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Trustworthy Intrusion Detection in E-Healthcare Systems
2021
Frontiers in Public Health
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In Internet of Things (IoT)-based network systems (IoT-net), intrusion detection systems (IDS) play a significant role to maintain patient health records (PHR) in e-healthcare. ...
In the proposed security model, the experiments present a security tool that helps to detect malicious network traffic. ...
Similarly, cloud networks are based on four types: private, public, hybrid, and community (6, 7). In a cloud networking environment, problems in security and authorization are the key risks. ...
doi:10.3389/fpubh.2021.788347
pmid:34926397
pmcid:PMC8678532
fatcat:4iso64ixvndtniok2igg5xapeq
IEEE Access Special Section Editorial: Artificial Intelligence in Cybersecurity
2020
IEEE Access
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intrusion detection classification model based on deep belief network. ...
VOLUME 8, 2020 The article ''An optimization method for intrusion detection classification model based on deep belief network,'' by Wei et al., proposes a new joint optimization algorithm to optimize the ...
doi:10.1109/access.2020.3021604
dblp:journals/access/ChenQCYLPZS20
fatcat:mjvkkgt3wjfo3ditb4eibdc3s4
Automatic Building of a Powerful IDS for The Cloud Based on Deep Neural Network by Using a Novel Combination of Simulated Annealing Algorithm and Improved Self- Adaptive Genetic Algorithm
2022
International Journal of Communication Networks and Information Security
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To address the aboveissues, we propose a smart approach to construct automatically an efficient and effective anomaly network IDS based on Deep Neural Network, by using a novel hybrid optimization framework ...
Our approach consists of using ISAGASAA with the aim of seeking the optimal or near optimal combination of most pertinent values of the parametersincluded in building of DNN based IDS or impacting its ...
Acknowledgement We would like to thank all members of Computer Science and Systems Laboratory within Department of Mathematics and Computer in Faculty of Sciences Ain Chock, Hassan II University for precious ...
doi:10.17762/ijcnis.v14i1.5264
fatcat:qw2dmoh3cbaynp3eqt6muu4r7y
A Survey on Intrusion Detection Systems for Fog and Cloud Computing
2022
Future Internet
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user actions, all of which are based on the use of established rules for cloud computing. ...
Moreover, intrusion detection systems are widely adopted solutions to monitor and analyze network traffic and detect anomalies that can help identify ongoing adversarial activities, trigger alerts, and ...
An Anomaly-Based Intrusion Detection System (AIDS) Recent research [31] has considered an anomaly-based intrusion detection system (AIDS), which is an approach to detect intentional attacks into network ...
doi:10.3390/fi14030089
fatcat:wxlz2xeduzdpvbyf63rwosal7e
Table of Contents
2018
2018 IEEE 4th International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS)
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Learning Based Online Anomaly Intrusion Detection 5 Khaled Alrawashdeh (University of Cincinnati) and Carla Purdy (Uinversity of Cincinnati) Research on a New Metadata Model of Political Event Data Set ...
for Content-Based Publish/Subscribe System 177 Guifang Cao (Henan University) Non-local Data Fetch Scheme Based on Delay Distribution for Hadoop Clusters in Public Cloud 188 Ravindra Sandaruwan Ranaweera ...
doi:10.1109/bds/hpsc/ids18.2018.00004
fatcat:ehjyuloblrbwjb4i2ojzeekg3q
Deep Learning Based Intrusion Detection in Cloud Services for Resilience Management
2022
Computers Materials & Continua
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This paper focuses on designing an intelligent deep learning based approach for detecting cloud anomalies in real time to make it more resilient. ...
The deep learning architectures-based anomaly detection mechanisms uses various monitoring metrics characterize the normal behavior of cloud services and identify the abnormal events. ...
Convolutional Neural Network (CNN) based detection algorithms have been proposed in literatures which uses Network Security Laboratory (NSL)-Knowledge Discovery in Databases (KDD) and University of South ...
doi:10.32604/cmc.2022.022351
fatcat:7mcqneanq5hf5c4hbqjg5uwm3a
Study of Machine Learning for Cloud Assisted IoT Security as a Service
2021
Sensors
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ML based IDS (ML-IDS) normally detect network traffic anomalies caused by known attacks as well as newly introduced attacks. ...
