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Abnormal Access Behavior Detection of Ideological and Political MOOCs in Colleges and Universities

Ni Hong, Xuefeng Wang, Zhonghua Wang
2021 Mobile Information Systems  
Based on deep learning, the network behavior detection model is established to distinguish whether the network behavior is normal, so as to detect the abnormal access network behavior.  ...  Therefore, in this paper, we propose a detection method of abnormal access behavior of ideological and political MOOCs in colleges and universities.  ...  In this paper, based on deep learning, we propose a deep learning-based network abnormal behavior detection method to detect the abnormal access behavior of university ideological and political MOOCs.  ... 
doi:10.1155/2021/9977736 doaj:d4714d1b404048b6999a1a2d1801a65e fatcat:vttl5lq4kjchplf3m4o4mynbfq

Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video

2018 Tehnički Vjesnik  
neural network deep learning and apply it to the smart city on the basis of summarizing video development technology.  ...  The object detection based on object modelling is applied to after-event data query and retrieval.  ...  Acknowledgements This work is financially supported by Scientific Research Project of Hebei Science and Technology Department, China (No.16214707).  ... 
doi:10.17559/tv-20171229024444 fatcat:bejt4g6tnbgbvhndkz2imyb5de

Deep Learning-Based Anomaly Traffic Detection Method in Cloud Computing Environment

Junjie Cen, Yongbo Li, Shalli Rani
2022 Wireless Communications and Mobile Computing  
To address the problem of poor detection performance of existing intrusion detection methods in the environment of high-dimensional massive data with uneven class distribution, a deep learning-based anomaly  ...  Finally, experiments are conducted based on the KDD CUP99 dataset, and the results demonstrate that the detection rate (DR) and false alarm rate (FAR) of the proposed FCM-MFOA-GRNN method are 91% and 1.176%  ...  To address the problem of poor detection performance of existing intrusion detection methods in the environment of high-dimensional massive data with uneven class distribution, a deep learning-based anomaly  ... 
doi:10.1155/2022/6155925 fatcat:ppkr3zjv5jhh7c4mcj4fe6p724

Anomaly Detection in Self-Organizing Networks: Conventional Versus Contemporary Machine Learning

Muhammed Furkan Kucuk, Ismail Uysal
2022 IEEE Access  
This paper presents a comparison of conventional and modern machine (deep) learning within the framework of anomaly detection in self-organizing networks.  ...  We demonstrate for the first time, on a previously published and publicly available dataset, that conventional machine learning can outperform the previous state-of-the-art using deep learning by 15% on  ...  ) specifically for anomaly detection applications on mobile networks.  ... 
doi:10.1109/access.2022.3182014 fatcat:77fbmisipbadtejmilo7swl56m

A novel approach to big data analysis using deep belief network for the detection of android malware

Uma Narayanan, Varghese Paul, Shelbi Joseph
2019 Indonesian Journal of Electrical Engineering and Computer Science  
In this examination, we propose to relate the features from the static examination with features from the dynamic examination of Android applications and depict malware using Deep learning systems.  ...  Deep learning is another domain of AI explore that has expanded extending thought in artificial information.  ...  Algorithm of model is shown in Algorithm 1 in which deep learning is used for the detection of Malware Detection in Mobile phones and Tablets.  ... 
doi:10.11591/ijeecs.v16.i3.pp1447-1454 fatcat:ggg5lg7inbappe45cdl4fzft44

Assessment of Deep Learning Methodology for Self-Organizing 5G Networks

Muhammad Zeeshan Asghar, Mudassar Abbas, Khaula Zeeshan, Pyry Kotilainen, Timo Hämäläinen
2019 Applied Sciences  
Our empirical study presents a framework for cell outage detection based on an autoencoder using simulated data obtained from a SON simulator.  ...  The concept of SON is then explained with applications of intrusion detection and mobility load balancing.  ...  Therefore, deep learning is essentially machine learning that is tailored for deep models. The recent progress in deep learning has been exclusively based on deep neural network models.  ... 
doi:10.3390/app9152975 fatcat:n7pgi2a4mba6xpo7cwphuw3ivu

Medical image-based detection of COVID-19 using Deep Convolution Neural Networks

Loveleen Gaur, Ujwal Bhatia, N Z Jhanjhi, Ghulam Muhammad, Mehedi Masud
2021 Multimedia Systems  
This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores).  ...  The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications.  ...  To process massive data for disease diagnosis, machine learning (ML) and deep learning (DL) techniques have exhibited noticeable performance.  ... 
doi:10.1007/s00530-021-00794-6 pmid:33935377 pmcid:PMC8079233 fatcat:6jyrvscydbcyzmb2mlfxzfg5au

Application of deep learning algorithms and architectures in the new generation of mobile networks

Dejan Dasic, Miljan Vucetic, Nemanja Ilic, Milos Stankovic, Marko Beko
2021 Serbian Journal of Electrical Engineering  
Having firstly presented the background of deep learning and related technologies, the paper goes on to present the architectures used for deployment of deep learning in mobile networks.  ...  Advanced machine learning techniques based on deep architectures and appropriate learning methods are recognized as promising ways of tackling the said challenges in many aspects of mobile networks, such  ...  Deep learning models are known for low interpretability and require massive volumes of data for training.  ... 
doi:10.2298/sjee2103397d fatcat:n3hduljspfbt3mkq2zdzbae72u

