott's histogram formula for bin numbers to calculate the optimal K number. Estimating the underlying probability density function of the data can give a good approximation of the K number for the K-Medoids algorithm. The clustering algorithm is simulated using OMNET++ and Veins simulators in a VN environment. Using Scott's formula, picking the optimal K number is evaluated against the Elbow method in different traffic density and vehicular speed scenarios. Scott's formula gave a close estimate of the K number when implemented using vehicle coordinates.
{"title":"Estimating The Optimal Cluster Number For Vehicular Network Using Scott's Formula","authors":"F. E. Samann, Shavan K. Askar","doi":"10.1109/ICOASE56293.2022.10075588","DOIUrl":"https://doi.org/10.1109/ICOASE56293.2022.10075588","url":null,"abstract":"Selecting the correct cluster number for K-Clustering algorithms such as K-Medoids is essential for optimal output. The Elbow and Silhouette methods are usually used to select the optimal K number for clustering. However, the high computational complexity makes these methods inefficient in Vehicular Network (VN) environment. Therefore, an efficient K estimating technique is essential for an effective VN clustering scheme. K-medoids algorithm is a Machine Learning clustering algorithm usually implemented by the road infrastructure in the VN. The algorithm selects cluster medoids that minimize the sum of dissimilarities between cluster members and their respective medoids. This paper proposes using Scott's histogram formula for bin numbers to calculate the optimal K number. Estimating the underlying probability density function of the data can give a good approximation of the K number for the K-Medoids algorithm. The clustering algorithm is simulated using OMNET++ and Veins simulators in a VN environment. Using Scott's formula, picking the optimal K number is evaluated against the Elbow method in different traffic density and vehicular speed scenarios. Scott's formula gave a close estimate of the K number when implemented using vehicle coordinates.","PeriodicalId":297211,"journal":{"name":"2022 4th International Conference on Advanced Science and Engineering (ICOASE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127190646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-21DOI: 10.1109/ICOASE56293.2022.10075585
J. Saeed, A. Abdulazeez, D. Ibrahim
While beauty is subjective, it is not easy to quantify. Assessing facial beauty based on a computer perspective is an emerging research area with various applications. Different trainable models have been proposed to identify the attractiveness of facial beauty utilizing different types of features, machine learning techniques and lately, convolutional neural networks (CNNs) have proven their efficiency in image classification. The main objective of recent previous work is to enhance the performance of the existing trainable methods and make them suitable for beauty attractiveness identification. In this study, the accuracy and effectiveness of four affective pre-trained CNNs models (AlexNet, GoogleNet, ResNet-50, and VGG16) in assessing the attractiveness of human facial images using the CelebA dataset have been explored, evaluated, and analyzed. The results demonstrate that GoogleNet surpassed the investigated pre-trained networks with a performance accuracy of 82.8%.
{"title":"2D Facial Images Attractiveness Assessment Based on Transfer Learning of Deep Convolutional Neural Networks","authors":"J. Saeed, A. Abdulazeez, D. Ibrahim","doi":"10.1109/ICOASE56293.2022.10075585","DOIUrl":"https://doi.org/10.1109/ICOASE56293.2022.10075585","url":null,"abstract":"While beauty is subjective, it is not easy to quantify. Assessing facial beauty based on a computer perspective is an emerging research area with various applications. Different trainable models have been proposed to identify the attractiveness of facial beauty utilizing different types of features, machine learning techniques and lately, convolutional neural networks (CNNs) have proven their efficiency in image classification. The main objective of recent previous work is to enhance the performance of the existing trainable methods and make them suitable for beauty attractiveness identification. In this study, the accuracy and effectiveness of four affective pre-trained CNNs models (AlexNet, GoogleNet, ResNet-50, and VGG16) in assessing the attractiveness of human facial images using the CelebA dataset have been explored, evaluated, and analyzed. The results demonstrate that GoogleNet surpassed the investigated pre-trained networks with a performance accuracy of 82.8%.","PeriodicalId":297211,"journal":{"name":"2022 4th International Conference on Advanced Science and Engineering (ICOASE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122267977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-21DOI: 10.1109/ICOASE56293.2022.10075590
Arqam M. Shareef, Khalil H. Sayidmarie
This paper proposes a compact reconfigurable filter that uses a resonant element in the form of a folded slot. The U-shaped slot is embedded into the 50 Ohm microstrip line. Due to folding, the slot length is reduced to 1/4 the effective wavelength leading to considerable miniaturization compared with the filters that use resonant elements like rings or coupled open-circuited or short-circuited stubs. The presented design example at the WLAN frequency of 2.45 GHz can be configured between a band-reject and all-pass states, by placing a PIN diode at the centre of the slot. The proposed filter was investigated using the CST Studio Suite Software - 3D Electromagnetic Simulation. The obtained results showed low insertion loss in the band-pass state and high rejection in the stopband state. The tests of the fabricated prototype showed comparable results to the simulation one thus verifying the design.
