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Application of deep neural networks for security analysis of digital infrastructure components

Alexander Pechenkin, Roman Demidov, A. Sarygulov, V. Sergeev, L. Ungvári, W. Semmler
2018 SHS Web of Conferences  
The approach is based on building semantically significant vector representations of software code and multistage instructing the deep neural network on revealing hierarchical abstractions in computer  ...  A new approach is offered to searching software vulnerabilities on the basis of application of deep learning.  ...  A preliminary setting of weights of neural network in the initial layers (Fig. 3 , network A+B) takes place as a result of solving an auxiliary task.  ... 
doi:10.1051/shsconf/20184400068 fatcat:5bfeqoudv5bfrh7njyffinhoiy

An Enhanced Deep Learning Model to Network Attack Detection, by using Parameter Tuning, Hidden Markov Model and Neural Network

Choukri Djellali, Mehdi adda
2021 Journal of Ubiquitous Systems and Pervasive Networks  
In the present study, we introduced a Deep Learning model to network attack detection, by using Hidden Markov Model and Artificial Neural Networks.  ...  The model selection technique is applied to optimize the bias-variance trade-off of the expected prediction.  ...  Acknowledgments We would like to gratefully acknowledge the support of university of Quebec at Rimouski for funding of this research.  ... 
doi:10.5383/juspn.15.01.005 fatcat:2pykvk6cnrcphpf2fydaewkh74

Research and Implementation of the Text Matching Algorithm in the Field of Housing Law and Policy Based on Deep Learning

Yin Xu, Hong Ma, Muhammad Javaid
2021 Complexity  
The research on the legal system of housing security is in the exploratory stage, involving various theoretical and practical research studies.  ...  This paper introduces the practical application of the deep learning model and fast learning algorithm in detail.  ...  and a new training method, which overcomes the bottleneck of the training of neural networks, and initiates the upsurge of research on neural networks [17] .  ... 
doi:10.1155/2021/3165600 fatcat:epzlj7tokvg4fdvn4knk3hcspa

Network Data Security for the Detection System in the Internet of Things with Deep Learning Approach

Kalubi Kalubi Deiu-merci, Mayou Mayou
2018 International Journal of Advanced Engineering Research and Science  
For this first time in the IoT research, the concepts of Gated Recurrent Neural Networks are applied for the IoT security.  ...  In the case of our work, we have used deep learning theories, to achieve a light data interconnection security solution; we also have TCP/IP protocol for data transmission control, algorithm drillers for  ...  The application of deep learning to the IoT domain, particularly in IoT security is still in the initial stages of research and has a great potential to find insights from the IoT data.  ... 
doi:10.22161/ijaers.5.6.34 fatcat:24ywzal5ezfb3ld6yx5lum44im

Research on Network Security Application Based on Deep Learning

Zhao Jianchao
2021 Converter  
In recent years, the outstanding performance of deep learning in classification and behavior prediction based on massive data makes people begin to study how to use deep learning technology.  ...  Behind the rapid development of the Internet industry, Internet security has become a hidden danger.  ...  In recent years, with the continuous improvement of relevant laws and regulations and management system in the field of network security, China's network security research ability and talent team construction  ... 
doi:10.17762/converter.235 fatcat:weajw3leufbclccntywctnzhba

Deep Learning Cluster Structures for Management Decisions: The Digital CEO

Will Serrano
2018 Sensors  
The Deep Learning Cluster Structure has been applied in the Cognitive Packet Network (CPN) for routing decisions based on Quality of Service (QoS) metrics (Delay, Loss and Bandwidth) and Cyber Security  ...  The proposed model is based on the Random Neural Network (RNN) Reinforcement Learning for fast local decisions and Deep Learning for long-term memory.  ...  The Deep Learning Cluster Structures has been applied in the Cognitive Packet Network (CPN) for Quality of Service metrics and Cyber Security keys in Management Decisions based on packet routing and flow  ... 
doi:10.3390/s18103327 fatcat:aqedquz3yjg7pbfevfrwrzouq4

Intelligent Processing of Intrusion Detection Data

Tao Duan, Youhui Tian, Hanrui Zhang, Yaozong Liu, Qianmu Li, Jian Jiang, Zongsheng Shi
2020 IEEE Access  
Intrusion detection technology, as an active and effective dynamic network defense technology, has rapidly become a hot research topic in the field of network security since it was proposed.  ...  , and focuses on the principle and working mechanism of deep belief network and Principal Component Analysis (PCA).  ...  Relevant parameters of each layer of RBM: The initial value of the bias between the visible and hidden layers. Connection weight initial value.  ... 
doi:10.1109/access.2020.2989498 fatcat:iragrarcbbdenmryyxd5nbocii

Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

Davy Preuveneers, Vera Rimmer, Ilias Tsingenopoulos, Jan Spooren, Wouter Joosen, Elisabeth Ilie-Zudor
2018 Applied Sciences  
on the federated learning, varying between 5 and 15%, while providing full transparency over the distributed training process of the neural network.  ...  The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions.  ...  Acknowledgments: We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.  ... 
doi:10.3390/app8122663 fatcat:22w3om3rwnbgjd7nuo3kdbjn3i

