Pub Date : 2021-04-01DOI: 10.33166/AETIC.2021.02.007
Khai Dinh Lai, T. Nguyen, T. Le
The development of Computer-aided diagnosis (CAD) systems for automatic lung nodule detection through thoracic computed tomography (CT) scans has been an active area of research in recent years. Lung Nodule Analysis 2016 (LUNA16 challenge) encourages researchers to suggest a variety of successful nodule detection algorithms based on two key stages (1) candidates detection, (2) false-positive reduction. In the scope of this paper, a new convolutional neural network (CNN) architecture is proposed to efficiently solve the second challenge of LUNA16. Specifically, we find that typical CNN models pay little attention to the characteristics of input data, in order to address this constraint, we apply the attention-mechanism: propose a technique to attach Squeeze and Excitation-Block (SE-Block) after each convolution layer of CNN to emphasize important feature maps related to the characteristics of the input image - forming Attention sub-Convnet. The new CNN architecture is suggested by connecting the Attention sub-Convnets. In addition, we also analyze the selection of triplet loss or softmax loss functions to boost the rating performance of the proposed CNN. From the study, this is agreed to select softmax loss during the CNN training phase and triplet loss for the testing phase. Our suggested CNN is used to minimize the number of redundant candidates in order to improve the efficiency of false-positive reduction with the LUNA database. The results obtained in comparison to the previous models indicate the feasibility of the proposed model.
{"title":"Detection of Lung Nodules on CT Images based on the Convolutional Neural Network with Attention Mechanism","authors":"Khai Dinh Lai, T. Nguyen, T. Le","doi":"10.33166/AETIC.2021.02.007","DOIUrl":"https://doi.org/10.33166/AETIC.2021.02.007","url":null,"abstract":"The development of Computer-aided diagnosis (CAD) systems for automatic lung nodule detection through thoracic computed tomography (CT) scans has been an active area of research in recent years. Lung Nodule Analysis 2016 (LUNA16 challenge) encourages researchers to suggest a variety of successful nodule detection algorithms based on two key stages (1) candidates detection, (2) false-positive reduction. In the scope of this paper, a new convolutional neural network (CNN) architecture is proposed to efficiently solve the second challenge of LUNA16. Specifically, we find that typical CNN models pay little attention to the characteristics of input data, in order to address this constraint, we apply the attention-mechanism: propose a technique to attach Squeeze and Excitation-Block (SE-Block) after each convolution layer of CNN to emphasize important feature maps related to the characteristics of the input image - forming Attention sub-Convnet. The new CNN architecture is suggested by connecting the Attention sub-Convnets. In addition, we also analyze the selection of triplet loss or softmax loss functions to boost the rating performance of the proposed CNN. From the study, this is agreed to select softmax loss during the CNN training phase and triplet loss for the testing phase. Our suggested CNN is used to minimize the number of redundant candidates in order to improve the efficiency of false-positive reduction with the LUNA database. The results obtained in comparison to the previous models indicate the feasibility of the proposed model.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48529997","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 : 2021-04-01DOI: 10.33166/AETIC.2021.02.005
U. Hudomalj, C. Mandla, M. Plattner
This paper presents FPGA implementations of image filtering and image averaging – two widely applied image preprocessing algorithms. The implementations are targeted for real time processing of high frame rate and high resolution image streams. The developed implementations are evaluated in terms of resource usage, power consumption, and achievable frame rates. For the evaluation, Microsemi’s Smartfusion2 Advanced Development Kit is used. It includes a SmartFusion2 M2S150 SoC FPGA. The performance of the developed implementation of image filtering algorithm is compared to a solution provided by MATLAB’s Vision HDL Toolbox, which is evaluated on the same platform. The performance of the developed implementations are also compared with FPGA implementations found in existing publications, although those are evaluated on different FPGA platforms. Difficulties with performance comparison between implementations on different platforms are addressed and limitations of processing image streams with FPGA platforms discussed.