本文提出了一种紧凑的可重构滤波器，该滤波器采用折叠槽形式的谐振元件。u型槽嵌入50欧姆微带线中。由于折叠，狭缝长度减少到有效波长的1/4，与使用环形或耦合开路或短路插脚等谐振元件的滤波器相比，具有相当的小型化。通过在插槽中心放置PIN二极管，可以将WLAN频率为2.45 GHz的设计示例配置为带阻和全通状态。利用CST Studio Suite软件- 3D电磁仿真对所提出的滤波器进行了研究。结果表明，带通状态下插入损耗低，阻带状态下抑制效果好。制造样机的试验结果与仿真结果相当，从而验证了设计的正确性。
{"title":"Compact Reconfigurable Band-Reject/All-Pass Microstrip Filter Using U-Shaped Slot","authors":"Arqam M. Shareef, Khalil H. Sayidmarie","doi":"10.1109/ICOASE56293.2022.10075590","DOIUrl":"https://doi.org/10.1109/ICOASE56293.2022.10075590","url":null,"abstract":"This paper proposes a compact reconfigurable filter that uses a resonant element in the form of a folded slot. The U-shaped slot is embedded into the 50 Ohm microstrip line. Due to folding, the slot length is reduced to 1/4 the effective wavelength leading to considerable miniaturization compared with the filters that use resonant elements like rings or coupled open-circuited or short-circuited stubs. The presented design example at the WLAN frequency of 2.45 GHz can be configured between a band-reject and all-pass states, by placing a PIN diode at the centre of the slot. The proposed filter was investigated using the CST Studio Suite Software - 3D Electromagnetic Simulation. The obtained results showed low insertion loss in the band-pass state and high rejection in the stopband state. The tests of the fabricated prototype showed comparable results to the simulation one thus verifying the design.","PeriodicalId":297211,"journal":{"name":"2022 4th International Conference on Advanced Science and Engineering (ICOASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130757521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-21DOI: 10.1109/ICOASE56293.2022.10075594
Hussain Hamdi Khalaf, A. Mohammad, A. Hussain, Z. S. Al-sagar
An artificial neural network (ANN) with backward-propagation technique was used to predict the power generation of PV module in sunny and cloudy weathers of Baghdad city-Iraq. Experiment tests were investigated in winter and summer days to get the best sunny and cloudy days. Three weather parameters were measured including: solar irradiance, ambient temperature and wind speed. In addition, the output electrical characteristics of PV module (voltage, current, power) and module temperature were measured. Therefore, the dataset of ANN system consists of four input and one output. Furthermore, the structure of ANN includes single and double hidden layers with backward propagation technique. Besides, number of neurons were optimized in training process. The evaluation of the ANN model was depended on determination coefficient (R) and Mean Squared Error (MSE). The obtained results show that the architecture of ANNs is appropriated for predicting the power generated from PV module. The two developed ANN models have good accuracy and the sunny model is relatively more accurate than the cloudy model. Where, the MSE is 0.002062 at epoch 6 in sunny model and 0.0087085 at epoch 9 in cloudy model. Furthermore, the R is recorded 0.993 and 0.982 in validation process for sunny and cloudy model respectively. In addition, the optimization number of neurons in hidden layer gave sufficient accuracy without referring to choose the neurons by trial and error.
