Proceedings of the 4th International Conference on Networking, Information Systems & Security最新文献_第4页

A survey on e-voting based on blockchain 基于区块链的电子投票调查

Proceedings of the 4th International Conference on Networking, Information Systems & Security Pub Date : 2021-04-01 DOI: 10.1145/3454127.3457626

Fatih Rabia, Sara Arezki, T. Gadi

引用次数: 4

A Review of Intrusion Detection Systems: Datasets and machine learning methods 入侵检测系统综述:数据集和机器学习方法

Proceedings of the 4th International Conference on Networking, Information Systems & Security Pub Date : 2021-04-01 DOI: 10.1145/3454127.3456576

Aouatif Arqane, Omar Boutkhoum, Hicham Boukhriss, A. Moutaouakkil

{"title":"A Review of Intrusion Detection Systems: Datasets and machine learning methods","authors":"Aouatif Arqane, Omar Boutkhoum, Hicham Boukhriss, A. Moutaouakkil","doi":"10.1145/3454127.3456576","DOIUrl":"https://doi.org/10.1145/3454127.3456576","url":null,"abstract":"At the present time, Security is a crucial issue for all organizations and companies, because intruders are constantly developing new techniques to infiltrate their infrastructure to steal or manipulate sensitive data. Thus, Intrusion Detection System (IDS) has emerged as new technology to protect networks and systems against suspicious activities. Numerous cybersecurity experts highlight the importance of IDS to strength the defensive capacities of systems by alerting for suspicious activities and malicious attacks. Over the years, many techniques like Machine learning (ML) and Deep Learning (DL) have been used to increase the detection accuracy and reduce the false alerts of IDSs. This survey paper presents an overview of some ML and DL algorithms among the most used for IDS. Additionally, because these algorithms depend on the characteristics of malicious events stored in datasets to identify anomalies, we list some publicly available cybersecurity datasets. Furthermore, we highlight the challenges that experts must overcome to enhance the performance of their methods.","PeriodicalId":432206,"journal":{"name":"Proceedings of the 4th International Conference on Networking, Information Systems & Security","volume":"362 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122826283","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}

引用次数: 3

Comparative Study on the protocols used by autonomous car,DSRC, C-V2X, 5G 自动驾驶汽车、DSRC、C-V2X、5G协议的比较研究

Proceedings of the 4th International Conference on Networking, Information Systems & Security Pub Date : 2021-04-01 DOI: 10.1145/3454127.3456616

Jellid Kawtar, T. Mazri

引用次数: 0

Secure user authentication scheme for the Virtual Doctor System enabled H-IoT through 5G Network: A comparison study 基于5G网络的H-IoT虚拟医生系统安全用户认证方案对比研究

Proceedings of the 4th International Conference on Networking, Information Systems & Security Pub Date : 2021-04-01 DOI: 10.1145/3454127.3457624

F. Rougaii, T. Mazri

引用次数: 0

On the Performance of Deep Learning in the Full Edge and the Full Cloud Architectures 全边缘和全云架构下深度学习的性能研究

Proceedings of the 4th International Conference on Networking, Information Systems & Security Pub Date : 2021-04-01 DOI: 10.1145/3454127.3457632

Tajeddine Benbarrad, Marouane Salhaoui, M. Arioua

{"title":"On the Performance of Deep Learning in the Full Edge and the Full Cloud Architectures","authors":"Tajeddine Benbarrad, Marouane Salhaoui, M. Arioua","doi":"10.1145/3454127.3457632","DOIUrl":"https://doi.org/10.1145/3454127.3457632","url":null,"abstract":"Deep learning today surpasses various machine learning approaches in performance and is widely used for variety of different tasks. Deep learning has increased accuracy compared to other approaches for tasks like language translation and image recognition. However, training a deep learning model on a large dataset is a challenging and expensive task that can be time consuming and require large computational resources. Therefore, Different architectures have been proposed for the implementation of deep learning models in machine vision systems to deal with this problem. Currently, the application of deep learning in the cloud is the most common and typical method. Nevertheless, the challenge of having to move the data from where it is generated to a cloud data center so that it can be used to prepare and develop machine learning models represents a major limitation of this approach. As a result, it is becoming increasingly important to consider moving aspects of deep learning to the edge, instead of the cloud, especially with the rapid increase in data volumes and the growing need to act in real time. From this perspective, a comparative study between the full edge and the full cloud architectures based on the performance of the deep learning models implemented in both architectures is elaborated. The results of this study lead us to specify the strengths of both the cloud and the edge for deploying deep learning models, and to choose the optimal architecture to deal with the rapid increase in data volumes and the growing need for real-time action.","PeriodicalId":432206,"journal":{"name":"Proceedings of the 4th International Conference on Networking, Information Systems & Security","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114405304","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}

引用次数: 1

Comparison Support Vector Machines and K-Nearest Neighbors in Classifying Ischemic Stroke by Using Convolutional Neural Networks as a Feature Extraction 比较支持向量机与k近邻在卷积神经网络缺血性脑卒中分类中的应用

Proceedings of the 4th International Conference on Networking, Information Systems & Security Pub Date : 2021-04-01 DOI: 10.1145/3454127.3456606