{"title":"FPGA Implementations of Algorithms for Preprocessing of High Frame Rate and High Resolution Image Streams in Real Time","authors":"U. Hudomalj, C. Mandla, M. Plattner","doi":"10.33166/AETIC.2021.02.005","DOIUrl":"https://doi.org/10.33166/AETIC.2021.02.005","url":null,"abstract":"This paper presents FPGA implementations of image filtering and image averaging – two widely applied image preprocessing algorithms. The implementations are targeted for real time processing of high frame rate and high resolution image streams. The developed implementations are evaluated in terms of resource usage, power consumption, and achievable frame rates. For the evaluation, Microsemi’s Smartfusion2 Advanced Development Kit is used. It includes a SmartFusion2 M2S150 SoC FPGA. The performance of the developed implementation of image filtering algorithm is compared to a solution provided by MATLAB’s Vision HDL Toolbox, which is evaluated on the same platform. The performance of the developed implementations are also compared with FPGA implementations found in existing publications, although those are evaluated on different FPGA platforms. Difficulties with performance comparison between implementations on different platforms are addressed and limitations of processing image streams with FPGA platforms discussed.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42699029","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 : 2021-03-20DOI: 10.33166/AETIC.2021.05.022
Rehana Thalib, M. Bakar, Nur Fadhilah Ibrahim
Support vector regression (SVR) is well known as a regression or prediction tool under the Machine Learning (ML) which preserves all the key features through the training data. Different from general prediction, here, we proposed SVR to predict the new approximate solutions after we generated some iterates using an iterative method called Lanczos algorithm, one class of Krylov solvers. As we know that all Krylov solvers, including Lanczos methods, for solving the high dimensions of systems of linear equations (SLEs) problems experiences breakdown which causes the sequence of the iterates is incomplete, or the good approximate solution is never reached. By assuming that some iterates exist after the breakdown, then we could predict what they are. It is realized by learning the previous iterates generated by the Lanczos solvers, which is also called the training data. The SVR is then used to predict the next iterate which is expected the sequence now has similar property as the previous one before breaking down. Furthermore, we implemented the hybrid SVR-Lanczos (or SVR-L) in the restarting frame work, then it is called as hybrid restarting-SVR-L. The idea behind the restarting is that one time running hybrid SVR-L cannot obtain a good approximate solution with small residual norm. By taking one iterate which is resulted by the hybrid SVR-L, putting it as the initial guess, will give us the better solution. To test our idea of prediction of SLEs solutions, we also used the regular regression and compared with the SVR. Numerical results are presented and compared between these two predictors. Lastly, we compared our proposed method with existing interpolation and extrapolation methods to predict the approximate solution of SLEs. The results showed that our restarting SVR-L performed better compared with the regular regression.
{"title":"Application of Support Vector Regression in Krylov Solvers","authors":"Rehana Thalib, M. Bakar, Nur Fadhilah Ibrahim","doi":"10.33166/AETIC.2021.05.022","DOIUrl":"https://doi.org/10.33166/AETIC.2021.05.022","url":null,"abstract":"Support vector regression (SVR) is well known as a regression or prediction tool under the Machine Learning (ML) which preserves all the key features through the training data. Different from general prediction, here, we proposed SVR to predict the new approximate solutions after we generated some iterates using an iterative method called Lanczos algorithm, one class of Krylov solvers. As we know that all Krylov solvers, including Lanczos methods, for solving the high dimensions of systems of linear equations (SLEs) problems experiences breakdown which causes the sequence of the iterates is incomplete, or the good approximate solution is never reached. By assuming that some iterates exist after the breakdown, then we could predict what they are. It is realized by learning the previous iterates generated by the Lanczos solvers, which is also called the training data. The SVR is then used to predict the next iterate which is expected the sequence now has similar property as the previous one before breaking down. Furthermore, we implemented the hybrid SVR-Lanczos (or SVR-L) in the restarting frame work, then it is called as hybrid restarting-SVR-L. The idea behind the restarting is that one time running hybrid SVR-L cannot obtain a good approximate solution with small residual norm. By taking one iterate which is resulted by the hybrid SVR-L, putting it as the initial guess, will give us the better solution. To test our idea of prediction of SLEs solutions, we also used the regular regression and compared with the SVR. Numerical results are presented and compared between these two predictors. Lastly, we compared our proposed method with existing interpolation and extrapolation methods to predict the approximate solution of SLEs. The results showed that our restarting SVR-L performed better compared with the regular regression.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43615902","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 : 2021-03-20DOI: 10.33166/AETIC.2021.05.025