{"title":"Comparison and Assessment of PV Module Power Prediction Based on ANN for Iraq Weather","authors":"Hussain Hamdi Khalaf, A. Mohammad, A. Hussain, Z. S. Al-sagar","doi":"10.1109/ICOASE56293.2022.10075594","DOIUrl":"https://doi.org/10.1109/ICOASE56293.2022.10075594","url":null,"abstract":"An artificial neural network (ANN) with backward-propagation technique was used to predict the power generation of PV module in sunny and cloudy weathers of Baghdad city-Iraq. Experiment tests were investigated in winter and summer days to get the best sunny and cloudy days. Three weather parameters were measured including: solar irradiance, ambient temperature and wind speed. In addition, the output electrical characteristics of PV module (voltage, current, power) and module temperature were measured. Therefore, the dataset of ANN system consists of four input and one output. Furthermore, the structure of ANN includes single and double hidden layers with backward propagation technique. Besides, number of neurons were optimized in training process. The evaluation of the ANN model was depended on determination coefficient (R) and Mean Squared Error (MSE). The obtained results show that the architecture of ANNs is appropriated for predicting the power generated from PV module. The two developed ANN models have good accuracy and the sunny model is relatively more accurate than the cloudy model. Where, the MSE is 0.002062 at epoch 6 in sunny model and 0.0087085 at epoch 9 in cloudy model. Furthermore, the R is recorded 0.993 and 0.982 in validation process for sunny and cloudy model respectively. In addition, the optimization number of neurons in hidden layer gave sufficient accuracy without referring to choose the neurons by trial and error.","PeriodicalId":297211,"journal":{"name":"2022 4th International Conference on Advanced Science and Engineering (ICOASE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114465762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-21DOI: 10.1109/ICOASE56293.2022.10075606
Omar M. Malallah
Despite the development of identity detection using biometrics in the field of financial transactions, the handwritten signature remains the most commonly used to this day. The main challenge is that each person's signature may be distinctive, on the other hand, many difficulties aroused because two signatures created by the same individual may appear to be extremely identical. This similarity allows the imposters to claim a forged identity. In this paper, an off-line handwritten forgery detection method is introduced using traditional machine learning rather than deep learning methods to fulfill the need for a simpler model for saving both computation time and computation resources. The proposed method uses Histogram of Gradients (HOG) as a feature extraction method and Principal Component Analysis (PCA) to reduce the large extracted features number and Support Vector Machine (SVM) as a classifier. Another approach has been used by using Boruta feature selection for further reduction of feature numbers. CEDAR dataset has been used in this paper and the results were 99.24 % and 98.79 % in terms of accuracy for the two proposed methods respectively.
