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A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges
[article]
2022
arXiv
pre-print
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Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most ...
recent deep learning (DL)-based algorithms. ...
well as deep learning based automatic modulation and wireless protocol classification. ...
arXiv:2201.00680v3
fatcat:435d72iv6zgi7f2exu2tynlcpm
Machine Learning and Deep Learning Approaches for CyberSecuriy: A Review
2022
IEEE Access
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It discusses recent machine learning and deep learning work with various network implementations, applications, algorithms, learning approaches, and datasets to develop an operational intrusion detection ...
As a result, an effective intrusion detection system was required to protect data, and the discovery of artificial intelligence's sub-fields, machine learning, and deep learning, was one of the most successful ...
D2H-IDS In wireless sensor networks, IDS was performed using a combination of machine learning and deep learning [43] . ...
doi:10.1109/access.2022.3151248
fatcat:3h6qhrddkbfipodxapeevn344q
A Novel Approach for Network Intrusion Detection Using Multistage Deep Learning Image Recognition
2021
Electronics
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The paper proposes a novel approach for network intrusion detection using multistage deep learning image recognition. ...
The images then are used for classification to train and test the pre-trained deep learning model ResNet50. ...
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. ...
doi:10.3390/electronics10151854
fatcat:67rt4e4s7zhpvbxxsyq5qd4ab4
Cyberattack detection in mobile cloud computing: A deep learning approach
2018
2018 IEEE Wireless Communications and Networking Conference (WCNC)
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In this paper, we propose a novel framework that leverages a deep learning approach to detect cyberattacks in mobile cloud environment. ...
Furthermore, we present the comparisons with current machine learning-based approaches to demonstrate the effectiveness of our proposed solution. ...
The core idea of deep learning method is using a training dataset to train the pre-established neural network in offline mode with the aim to adjust weights of the neural network. ...
doi:10.1109/wcnc.2018.8376973
dblp:conf/wcnc/NguyenHNWND18
fatcat:ocro5362zvhstdyff4od5eiwpe
A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking
2023
Sensors
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Therefore, this study presents a systematic literature review (SLR) to systematically investigate and critically analyze the existing DDoS attack approaches based on machine learning (ML), deep learning ...
The trend indicates that the number of studies on SDN DDoS attacks has increased dramatically in the last few years. ...
[118] proposed a real-time mitigation agent based on deep reinforcement learning to mitigate TCP, UDP, ICMP, and SYN flood DDoS attacks in the SDN network environment. ...
doi:10.3390/s23094441
fatcat:b77ivgrvzngeljkakqeul7o7zq
Pattern-Aware Intelligent Anti-Jamming Communication: A Sequential Deep Reinforcement Learning Approach
2019
IEEE Access
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A sequential deep reinforcement learning algorithm (SDRLA) without prior information is proposed, and raw spectrum information is used as the input of SDRLA. ...
Taking advantage of both deep learning and reinforcement learning, this method can realize rapid and effective anti-jamming channel selection with no need for modeling the jammer's characteristics. ...
Moreover, the sequential deep reinforcement learning approach is presented in Section IV and Section V. ...
doi:10.1109/access.2019.2954531
fatcat:oibdgguwarbn5ls3g5wzmtnwsm
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey
[article]
2024
arXiv
pre-print
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Recent advancements in deep learning (DL) have posed a significant challenge for automatic speech recognition (ASR). ...
The paper starts by presenting the background of DTL, FL, RL, and Transformers and then adopts a well-designed taxonomy to outline the state-of-the-art approaches. ...
In terms of AM, deep learning-based models like the deep neural network-hidden Markov model (DNN-HMM) and the connectionist temporal classification (CTC) have made significant advancements. ...
arXiv:2403.01255v1
fatcat:4xr4fwqp3zdvrojcgwwtrcbzf4
Intelligent Software-Defined Network for Cognitive Routing Optimization using Deep Extreme Learning Machine Approach
2021
Computers Materials & Continua
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networks are examined in Machine Learning (ML) applications. ...
approach (ISDN-CRO-DELM) in light of the new challenges in the development and operation of communication systems, and capturing motivation from how living creatures deal with difficulty and usability ...