Adnan Helmi Azizan, S. Mostafa, Aida Mustapha, Cik Feresa Mohd Foozy, M. Wahab, M. Mohammed, Bashar Ahmad Khalaf
Intrusion detection systems (IDS) are used in analyzing huge data and diagnose anomaly traffic such as DDoS attack; thus, an efficient traffic classification method is necessary for the IDS. The IDS models attempt to decrease false alarm and increase true alarm rates in order to improve the performance accuracy of the system. To resolve this concern, three machine learning algorithms have been tested and evaluated in this research which are decision jungle (DJ), random forest (RF) and support vector machine (SVM). The main objective is to propose a ML-based network intrusion detection system (ML-based NIDS) model that compares the performance of the three algorithms based on their accuracy and precision of anomaly traffics. The knowledge discovery in databases (KDD) methodology and intrusion detection evaluation dataset (CIC-IDS2017) are used in the testing which both are considered as a benchmark in the evaluation of IDS. The average accuracy results of the SVM is 98.18%, RF is 96.76% and DJ is 96.50% in which the highest accuracy is achieved by the SVM. The average precision results of the SVM is 98.74, RF is 97.96 and DJ is 97.82 in which the SVM got a higher average precision compared with the other two algorithms. The average recall results of the SVM is 95.63, RF is 97.62 and DJ is 95.77 in which the RF achieves the highest average of recall than SVM and DJ. In overall, the SVM algorithm is found to be the best algorithm that can be used to detect an intrusion in the system.
入侵检测系统（IDS）用于分析海量数据和诊断DDoS攻击等异常流量；因此，一种有效的流量分类方法对于IDS来说是必要的。IDS模型试图减少误报并提高真实报警率，以提高系统的性能准确性。为了解决这一问题，本研究对决策丛林（DJ）、随机森林（RF）和支持向量机（SVM）三种机器学习算法进行了测试和评估。主要目标是提出一种基于ML的网络入侵检测系统（ML based NIDS）模型，该模型基于三种算法对异常流量的准确性和精度来比较它们的性能。测试中使用了数据库中的知识发现（KDD）方法和入侵检测评估数据集（CIC-IDS2017），这两种方法都被视为IDS评估的基准。SVM的平均准确率为98.18%，RF为96.76%，DJ为96.50%，其中SVM的准确率最高。SVM的平均精度为98.74，RF为97.96，DJ为97.82，与其他两种算法相比，SVM获得了更高的平均精度。SVM的平均召回率为95.63，RF为97.62，DJ为95.77，其中RF的召回率平均值高于SVM和DJ。总体而言，SVM算法是可用于检测系统中入侵的最佳算法。
{"title":"A Machine Learning Approach for Improving the Performance of Network Intrusion Detection Systems","authors":"Adnan Helmi Azizan, S. Mostafa, Aida Mustapha, Cik Feresa Mohd Foozy, M. Wahab, M. Mohammed, Bashar Ahmad Khalaf","doi":"10.33166/AETIC.2021.05.025","DOIUrl":"https://doi.org/10.33166/AETIC.2021.05.025","url":null,"abstract":"Intrusion detection systems (IDS) are used in analyzing huge data and diagnose anomaly traffic such as DDoS attack; thus, an efficient traffic classification method is necessary for the IDS. The IDS models attempt to decrease false alarm and increase true alarm rates in order to improve the performance accuracy of the system. To resolve this concern, three machine learning algorithms have been tested and evaluated in this research which are decision jungle (DJ), random forest (RF) and support vector machine (SVM). The main objective is to propose a ML-based network intrusion detection system (ML-based NIDS) model that compares the performance of the three algorithms based on their accuracy and precision of anomaly traffics. The knowledge discovery in databases (KDD) methodology and intrusion detection evaluation dataset (CIC-IDS2017) are used in the testing which both are considered as a benchmark in the evaluation of IDS. The average accuracy results of the SVM is 98.18%, RF is 96.76% and DJ is 96.50% in which the highest accuracy is achieved by the SVM. The average precision results of the SVM is 98.74, RF is 97.96 and DJ is 97.82 in which the SVM got a higher average precision compared with the other two algorithms. The average recall results of the SVM is 95.63, RF is 97.62 and DJ is 95.77 in which the RF achieves the highest average of recall than SVM and DJ. In overall, the SVM algorithm is found to be the best algorithm that can be used to detect an intrusion in the system.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45043426","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 : 2021-03-20DOI: 10.33166/AETIC.2021.05.015
Ahmad Affandi Supli
Human anatomy is a biology field that studies human body which consists of intricate and complex piece of engineering in which every assembly has an important role. This subject is considered to be very complex and thus need an advanced technology to help users learning this course more effectively. In this study, we propose and report our research strategy and progress to build a constructive play on human anatomy system based on finger motion gesture of Leap Motion controller (LMC). This LMC device can detect hand gestures and fingers’ motion and translate it into interaction input. Then, we utilize holographic display to portray our 3D human anatomy as its output. In detail, the research strategy of this paper consists of research plan, general framework and general architecture of the developed system. Then, we also present our current development of constructive anatomy learning system. In this future, we will discuss in more detail about the development stage.