Effective Bots' Detection for Online Smartphone Game Using Multilayer Perceptron Neural Networks

Woei-Jiunn Tsaur, Chinyang Henry Tseng, Chin-Ling Chen, Mamoun Alazab
2022 Security and Communication Networks  
Based on these abnormal rates, we then use K means to cluster players and further define the gray area.  ...  This approach calculates each player's abnormal rate to judge game bots and is evaluated on the famous mobile online game.  ...  In this study, we train the proposed deep neural network models on the famous mobile online baseball game named KANO.  ... 
doi:10.1155/2022/9429475 fatcat:ict7f5jjjbft5krwiuncpjepzi

AI assisted PHY in future wireless systems: Recent developments and challenges

Wei Chen, Ruisi He, Gongpu Wang, Jiayi Zhang, Fanggang Wang, Ke Xiong, Bo Ai, Zhangdui Zhong
2021 China Communications  
characterization, channel coding, intelligent signal identification, channel estimation, new PHY for random access in massive machine-type communication (mMTC), massive multiple-input multiple-output (  ...  Innumerable attempts exploiting AI methods have been carried out, which results in the state-of-the-art performance in many different areas of wireless communications.  ...  Data processing Data processing Deep learning network Modeling/regression Ruisi He, is a professor at the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University  ... 
doi:10.23919/jcc.2021.05.019 fatcat:g7njljc5ojdh5p6fvtzovzgpoa

A Review of Deep Learning in 5G Research: Channel Coding, Massive MIMO, Multiple Access, Resource Allocation, and Network Security

Amanda Ly, Yu-Dong Yao
2021 IEEE Open Journal of the Communications Society  
Due to the advancement of deep learning, numerous such research has utilized this technique. This article provides a comprehensive review of 5G communications research using deep learning.  ...  Research tasks include but are not limited to quality-of-service (QoS), energy efficiency, massive connectivity, reliable communications, and security.  ...  From automated driving by using deep learning for object and pedestrian detection to medical research for the detection of cell abnormalities, the growth of deep learning has just begun.  ... 
doi:10.1109/ojcoms.2021.3058353 fatcat:vqyfhhm4gnb4po4nhtjch7dlpe

Network Intrusion Detection Based on an Improved Long-Short-Term Memory Model in Combination with Multiple Spatiotemporal Structures

Xiaolong Huang
2021 Wireless Communications and Mobile Computing  
Experimental verification shows that the accuracy and false alarm rate of the intrusion detection model based on the neural network are significantly better than those of other traditional models.  ...  The unsupervised spatiotemporal encoder is used to intelligently extract the spatial characteristics of network traffic data samples.  ...  Acknowledgments This work was supported in part by the Cultural Science Research of Jiangsu Province under Grant 18YB27.  ... 
doi:10.1155/2021/6623554 doaj:899f6f4c6fcf44348cc18fc0ed4009bf fatcat:klifk2xvobfgzfy4l2nvhsjlka

Intelligent Botnet Detection Approach in Modern Applications

Khattab M. Ali Alheeti, Ibrahim Alsukayti, Mohammed Alreshoodi
2021 International Journal of Interactive Mobile Technologies  
In this paper, a new detection system is proposed to secure sensitive information of IoT devices. However, it is heavily based on deep learning networks.  ...  To prove the efficiency of the proposed approach, the system was tested by using two datasets; normal and fuzzification datasets.  ...  In [26] , the performance of two IoT intrusion detection based on deep learning models was compared and examined the effects of adversarial samples on such deep learning models.  ... 
doi:10.3991/ijim.v15i16.24199 doaj:a1525ebce3404c02a1bb8596d27116f7 fatcat:yopfwjipbncnle3hbaod6m2hwu

A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET

Safaa Laqtib, Khalid El Yassini, Moulay Lahcen Hasnaoui
2020 International Journal of Electrical and Computer Engineering (IJECE)  
Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber  ...  (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on  ...  DEEP LEARNING MODELS FOR INTRUSION DETECTION SYSTEM IN MANET Deep learning is a class of Machine learning algorithms that uses multiple layers to progressively extract higher level features from the raw  ... 
doi:10.11591/ijece.v10i3.pp2701-2709 fatcat:wqxjcfks2nb47lsolmctyrg23y

Power Intelligent Terminal Intrusion Detection Based on Deep Learning and Cloud Computing

Tong Li, Hai Zhao, Yaodong Tao, Donghua Huang, Chao Yang, Shuheng Xu, Jun Ye
2022 Computational Intelligence and Neuroscience  
It analyzes the structure of the power knowledge network and cloud computing through deep learning-based methods and provides a network interference detection model.  ...  At the same time, for big data network data retrieval, it retrieves and analyzes data flow quickly and accurately with the help of deep learning of data components.  ...  Conflicts of Interest e authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this paper.  ... 
doi:10.1155/2022/1415713 pmid:35586098 pmcid:PMC9110159 fatcat:gjggxz4nzrbozdxjofw33e64ly
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