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Data Fusion-Based Machine Learning Architecture for Intrusion Detection

Muhammad Adnan Khan, Taher M. Ghazal, Sang-Woong Lee, Abdur Rehman
2022 Computers Materials & Continua  
Simulation results show that wireless sensor networks are optimized effectively to monitor and detect any malicious or intrusion activity through this proposed approach.  ...  This study investigates the methodology of Real Time Sequential Deep Extreme Learning Machine (RTS-DELM) implemented to wireless Internet of Things (IoT) enabled sensor networks for the detection of any  ...  Acknowledgement: We thank our families and colleagues who provided us with moral support. Funding Statement: The author(s) received no specific funding for this study.  ... 
doi:10.32604/cmc.2022.020173 fatcat:frdsysaffjeflh2gxwcl6tdcvi

WLAN Indoor Intrusion Detection Based on Deep Signal Feature Fusion and Minimized-MKMMD Transfer Learning [article]

M. Zhou Chongqing University of Posts, Telecommunications)
2019 arXiv   pre-print
In response to this urgent problem, this paper proposes a novel WLAN indoor intrusion detection method based on deep signal feature fusion and Minimized Multiple Kernel Maximum Mean Discrepancy (Minimized-MKMMD  ...  Firstly, the multi-branch deep convolutional neural network is used to conduct the dimensionality reduction and feature fusion of the RSS data, and the tags are obtained according to the features of the  ...  With the rapid development of computer processing technology, data processing methods based on machine learning such as transfer learning and deep neural network have been widely used in intrusion detection  ... 
arXiv:1910.02051v1 fatcat:33xgzkifdbhohemaggyfznm2kq

Guest Editorial Introduction to the Special Issue on Deep Learning Models for Safe and Secure Intelligent Transportation Systems

Alireza Jolfaei, Neeraj Kumar, Min Chen, Krishna Kant
2021 IEEE transactions on intelligent transportation systems (Print)  
He has published in a wide variety of areas in computer science and authored a graduate textbook on performance modeling of computer systems.  ...  He is currently a Professor with the Department of Computer and Information Science, Temple University, Philadelphia, PA, USA, where he directs the IUCRC Center on Intelligent Storage.  ...  and intrusion detection algorithm for Unmanned Aerial Vehicle (UAV)-based on an Airborne LiDAR (ALORID).  ... 
doi:10.1109/tits.2021.3090721 fatcat:c2o2vno6bjbnxdn6y4zm7ztmvq

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.  ...  Aimed at the existing problems in network intrusion detection, this paper proposes an improved LSTM combined with spatiotemporal structure for intrusion detection.  ...  learning-based LSTM neural network intrusion detection model.  ... 
doi:10.1155/2021/6623554 doaj:899f6f4c6fcf44348cc18fc0ed4009bf fatcat:klifk2xvobfgzfy4l2nvhsjlka

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.  ...  Acknowledgments is work was supported by the Science and Technology Project of the State Grid Corporation of China (2021YF-56).  ... 
doi:10.1155/2022/1415713 fatcat:gjggxz4nzrbozdxjofw33e64ly

Optimized Fuzzy Enabled Semi-Supervised Intrusion Detection System for Attack Prediction

Gautham Praveen Ramalingam, R. Arockia Xavier Annie, Shobana Gopalakrishnan
2022 Intelligent Automation and Soft Computing  
This model increases the accuracy of intrusion detection using Machine Learning Methodologies and fuzziness has been used to identify various categories of hazards, and a machine learning approach has  ...  The combined use of fuzziness-based and RNN-IDS is therefore highly suited to high-precision classification, and its efficiency is better compared to that of conventional machine learning approaches.  ...  Acknowledgement: The authors would like to thank the editors and reviewers for their review and recommendations. Funding Statement: The authors received no specific funding for this study.  ... 
doi:10.32604/iasc.2022.022211 fatcat:u4jmyoynyzfv3mjvq23mneoywm

Introduction to the Special Section on Artificial Intelligence Security: Adversarial Attack and Defense

Xiaojiang Du, Willy Susilo, Mohsen Guizani, Zhihong Tian
2021 IEEE Transactions on Network Science and Engineering  
Further, they defined an advanced adaptive attack based on intrusion-sharing incentive mechanism, and propose an IDS intelligent configuration scheme based on evolutionary game to detect our defined attack  ...  Zhang et al. in "Spectral-based Directed Graph Network for Malware Detection" consider a graph-based deep learning method for malware detection, where the weighted graph matrix normalization methods can  ... 
doi:10.1109/tnse.2021.3073637 fatcat:ib5qh53qq5bu5hrfjejm3fp76i

Intrusion Detection Model Using Temporal Convolutional Network Blend Into Attention Mechanism

Ping Zhao, Zhijie Fan*, Zhiwei Cao, Xin Li
2022 International Journal of Information Security and Privacy  
In order to improve the ability to detect network attacks, traditional intrusion detection models often used convolutional neural networks to encode spatial information or recurrent neural networks to  ...  However, these approaches used separate models and learned features insufficiently. This paper presented an improved model based on temporal convolutional networks (TCN) and attention mechanism.  ...  Network intrusion detection systems include techniques based on traditional machine learning, based on deep learning, reinforcement learning, and visualization learning (Wang et al, 2021) .  ... 
doi:10.4018/ijisp.290832 fatcat:mriwbn6y5refxmhnoocu2vexbu