{"title":"Research Strategy: A Constructive Play for Anatomy Learning System Based on Human Finger Gestures on Holographic Display","authors":"Ahmad Affandi Supli","doi":"10.33166/AETIC.2021.05.015","DOIUrl":"https://doi.org/10.33166/AETIC.2021.05.015","url":null,"abstract":"Human anatomy is a biology field that studies human body which consists of intricate and complex piece of engineering in which every assembly has an important role. This subject is considered to be very complex and thus need an advanced technology to help users learning this course more effectively. In this study, we propose and report our research strategy and progress to build a constructive play on human anatomy system based on finger motion gesture of Leap Motion controller (LMC). This LMC device can detect hand gestures and fingers’ motion and translate it into interaction input. Then, we utilize holographic display to portray our 3D human anatomy as its output. In detail, the research strategy of this paper consists of research plan, general framework and general architecture of the developed system. Then, we also present our current development of constructive anatomy learning system. In this future, we will discuss in more detail about the development stage.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42128831","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 : 2021-01-01DOI: 10.1007/978-3-030-90016-8
{"title":"Emerging Technologies in Computing: 4th EAI/IAER International Conference, iCETiC 2021, Virtual Event, August 18–19, 2021, Proceedings","authors":"","doi":"10.1007/978-3-030-90016-8","DOIUrl":"https://doi.org/10.1007/978-3-030-90016-8","url":null,"abstract":"","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":"307 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83329998","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 : 2021-01-01DOI: 10.33166/10.33166/aetic.2021.01.005
Adnan Ahmed, Abdul Majeed Shaikh, Muhammad Fawad Shaikh, S. Shaikh, J. Soomro
Induction motors are widely used from home to industrial applications. Speed of induction motor plays important role, so to control the speed of induction motor various techniques are adopted and one of these techniques is V/F control, which is adopted in this paper. This technique helps to control the speed in open control system in RPM. Moreover, Control is designed in LabVIEW, it is quite helpful to develop the circuit graphically and code is automatically written in the background to run on Field Programmable Gate Array (FPGA). The aim of this research is to study the impacts on diverse parameters during speed control of three phase induction machine with manipulation of GPIC. Solar technology is used as input source to drive the General-Purpose Inverter Controller (GPIC). Apart of this, impacts of modulation index and carrier frequency influencing the active, reactive and apparent power, temperature and power quality and current overshoot is analysed. MATLAB/Simulink and LabVIEW tools are used for simulation and results along with GPIC, Induction motor and solar panel as hardware.