Security and Privacy Risk Assessment of Energy Big Data in Cloud Environment

Zhiru Li, Wei Xu, Huibin Shi, Yuanyuan Zhang, Yan Yan, Daqing Gong
2021 Computational Intelligence and Neuroscience  
According to the different degrees of risk impact, AHP method is used to give indexes weights, genetic algorithm is used to optimize the initial weights and thresholds of BP neural network, and then the  ...  optimized weights and thresholds are given to BP neural network, and the evaluation samples in the database are used to train it.  ...  Conflicts of Interest e authors declare that they have no conflicts of interest regarding the publication of this paper.  ... 
doi:10.1155/2021/2398460 pmid:34912444 pmcid:PMC8668351 fatcat:wupf4l5hanbbbjr46uznmnio6m

Shallow Encoder Deep Decoder (SEDD) Networks for Image Encryption and Decryption [article]

Chirag Gupta
2020 arXiv   pre-print
Therefore, this paper also explores empirically if a deep neural network can learn to reconstruct the original data in any useful form given the output of a neural network or any other nonlinear function  ...  This paper explores a new framework for lossy image encryption and decryption using a simple shallow encoder neural network E for encryption, and a complex deep decoder neural network D for decryption.  ...  An older but relevant 'Analysis of Neural Cryptography' [16] is based on mutually learning networks but is prone to attacks.  ... 
arXiv:2001.03017v2 fatcat:4vsaz4ctpfav7iqga73tqn4ldq

Improved Dragonfly Optimizer for Intrusion Detection Using Deep Clustering CNN-PSO Classifier

K. S. Bhuvaneshwari, K. Venkatachalam, S. Hub醠ovsk� P. Trojovsk� P. Prabu
2022 Computers Materials & Continua  
Since, the large volume of data is generated and transferred through network, the security and performance are remained an issue.  ...  With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and information.  ...  Performance Evaluation of Proposed IGDFO-PSOCCNN IDS with Existing IDS Systems In order to prove the deep neural network based IDS systems, our proposed convolutional deep neural network based on IDS is  ... 
doi:10.32604/cmc.2022.020769 fatcat:i6v4hpwpqza7zph4v7wjymxb54

Understanding Deep Learning Architecture to Various Problems of Cyber Security

Dr. Diwakar Ramanuj Tripathi
2021 International Journal for Research in Applied Science and Engineering Technology  
Deep auto encoders, limited Boltzmann machines, recurrent neural networks, generative adversarial networks, and other DL methods are all described in this study in a brief tutorial-style method.  ...  After that, we'll go over how each of the DL methods is employed in security applications. Keywords: Machine, Cyber, Security, Architecture, Technology.  ...  The relevance of cyber security employing deep learning techniques is summarised in this survey (DL). In recent years, researchers have used deep learning techniques.  ... 
doi:10.22214/ijraset.2021.39349 fatcat:bzbbhvip45bbbfkaspedqmu37m

An Intelligent Approach for Intrusion Detection using Convolutional Neural Network

P. Manoj Kumar, M. Parvathy, C. Abinaya Devi
2022 Journal of Network Security Computer Networks  
Intrusion Detection Systems (IDS) is one of the important aspects of cyber security that can detect the anomalies in the network traffic.  ...  Finally, the experimental results are compared with those of various Deep Discriminative models including Recurrent Neural network (RNN), Deep Neural Network (DNN) etc., proposed for IDS under the same  ...  weights.Weights are initialized randomly and updated based on weight values in the previously connected layer and the learning parameters.  ... 
doi:10.46610/jonscn.2022.v08i01.001 fatcat:laorzkbfsrdm3gi7rn2f3so5nm

Deep Learning Approach for Enhanced Cyber Threat Indicators in Twitter Stream [article]

Simran K, Prathiksha Balakrishna, Vinayakumar R, Soman KP
2020 arXiv   pre-print
In recent days, the amount of Cyber Security text data shared via social media resources mainly Twitter has increased.  ...  Various hyperparameter tuning approaches are used for identifying optimal text representation method as well as optimal network parameters and network structures for deep learning models.  ...  Acknowledgements This research was supported in part by Paramount Computer Systems and Lakhshya Cyber Security Labs. We are grateful to NVIDIA India, for the GPU hardware support to research grant.  ... 
arXiv:2004.00503v1 fatcat:qr64br47uvfilbolzbdzspxoeu

An intrusion detection system for packet and flow based networks using deep neural network approach

Kaniz Farhana, Maqsudur Rahman, Md. Tofael Ahmed
2020 International Journal of Electrical and Computer Engineering (IJECE)  
Study on deep neural networks and big data is merging now by several aspects to enhance the capabilities of intrusion detection system (IDS).  ...  Many IDS models has been introduced to provide security over big data. This study focuses on the intrusion detection in computer networks using big datasets.  ...  Attack instances number for multi-class Normalization Normalization is important for data preparation in neural networks because the layers in the model act sensitive depending on the weights of the  ... 
doi:10.11591/ijece.v10i5.pp5514-5525 fatcat:sgnrgrlccncnvjefordgdkhhoq
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