[46] have proposed an enhanced learning approach that is implemented in cognitive wireless through fiber to introduce dynamic channel selection. ...
doi:10.32604/cmc.2021.013303
fatcat:hrye674bnneddn5ayshmo4x5j4
A NOVEL DEEP LEARNING BASED CYBER ATTACK DETECTION SYSTEM WITH BAIT BASED APPROACH FOR MITIGATION
2024
Zenodo
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The threat or crime related to a malicious event owing to a malware attack in cyber-space, which distraction and loss in business and money, is termed Cyber-risk. ...
On the federal network, FIS has records of 4500 malicious samples, 4.5 million spam emails, together with over a billion spams. ...
Usually, such attacks earned it access via a non-illusive wireless access node. ...
doi:10.5281/zenodo.10867501
fatcat:yutmiq3jkvdfhkkbo5vloxx4u4
Deep Learning Approaches for Video Compression: A Bibliometric Analysis
2022
Big Data and Cognitive Computing
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This paper presents a bibliometric analysis and literature survey of all Deep Learning (DL) methods used in video compression in recent years. ...
Proposed approaches have used Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), and various variants of Autoencoders (AEs) are used in their approaches ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/bdcc6020044
fatcat:w3hsnm4c6fantctozkwio5qklu
Deep Learning Based Homomorphic Secure Search-Able Encryption for Keyword Search in Blockchain Healthcare System: A Novel Approach to Cryptography
2022
Sensors
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In this research, we developed a unique deep-learning-based secure search-able blockchain as a distributed database using homomorphic encryption to enable users to securely access data via search. ...
Our suggested approach significantly improves security, anonymity, and monitoring of user behavior, resulting in a more efficient blockchain-based IoT system as compared to benchmark models. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s22020528
pmid:35062491
pmcid:PMC8779567
fatcat:2xusxey445bczcjjhtohd3zkc4
Joint relay and channel selection against mobile and smart jammer: A deep reinforcement learning approach
2021
IET Communications
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This paper investigates the joint relay and channel selection problem using a deep reinforcement learning (DRL) algorithm for cooperative communications in a dynamic jamming environment. ...
Concretely, a joint decision-making network composed of three sub-networks is designed and the independent learning method of each sub-network is proposed. ...
ACKNOWLEDGMENT This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 62071488 and 61771488.
ORCID Hongcheng Yuan https://orcid.org/0000-0001-8015-7400 ...
doi:10.1049/cmu2.12257
fatcat:wgthbbtfobdnhm4qgzjsih2ro4
Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: a deep learning approach
2021
Neural computing & applications (Print)
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To address these issues, this paper proposes a 5G-enabled real-time cardiovascular monitoring system for COVID-19 patients using deep learning. ...
Second, current monitoring platforms lack efficient streaming data processing mechanisms to cope with the large amount of cardiovascular data generated in real time. ...
The frequency of the communication channel is assigned by the public pool selection frequency. 5G infrastructure includes 5G access network and 5G core network. ...
doi:10.1007/s00521-021-06219-9
pmid:34248288
pmcid:PMC8255093
fatcat:lpquvxjubrdhdlncbt4cn2nbrm
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach
[article]
2020
arXiv
pre-print
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With this focus, this paper proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. ...
Furthermore, this model retains the advantages of LSTM unit in predicting time series data. ...
Schlegl et al. in [25] proposed a deep convolutional generative adversarial network, called AnoGAN, which detects abnormal anatomical images by learning a variety of normal anatomical images. ...
arXiv:2007.09712v1
fatcat:tkfhoj4rmrhnjmgp5zyn33xlai
Ambient Assisted Living: A Research on Human Activity Recognition and Vital Health Sign Monitoring using Deep Learning Approaches
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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Later, we survey the existing literature for HAR and VHSM based on sensor modality and deep learning approach used. ...
Next we present brief insights into sensor modalities and different deep learning architectures. ...
DEEP LEARNING ARCHITECTURES Deep learning, a subset of machine learning which models the several levels of features has become a trend in the area of image recognition, activity recognition, speech processing ...
doi:10.35940/ijitee.f1111.0486s419
fatcat:5mtkrtx54ndtbn2oms6ij7s5xy
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