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Guest Editorial Introduction to the Special Issue on Deep Learning Models for Safe and Secure Intelligent Transportation Systems
2021
IEEE transactions on intelligent transportation systems (Print)
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He was a Research Professor with the Center for Secure Information Systems, George Mason University. ...
His research interests include energy efficiency, robustness, and security in cyber and cyber-physical systems. ...
network (GAN) model using controller area network message blocks and an enhanced GAN discriminator. ...
doi:10.1109/tits.2021.3090721
fatcat:c2o2vno6bjbnxdn6y4zm7ztmvq
Towards digital cognitive clones for the decision-makers: adversarial training experiments
2021
Procedia Computer Science
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) and a smart digital adversary as a challenger (generator of sophisticated decision situations, emergencies and attacks, which supposedly catalyzes the cloning process). ...
) and a smart digital adversary as a challenger (generator of sophisticated decision situations, emergencies and attacks, which supposedly catalyzes the cloning process). ...
experience, supply chain and asset management: simulations with the self-managed "digital customers" and digitalized processes can be used to obtain the optimal settings in real-world environments and improving ...
doi:10.1016/j.procs.2021.01.155
fatcat:74qe4vvcurbftco53z45baupoq
GenAtSeq GAN with Heuristic Reforms for Knowledge Centric Network with Browsing Characteristics Learning, Individual Tracking and Malware Detection with Website2Vec
2020
SN Computer Science
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Our contributions are in the architectural definition of Generalized Attentive Sequential Generative Adversarial Network (GenAtSeq-GAN), identification of log characteristics for discrimination, and the ...
Our proposed GAN network is characterized for the detection of individuals through dissimilarity between real and generated samples and quantification of detected difference, both of which enhanced learning ...
Network researcher is empowering these networks through several improvements in technology like deciding the right path, improved and situation-aware stitching systems, effective network coding, distribution ...
doi:10.1007/s42979-020-00234-8
fatcat:tyowypduvrczrpieyhh2wycsau
Generative Adversarial Networks: A Literature Review
2020
KSII Transactions on Internet and Information Systems
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Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction. ...
Along with the idea of "generative" and "adversarial", researchers are trying to apply Generative Adversarial Networks to the security field. ...
Under GAN framework, RL method based on strategy gradient is still adopted, which greatly improves the performance of information retrieval. ...
doi:10.3837/tiis.2020.12.001
fatcat:yvafa3j5ofdkbdp7xblxegxlpy
Multimedia Security Situation Prediction Based on Optimization of Radial Basis Function Neural Network Algorithm
2022
Computational Intelligence and Neuroscience
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Aiming at the problem of prediction accuracy in network situation awareness, a network security situation prediction method based on a generalized radial basis function (RBF) neural network is proposed ...
Simulation experiments show that this method can obtain situation prediction results more accurately and improve the active security protection of network security. ...
Some research results have been made in the field of network security situation prediction at home and abroad. ...
doi:10.1155/2022/6314262
pmid:35432511
pmcid:PMC9012625
fatcat:mcwxdlkgwbbo5kvlsu6yngg2h4
Data augmentation through multivariate scenario forecasting in Data Centers using Generative Adversarial Networks
[article]
2022
arXiv
pre-print
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For this purpose, we will implement a powerful generative algorithm: Generative Adversarial Networks (GANs). ...
This paper proposes a time-series data augmentation methodology based on synthetic scenario forecasting within the Data Center. ...
Methodology
Implemented GAN Training Improvements In the following, we describe the GAN training improvements found in the literature that have been implemented for this research. ...
arXiv:2201.06147v2
fatcat:tbeoxnmqnfhvbjtxiouemnaede
AE-CGAN Model based High Performance Network Intrusion Detection System
2019
Applied Sciences
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In this paper, a high-performance network intrusion detection system based on deep learning is proposed for situations in which there are significant imbalances between normal and abnormal traffic. ...
Based on the unsupervised learning models autoencoder (AE) and the generative adversarial networks (GAN) model during deep learning, the study aim is to solve the imbalance of data and intrusion detection ...
We used GAN to solve the data imbalance, and the above research differed in that it developed the IDS by making malicious data based on GAN. ...
doi:10.3390/app9204221
fatcat:b7xszqyzy5cwnnztjf2tdo4o7y
Learning-based Intrusion Detection System for On-Board Vehicle Communication
2021
Italian Conference on Cybersecurity
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The second includes a discriminator that has been trained exploiting the Generative Adversarial Network (GAN) paradigm, to distinguish among the attack-free situation and an anomalous situation. ...
Since the introduction of many external interfaces in modern vehicles exposes users to the risk of cyber-attacks, the need of focus on security is concrete. ...