尽管在金融交易领域使用生物识别技术进行身份检测，但手写签名至今仍是最常用的。主要的挑战是每个人的签名可能是不同的，另一方面，由于同一个人创建的两个签名可能看起来非常相同，因此引起了许多困难。这种相似性使得冒名顶替者可以申请伪造的身份。本文提出了一种离线手写伪造检测方法，采用传统的机器学习方法代替深度学习方法，以满足简化模型以节省计算时间和计算资源的需要。该方法采用梯度直方图(Histogram of Gradients, HOG)作为特征提取方法，主成分分析(Principal Component Analysis, PCA)减少提取的特征数量，支持向量机(Support Vector Machine, SVM)作为分类器。另一种方法是使用Boruta特征选择来进一步减少特征数。本文使用CEDAR数据集，两种方法的准确率分别为99.24%和98.79%。
{"title":"Handwritten Signature Forgery Detection Using PCA and Boruta Feature Selection","authors":"Omar M. Malallah","doi":"10.1109/ICOASE56293.2022.10075606","DOIUrl":"https://doi.org/10.1109/ICOASE56293.2022.10075606","url":null,"abstract":"Despite the development of identity detection using biometrics in the field of financial transactions, the handwritten signature remains the most commonly used to this day. The main challenge is that each person's signature may be distinctive, on the other hand, many difficulties aroused because two signatures created by the same individual may appear to be extremely identical. This similarity allows the imposters to claim a forged identity. In this paper, an off-line handwritten forgery detection method is introduced using traditional machine learning rather than deep learning methods to fulfill the need for a simpler model for saving both computation time and computation resources. The proposed method uses Histogram of Gradients (HOG) as a feature extraction method and Principal Component Analysis (PCA) to reduce the large extracted features number and Support Vector Machine (SVM) as a classifier. Another approach has been used by using Boruta feature selection for further reduction of feature numbers. CEDAR dataset has been used in this paper and the results were 99.24 % and 98.79 % in terms of accuracy for the two proposed methods respectively.","PeriodicalId":297211,"journal":{"name":"2022 4th International Conference on Advanced Science and Engineering (ICOASE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122520255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-21DOI: 10.1109/ICOASE56293.2022.10075583
Z. Ageed, Subhi R. M. Zeebaree, R. Saeed
Even while the Internet of Things (IoT) already affects our day-to-day activities, its future relevance and potential for transformation remain untapped. Security issues with present communications technology must be solved before secure end-to-end connection between services can be achieved. Internet of Things (IoT) is here to stay. In the next years, it will become an integral part of our everyday life. There is a good chance that sensor-based networks can recognize it as a direct service provider in our surroundings. Even if it's just in the form of a value-added service delivered through cellular networks, it's going to be helpful nevertheless. It is, nevertheless, subject to a wide range of security threats. The current level of security is insufficient for IoT applications in the future. A secure cryptosystem is needed for the Internet of Things. quantum-based security has seen a surge of attention recently. Additional quantum key distribution systems and network services are now supported by this solution. As a result of the quantum computing's unrivaled security level, it has become more popular in recent years. Because every measurement must affect the state of the quantum bit being sent, quantum physics dictates that this must be the case. Regardless of whether the sender or recipient is aware of the change, it is clear. It is thus no longer possible to listen passively. Polarized photons may encode a string of bits using protocols like BB84. Secure cryptographic keys may be generated over an unsafe channel utilizing various key distillation procedures.
{"title":"Influence of Quantum Computing on IoT Using Modern Algorithms","authors":"Z. Ageed, Subhi R. M. Zeebaree, R. Saeed","doi":"10.1109/ICOASE56293.2022.10075583","DOIUrl":"https://doi.org/10.1109/ICOASE56293.2022.10075583","url":null,"abstract":"Even while the Internet of Things (IoT) already affects our day-to-day activities, its future relevance and potential for transformation remain untapped. Security issues with present communications technology must be solved before secure end-to-end connection between services can be achieved. Internet of Things (IoT) is here to stay. In the next years, it will become an integral part of our everyday life. There is a good chance that sensor-based networks can recognize it as a direct service provider in our surroundings. Even if it's just in the form of a value-added service delivered through cellular networks, it's going to be helpful nevertheless. It is, nevertheless, subject to a wide range of security threats. The current level of security is insufficient for IoT applications in the future. A secure cryptosystem is needed for the Internet of Things. quantum-based security has seen a surge of attention recently. Additional quantum key distribution systems and network services are now supported by this solution. As a result of the quantum computing's unrivaled security level, it has become more popular in recent years. Because every measurement must affect the state of the quantum bit being sent, quantum physics dictates that this must be the case. Regardless of whether the sender or recipient is aware of the change, it is clear. It is thus no longer possible to listen passively. Polarized photons may encode a string of bits using protocols like BB84. Secure cryptographic keys may be generated over an unsafe channel utilizing various key distillation procedures.","PeriodicalId":297211,"journal":{"name":"2022 4th International Conference on Advanced Science and Engineering (ICOASE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122542159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-21DOI: 10.1109/ICOASE56293.2022.10075593