G. Saragih, Z. Rustam

{"title":"Comparison Support Vector Machines and K-Nearest Neighbors in Classifying Ischemic Stroke by Using Convolutional Neural Networks as a Feature Extraction","authors":"G. Saragih, Z. Rustam","doi":"10.1145/3454127.3456606","DOIUrl":"https://doi.org/10.1145/3454127.3456606","url":null,"abstract":"The paper introduces the hybrid method of Convolutional Neural Network (CNN) and machine learning methods as a classifier, that is Support Vector Machines and K-Nearest Neighbors for classifying the ischemic stroke based on CT scan images. CNN is used as a feature extraction and the machine learning methods used to replace the fully connected layers in CNN. The proposed method is used to reduce computation time and improve accuracy in classifying image data, because we know that deep learning is not efficient for small amounts of data, where the data we use is only 93 CT scan images obtained from Cipto Mangunkusumo General Hospital (RSCM), Indonesia. The architecture of CNN used in this research consists of 5 layers convolutional layers, ReLU, MaxPooling, batch normalization and dropout. The elapsed time required for CNN is 7.631490 seconds. The output of feature extraction is used as an input for SVM and KNN. SVM with linear kernel can correctly classify ischemic stroke, with 100% accuracy in the training model and 96% accuracy in testing model with a test size of 60%. KNN classify ischemic stroke, with 97.3% (#neighbors = 5) accuracy in training model with a test size of 60% and 90% (#neighbors = 10, 15, 25) accuracy in the testing model with a test size of 10%. Based on these results, the SVM produces the higher accuracy compared to KNN in classifying ischemic stroke using CNN as feature extraction based on CT scan images with a computation time of only 8.0973 seconds.","PeriodicalId":432206,"journal":{"name":"Proceedings of the 4th International Conference on Networking, Information Systems & Security","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123176070","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}

引用次数: 0

INSOMNIA EEG SIGNAL PREPROCESSING USING ICA ALGORITHMS 失眠症脑电信号预处理的ica算法

Proceedings of the 4th International Conference on Networking, Information Systems & Security Pub Date : 2021-04-01 DOI: 10.1145/3454127.3457630

Djerassembe Laouhingamaye Frédéric, Awatif Rouijel, Hassan El Ghazi

引用次数: 0

SDN Control Plane Security: Attacks and Mitigation Techniques SDN控制平面安全:攻击和缓解技术

Proceedings of the 4th International Conference on Networking, Information Systems & Security Pub Date : 2021-04-01 DOI: 10.1145/3454127.3456612

Kiran Fatima, Kanwal Zahoor, N. Bawany

{"title":"SDN Control Plane Security: Attacks and Mitigation Techniques","authors":"Kiran Fatima, Kanwal Zahoor, N. Bawany","doi":"10.1145/3454127.3456612","DOIUrl":"https://doi.org/10.1145/3454127.3456612","url":null,"abstract":"Traditional networks are complex and hard to manage due to many reasons such as manual configuration requirements of dedicated devices, lack of flexibility and a non-dynamic approach. To overcome these limitations and to meet the challenges of modern networks a new networking paradigm Software Defined Networking (SDN) has been introduced. SDN presents a centralized and completely dynamic environment which provides flexibility and programmability in networks. It enables the network to be controlled centrally and intelligently using multiple software applications. SDN being in its infancy, brings along new challenges. Standardization of various interfaces, scalability, compatibility of contrasting or divergent networks, and vulnerability issues are few of them. This paper discusses various vulnerabilities and possible attacks on every layer of an SDN and focuses on control plane attacks. Further, it presents a comprehensive survey on numerous attacks on the brain of an SDN i.e. the control plane along with existing solutions. The study concludes that despite the challenges that are worth debating, SDN has many characteristics that make it an ideal candidate for future networks.","PeriodicalId":432206,"journal":{"name":"Proceedings of the 4th International Conference on Networking, Information Systems & Security","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128354294","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}

引用次数: 2

IoT Protocols – MQTT versus CoAP

Proceedings of the 4th International Conference on Networking, Information Systems & Security Pub Date : 2021-04-01 DOI: 10.1145/3454127.3456594

Alyaziya Almheiri, Z. Maamar

引用次数: 1

BMGC: A Deep Learning Approach to Classify Bengali Music Genres 孟加拉音乐流派分类的深度学习方法

Proceedings of the 4th International Conference on Networking, Information Systems & Security Pub Date : 2021-04-01 DOI: 10.1145/3454127.3456593

Moumita Sen Sarma, Avishek Das

{"title":"BMGC: A Deep Learning Approach to Classify Bengali Music Genres","authors":"Moumita Sen Sarma, Avishek Das","doi":"10.1145/3454127.3456593","DOIUrl":"https://doi.org/10.1145/3454127.3456593","url":null,"abstract":"Music genre classification (MGC) is the process of tagging music with their appropriate genres by analyzing music signals or the lyrics. With the accelerated surge in music data repositories, MGC can be extensively used in music recommendation systems, advertisement, and streaming services for systematic and efficient management. However, there have been many works on English music classification using different statistical and machine learning approaches, but there is no notable progress found in the arena of Bengali music. Besides, a few significant works have been found in utilizing Deep Learning (DL) methods to classify different music genres. Bengali music is significantly enriched with its contents and uniqueness. Moreover, the extent and scope of exploring the DL approach in Bengali music ground are still latent. Therefore, Bengali music genre classification is quite a new research area in the Deep learning field. In this work, we have constructed a Bengali Music Genre Classifier (BMGC) to categorize 6 Bengali music genres: ‘Adhunik’, ‘Band’, ‘Hiphop’, ‘Nazrulgeeti’, ‘Lalon’, and ‘Rabindra Sangeet’. We have created a Bengali music genre classification dataset (hereafter named BMGCD) containing 2944 Bengali music clips, and a Gated Recurrent Unit based deep learning model has been developed to predict the music genre from audio signals. Our developed model achieved an accuracy of 80.4% and 80.6% F1-score which surpasses the related existing works.","PeriodicalId":432206,"journal":{"name":"Proceedings of the 4th International Conference on Networking, Information Systems & Security","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131077615","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}

引用次数: 1