A CAN packet is broadcasted to the entire network, and only some ECUs will process the packet based on the ID received. ...
dblp:conf/itasec/FioreseM21
fatcat:tvz6wn5c4jc5pe3gjv3zgvj6gq
Spectrum of Advancements and Developments in Multidisciplinary Domains for Generative Adversarial Networks (GANs)
2021
Archives of Computational Methods in Engineering
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The survey paper summarizes the recent applications and developments in the domain of Generative Adversarial Networks (GANs) i.e. a back propagation based neural network architecture for generative modeling ...
GANs is one of the most highlighted research avenue due to its synthetic data generation capabilities and benefits of representations comprehended irrespective of the application. ...
Acknowledgements The authors would like to express their gratitude for the institutional support and research grant of Higher Education Commission (HEC) of Pakistan under International Research Support ...
doi:10.1007/s11831-021-09543-4
pmid:33824572
pmcid:PMC8017345
fatcat:efjw24635vdlbgj26bezu4y5sq
Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue
[article]
2022
arXiv
pre-print
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Conclusion: Based on this study, we would suggest the use of optimized GANs as an alternative method for security vulnerability data class imbalanced issues. ...
Goal: To help security practitioners address software security data class imbalanced issues and further help build better prediction models with resampled datasets. ...
We recommend using optimized GANs for security vulnerability dataset class rebalancing purposes based on this study. ...
arXiv:2203.11410v2
fatcat:nyt3w6g7r5goxntayoux4wwusq
Network-Simulated Generation of Human Faces with Expressions and Orientations for Deep Learning Classification
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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network (WGAN) is presented. ...
The application using ResNet-50 and RetinaNet as a pre-model for the prediction and detection of the human faces revealed a rapid prediction time and accuracy during the assessment test. ...
Synthetic-image-based Deep Learning method For predicting human faces, the ResNet50 pre-model by Microsoft Research is used [21], which exhibits a rapid prediction time and high accuracy. ...
doi:10.35940/ijitee.b7491.129219
fatcat:qegapcmrbfbd3jkxrxmil7oble
Deep Learning-Based Solutions for 5G Network and 5G-Enabled Internet of Vehicles: Advances, Meta-Data Analysis, and Future Direction
2022
Mathematical Problems in Engineering
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The deep learning algorithm solutions for security, energy, resource management, 5G-enabled IoV, and mobile network in 5G communication systems were presented including several other applications. ...
However, it prompted new challenges on the 5G network cybersecurity defense system, resource management, energy, cache, and mobile network, therefore making the existing approaches obsolete to tackle the ...
[84] predicted next packet time based on traffic trace using LSTM. e LSTM predicts the dynamic sleep time in discontinuous reception in 5G networks. It is found to improve power savings. Chen et al ...
doi:10.1155/2022/6855435
fatcat:i2rzrbvt4bbwpgebuwhisgilyu
Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation
2021
Computer Networks
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2021 pre-print arXiv:2105.04184v1 fatcat:yayheyx7jzdafocs6br4utpygi
Given their relative ease of use, it is therefore natural that researchers in the field of networking (which has seen extensive application of deep learning methods) should take an interest in GAN-based ...
Despite the recency of their conception, Generative Adversarial Networks (GANs) constitute an extensively researched machine learning sub-field for the creation of synthetic data through deep generative ...
[80] proposed Tran-GAN, a GAN-based transfer learning method for social tie prediction that seeks to uncover latent information in social networks. ...
doi:10.1016/j.comnet.2021.108149
fatcat:4ekgil24ijha3evmzruez63tdq
ICISCAE 2019 Table of Contents
2019
2019 2nd International Conference on Information Systems and Computer Aided Education (ICISCAE)
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Traffic Prediction Model Based on Neural Network Jianyong Fan, Dejun Mu, Yang Liu 554 IS284 Research and Design of Key Management System in Power Distribution Network Based on Quantum Private Communication ...
Neural Network Fault Diagnosis Based on UKF Algorithm for Sallen-Key Bandpass Filter Circuit Xusheng Gan, Jingjuan Sun, Shuangfeng Li IS292 The Method of Thread Defect Detection Based on Machine Vision ...
doi:10.1109/iciscae48440.2019.9075543
fatcat:hwdx6lgqebdytdxphw562s465e
Behavior Prediction of Traffic Actors for Intelligent Vehicle using Artificial Intelligence Techniques: A Review
2021
IEEE Access
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Minor misbehavior of these vehicles on the busy roads may lead to an accident. Due to this, there is a need for vehicle behavior research work in today's era. ...
behavior prediction of surrounding traffic actors for secure and accurate intelligent vehicle navigation. ...
Based on input representation, output type, and prediction model, various researchers have used different approaches. ...
doi:10.1109/access.2021.3116303
fatcat:spra4jjme5ezdpjhmn4uyptwva
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