An Ensemble Multi-View Federated Learning Intrusion Detection for IoT

Dinesh Chowdary Attota, Viraaji Mothukuri, Reza M. Parizi, Seyedamin Pouriyeh
2021 IEEE Access  
This paper proposes a federated learning-based intrusion detection approach, called MV-FLID, that trains on multiple views of IoT network data in a decentralized format to detect, classify, and defend  ...  INDEX TERMS Internet of Things, IoT security, federated learning, neural networks, multi-view classification, intrusion detection system.  ...  We have implemented the FL approach using PySyft [32] deep learning framework, which is based on Pytorch deep learning framework. B.  ... 
doi:10.1109/access.2021.3107337 fatcat:6lswz34lonbj5ddwc7pmvbtn7u

Machine Learning for Misuse-Based Network Intrusion Detection: Overview, Unified Evaluation and Feature Choice Comparison Framework

Laurens Le Jeune, Toon Goedeme, Nele Mentens
2021 IEEE Access  
These metrics, the detection score and the identification score, together reliably present the performance of a network intrusion detection system to allow for practical comparison on a large scale.  ...  Network Intrusion detection systems are essential for the protection of advanced communication networks.  ...  Researchers in [78] investigate the use of transfer learning for their SAE, implementing it as the neural network in their Deep neural network and adaptive Self-Taught-based Transfer Learning (DST-TL  ... 
doi:10.1109/access.2021.3075066 doaj:9ef1b9f8c511423d93e4e46f37c9cb8b fatcat:t2viwx3uubfypazhhsdmt3zsma

Effective Parameter Optimization & Classification using Bat-Inspired Algorithm with Improving NSSA

2019 International Journal of Engineering and Advanced Technology  
Four classification algorithms were used out of which, gradient boosting model outperformed the benchmarked algorithms and proved its importance on data classification based on the accuracy obtained from  ...  In this paper, a machine learning-based approach is proposed to identify the pattern of different categories of attacks made in the past.  ...  A deep belief network (DBN) and probabilistic neural network (PNN) technique are proposed to overcome the existing problems identified in the process of detecting intrusions.  ... 
doi:10.35940/ijeat.a1498.109119 fatcat:jjla5a2lsjef7f5zkwj7pwtlou

HYBRID-CNN: An Efficient Scheme for Abnormal Flow Detection in the SDN-Based Smart Grid

Pengpeng Ding, Jinguo Li, Liangliang Wang, Mi Wen, Yuyao Guan
2020 Security and Communication Networks  
Prior works were designed based on traditional machine learning methods, such as Support Vector Machine and Naive Bayes.  ...  Specifically, the HYBRID-CNN utilizes a Deep Neural Network (DNN) to effectively memorize global features by one-dimensional (1D) data and utilizes a CNN to generalize local features by two-dimensional  ...  Acknowledgments is work was supported by the National Natural Science Foundation of China (nos. 61702321, 61872230, 61802249, 61802248, and U1936213).  ... 
doi:10.1155/2020/8850550 fatcat:aufthgyok5hxtko3vl2tokx3du

Table of contents

2020 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)  
Deep Neural Network With Transfer Learning 0668 SESSION 23: GREEN WIRELESS NETWORKS AND MOBILE AND WIRELESS COMPUTING 23.1 1570677700 Localization Methods Based on Error Analysis and Modeling  ...  Placement in Optical Networks Using Deep Tensor Neural Network 0218 SESSION 8: BIG DATA, DATA ANALYTICS AND CLOUD NETWORK 08.1 1570681537 Performance Evaluation of TPC-C Benchmark on Various Cloud  ... 
doi:10.1109/uemcon51285.2020.9298057 fatcat:p4v3pn2m2zaaxdgobcaw5db76m

Deep Neural Network Perception Models and Robust Autonomous Driving Systems [article]

Mohammad Javad Shafiee, Ahmadreza Jeddi, Amir Nazemi, Paul Fieguth,, Alexander Wong
2020 arXiv   pre-print
This paper analyzes the robustness of deep learning models in autonomous driving applications and discusses the practical solutions to address that.  ...  Traffic signs and traffic lights are detected by object detection models, typically based on visible camera images; other cars driving in the road can be detected using object detection models by fusing  ...  and deep learning techniques, and complex deep learning networks are utilized in all phases from perception to decision-making and control.  ... 
arXiv:2003.08756v1 fatcat:w2ajk6ryrzg4bl5avd3thod5ti

LCHI: Low-Order Correlation and High-Order Interaction Integrated Model Oriented to Network Intrusion Detection

Shengwei Lei, Chunhe Xia, Tianbo Wang, Zhihan Lv
2021 Wireless Communications and Mobile Computing  
We carry on a series of experiments on the public wireless and wired network intrusion detection datasets.  ...  Learning sophisticated feature interactions is critical in improving detection accuracy for network intrusion.  ...  At present, more effective methods of network intrusion detection are to use deep learning models.  ... 
doi:10.1155/2021/6830372 fatcat:ks6xvvu7ifhlloal3ijm2jpmwa
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