M. Baykara, Awf Abdulrahman, Ali Shakir Alahmed
In information systems, it has become very important to store personal and institutional information and access it safely and quickly when necessary. To ensure the confidentiality of information against unauthorized access, institutions or organizations must protect their important data securely and take various precautions. Intrusion detection systems (IDS) are among these measures. One of the issues that should be carefully considered while creating an IDS is the dataset to be used. In terms of IDS, a dataset is the data obtained from network packets or log records that contain attack data and are necessary to identify attack patterns during the training and testing stages of the system. In this article, widely used machine learning techniques (decision tree, K-nearest neighbor, and support vector machine algorithms) are used to increase the performance of IDSs. The studies were tested on the NSL-KDD dataset, one of the most used datasets in evaluating IDSs. As a result of the tests, it was seen that the highest accuracy rate was 99.7%, and the lowest accuracy rate was 98.7%. The obtained results have shown that the proposed machine learning methods can be used with high sensitivity and accuracy to develop smart IDSs.
{"title":"Classification of Network Data with Machine Learning Methods for Intelligent Intrusion Detection Systems","authors":"M. Baykara, Awf Abdulrahman, Ali Shakir Alahmed","doi":"10.1109/ICOASE56293.2022.10075593","DOIUrl":"https://doi.org/10.1109/ICOASE56293.2022.10075593","url":null,"abstract":"In information systems, it has become very important to store personal and institutional information and access it safely and quickly when necessary. To ensure the confidentiality of information against unauthorized access, institutions or organizations must protect their important data securely and take various precautions. Intrusion detection systems (IDS) are among these measures. One of the issues that should be carefully considered while creating an IDS is the dataset to be used. In terms of IDS, a dataset is the data obtained from network packets or log records that contain attack data and are necessary to identify attack patterns during the training and testing stages of the system. In this article, widely used machine learning techniques (decision tree, K-nearest neighbor, and support vector machine algorithms) are used to increase the performance of IDSs. The studies were tested on the NSL-KDD dataset, one of the most used datasets in evaluating IDSs. As a result of the tests, it was seen that the highest accuracy rate was 99.7%, and the lowest accuracy rate was 98.7%. The obtained results have shown that the proposed machine learning methods can be used with high sensitivity and accuracy to develop smart IDSs.","PeriodicalId":297211,"journal":{"name":"2022 4th International Conference on Advanced Science and Engineering (ICOASE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128818138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-21DOI: 10.1109/ICOASE56293.2022.10075576
H. Sadeeq, A. Abdulazeez
Global optimization has been used in many real-world problems. Nature-inspired meta-heuristic algorithms, such as the Northern Goshawk Optimization NGO algorithm that has just been proposed, are often used to solve these kinds of optimization problems. An NGO provides satisfactory results. In this algorithm, the proposed exploration model may not provide sufficient coverage of the problem space, trapping the system in a local optimal solution. To improve the performance of NGO, a novel and efficient improved northern goshawk optimization technique named INGO is proposed in this paper. In INGO, a new concept of switching between exploration and exploitation has been developed to improve overall algorithm performance to avoid being stuck in local optima. Also, to increase search capabilities, Levy Flight is used. Twenty-three known benchmark functions were used to test the performance of the proposed INGO. The results were compared to those of an NGO and some well-known robust algorithms. Experimental data indicates that the INGO suggested in this study consistently outperforms the traditional NGO and alternative methods in a significant number of test functions.
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