{"title":"Experimental Study of Various Parameters during Speed Control of Three-phase Induction Motor Using GPIC and LabVIEW","authors":"Adnan Ahmed, Abdul Majeed Shaikh, Muhammad Fawad Shaikh, S. Shaikh, J. Soomro","doi":"10.33166/10.33166/aetic.2021.01.005","DOIUrl":"https://doi.org/10.33166/10.33166/aetic.2021.01.005","url":null,"abstract":"Induction motors are widely used from home to industrial applications. Speed of induction motor plays important role, so to control the speed of induction motor various techniques are adopted and one of these techniques is V/F control, which is adopted in this paper. This technique helps to control the speed in open control system in RPM. Moreover, Control is designed in LabVIEW, it is quite helpful to develop the circuit graphically and code is automatically written in the background to run on Field Programmable Gate Array (FPGA). The aim of this research is to study the impacts on diverse parameters during speed control of three phase induction machine with manipulation of GPIC. Solar technology is used as input source to drive the General-Purpose Inverter Controller (GPIC). Apart of this, impacts of modulation index and carrier frequency influencing the active, reactive and apparent power, temperature and power quality and current overshoot is analysed. MATLAB/Simulink and LabVIEW tools are used for simulation and results along with GPIC, Induction motor and solar panel as hardware.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42416437","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 : 2020-12-20DOI: 10.33166/aetic.2020.05.005
Muhammad Reazul Haque, S. C. Tan, Z. Yusoff, K. Nisar, C. K. Lee, R. Kaspin, B. S. Chowdhry, S. Ali, Shuaib K. Memon
Security issues like Distributed Denial of Service (DDoS) attacks are becoming the main threat for Software-Defined Networking (SDN). Controller placement is a fundamental factor in the design and planning of SDN infrastructure. The controller could be seen as a single dot of failure for the whole SDN and it's the alluring point for DDoS attack. Single controller placement implies a single point of SDN control. So, there is a very high chance to fail the entire network topology as the controller associated with all switches. As a result, legitimate clients won't have the capacity to use SDN services. This is the reason why the controller is the suitable center dot of attack for the aggressor. To protect SDN from this type of single purpose of failure, it is essential to place multiple smart backup controllers to guarantee the SDN operation. In this paper, we propose a novel Integer Linear Programming (ILP) model to optimize the security issue by placing powerful smart backup controller. Result obtained from the simulation shows that our proposed novel ILP model can suggest single or multiple smart backup controller placement to support several ordinary victim controllers which has the capacity to save the cost of multiple ordinary controllers by sharing link, maximum new flows per second of controller and port, etc.
{"title":"A Novel DDoS Attack-aware Smart Backup Controller Placement in SDN Design","authors":"Muhammad Reazul Haque, S. C. Tan, Z. Yusoff, K. Nisar, C. K. Lee, R. Kaspin, B. S. Chowdhry, S. Ali, Shuaib K. Memon","doi":"10.33166/aetic.2020.05.005","DOIUrl":"https://doi.org/10.33166/aetic.2020.05.005","url":null,"abstract":"Security issues like Distributed Denial of Service (DDoS) attacks are becoming the main threat for Software-Defined Networking (SDN). Controller placement is a fundamental factor in the design and planning of SDN infrastructure. The controller could be seen as a single dot of failure for the whole SDN and it's the alluring point for DDoS attack. Single controller placement implies a single point of SDN control. So, there is a very high chance to fail the entire network topology as the controller associated with all switches. As a result, legitimate clients won't have the capacity to use SDN services. This is the reason why the controller is the suitable center dot of attack for the aggressor. To protect SDN from this type of single purpose of failure, it is essential to place multiple smart backup controllers to guarantee the SDN operation. In this paper, we propose a novel Integer Linear Programming (ILP) model to optimize the security issue by placing powerful smart backup controller. Result obtained from the simulation shows that our proposed novel ILP model can suggest single or multiple smart backup controller placement to support several ordinary victim controllers which has the capacity to save the cost of multiple ordinary controllers by sharing link, maximum new flows per second of controller and port, etc.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46671309","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 : 2020-12-20DOI: 10.33166/aetic.2020.05.001
Tanweer Alam, Baha Rababah, Arshad Ali, S. Qamar
The Internet of Things (IoT) has revolutionized innovation to collect and store the information received from physical objects or sensors. The smart devices are linked to a repository that stores intelligent information executed by sensors on IoT-based smart objects. Now, the IoT is shifted from knowledge-based technologies to operational-based technologies. The IoT integrates sensors, smart devices, and a smart grid of implementations to deliver smart strategies. Nowadays, the IoT has been pondered to be an essential technology. The transmission of information to or from the cloud has recently been found to cause many network problems to include latency, power usage, security, privacy, etc. The distributed intelligence enables IoT to help the correct communication available at the correct time and correct place. Distributed Intelligence could strengthen the IoT in a variety of ways, including evaluating the integration of different big data or enhancing efficiency and distribution in huge IoT operations. While evaluating distributed intelligence in the IoT paradigm, the implementation of distributed intelligence services should take into consideration the transmission delay and bandwidth requirements of the network. In this article, the distributed intelligence at the Edge on IoT Networks, applications, opportunities, challenges and future scopes have been presented.
{"title":"Distributed Intelligence at the Edge on IoT Networks","authors":"Tanweer Alam, Baha Rababah, Arshad Ali, S. Qamar","doi":"10.33166/aetic.2020.05.001","DOIUrl":"https://doi.org/10.33166/aetic.2020.05.001","url":null,"abstract":"The Internet of Things (IoT) has revolutionized innovation to collect and store the information received from physical objects or sensors. The smart devices are linked to a repository that stores intelligent information executed by sensors on IoT-based smart objects. Now, the IoT is shifted from knowledge-based technologies to operational-based technologies. The IoT integrates sensors, smart devices, and a smart grid of implementations to deliver smart strategies. Nowadays, the IoT has been pondered to be an essential technology. The transmission of information to or from the cloud has recently been found to cause many network problems to include latency, power usage, security, privacy, etc. The distributed intelligence enables IoT to help the correct communication available at the correct time and correct place. Distributed Intelligence could strengthen the IoT in a variety of ways, including evaluating the integration of different big data or enhancing efficiency and distribution in huge IoT operations. While evaluating distributed intelligence in the IoT paradigm, the implementation of distributed intelligence services should take into consideration the transmission delay and bandwidth requirements of the network. In this article, the distributed intelligence at the Edge on IoT Networks, applications, opportunities, challenges and future scopes have been presented.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41800740","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 : 2020-12-20DOI: 10.33166/aetic.2020.05.004
R. Ramadan, Kusum Yadav
Nowadays, IoT has been widely used in different applications to improve the quality of life. However, the IoT becomes increasingly an ideal target for unauthorized attacks due to its large number of objects, openness, and distributed nature. Therefore, to maintain the security of IoT systems, there is a need for an efficient Intrusion Detection System (IDS). IDS implements detectors that continuously monitor the network traffic. There are various IDs methods proposed in the literature for IoT security. However, the existing methods had the disadvantages in terms of detection accuracy and time overhead. To enhance the IDS detection accuracy and reduces the required time, this paper proposes a hybrid IDS system where a pre-processing phase is utilized to reduce the required time and feature selection as well as the classification is done in a separate stage. The feature selection process is done by using the Enhanced Shuffled Frog Leaping (ESFL) algorithm and the selected features are classified using Light Convolutional Neural Network with Gated Recurrent Neural Network (LCNN-GRNN) algorithm. This two-stage method is compared to up-to-date methods used for intrusion detection and it over performs them in terms of accuracy and running time due to the light processing required by the proposed method.
{"title":"A Novel Hybrid Intrusion Detection System (IDS) for the Detection of Internet of Things (IoT) Network Attacks","authors":"R. Ramadan, Kusum Yadav","doi":"10.33166/aetic.2020.05.004","DOIUrl":"https://doi.org/10.33166/aetic.2020.05.004","url":null,"abstract":"Nowadays, IoT has been widely used in different applications to improve the quality of life. However, the IoT becomes increasingly an ideal target for unauthorized attacks due to its large number of objects, openness, and distributed nature. Therefore, to maintain the security of IoT systems, there is a need for an efficient Intrusion Detection System (IDS). IDS implements detectors that continuously monitor the network traffic. There are various IDs methods proposed in the literature for IoT security. However, the existing methods had the disadvantages in terms of detection accuracy and time overhead. To enhance the IDS detection accuracy and reduces the required time, this paper proposes a hybrid IDS system where a pre-processing phase is utilized to reduce the required time and feature selection as well as the classification is done in a separate stage. The feature selection process is done by using the Enhanced Shuffled Frog Leaping (ESFL) algorithm and the selected features are classified using Light Convolutional Neural Network with Gated Recurrent Neural Network (LCNN-GRNN) algorithm. This two-stage method is compared to up-to-date methods used for intrusion detection and it over performs them in terms of accuracy and running time due to the light processing required by the proposed method.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48889018","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：