Machine learning (ML) has been emerging as a viable solution for intrusion detection systems (IDS) to secure IoT devices against different types of attacks. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s21041034
pmid:33546394
fatcat:6uxbwk6e3ncsjloe4vabcncgby
Top Cited Article in Informatics Engineering Research: October 2020
2020
Zenodo
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This has led to the study of informatics to solve privacy, security, healthcare, education, poverty, and challenges in our environment. ...
The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on the human use of computing fields such as communication, mathematics, multimedia, and ...
Grance, "Guidelines on Security and Privacy in Public CloudComputing," Computer Security Division, Information Technology Laboratory, National Institute of Standards and Technology, Special Publication ...
doi:10.5281/zenodo.4269782
fatcat:ml2o2bq4t5eb7pi3cdi6fjc36y
Intrusion Detection Method Based on Support Vector Machine and Information Gain for Mobile Cloud Computing
2020
International Journal of Network Security
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Intrusion detection system (IDS) has become an important security method that monitors and investigates the network security in mobile cloud computing (MCC). ...
To counter these limitations, an intrusion detection method based on support vector machine (SVM) and information gain (IG) for MCC was proposed in this paper. ...
Acknowledgments This work is supported by the National Natural Science Foundation of China (No. 61862041), the Research Project in Universities of Education Department of Gansu Province (2017B-16, 2018A ...
dblp:journals/ijnsec/MugaboZ20
fatcat:ugi7j5macvecpkbllb53xuu4te
Machine Learning for Anomaly Detection: A Systematic Review
2021
IEEE Access
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Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000. ...
Ali Bou Nassif and co-authors would like to thank the University of Sharjah and OpenUAE Research and Development Group for funding this research study. ...
"Conflict of Interest: The authors declare that they have no competing interests". "Informed consent: This study does not involve any experiments on animals or humans". ...
doi:10.1109/access.2021.3083060
fatcat:vv7qthbvqjdz7ksm3yosulk22q
Machine Learning and Deep Learning Approaches for CyberSecuriy: A Review
2022
IEEE Access
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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 ...
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 ...
A comprehensive assessment of network-based intrusion detection systems was offered in [10] , in which they stressed the need for labeling data when doing evaluation and training on anomaly-based intrusion ...
doi:10.1109/access.2022.3151248
fatcat:3h6qhrddkbfipodxapeevn344q
SECURE RESILIENT EDGE CLOUD DESIGNED NETWORK
2019
IEICE transactions on communications
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This paper focuses on three main aspects of the CPS: a) resource management in mobile cloud computing; b) information management in dynamic distributed databases; and c) biological-inspired intrusion detection ...
, secure, resilient Edge Cloud (EC) computing. ...
To this end, we propose an anomaly-based bio-inspired intrusion detection system (BioIDS) that utilizes intrusion detection approaches followed by the human body to detect cyber-attacks targeting an IoT ...
doi:10.1587/transcom.2019nri0002
fatcat:4tztlpsa6be63ncstuyn7b2r5m
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
2019
IEEE Access
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anomaly detection, Internet traffic classification, and quality of service optimization. ...
While a few survey papers focusing on applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in the literature. ...
ANOMALY/INTRUSION DETECTION The increasing use of networks in every domain has increased the risk of network intrusions, which makes user privacy and the security of critical data vulnerable to attacks ...
doi:10.1109/access.2019.2916648
fatcat:xutxh3neynh4bgcsmugxsclkna
Guest Editorial: Special Issue on Data Analytics and Machine Learning for Network and Service Management—Part II
2021
IEEE Transactions on Network and Service Management
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In "Hierarchical Anomaly-Based Detection of Distributed DNS Attacks on Enterprise Networks," Lyu et al. ...
In "WIDS: An Anomaly Based Intrusion Detection System for Wi-Fi (IEEE 802.11) Protocol," Satam and Hariri [item 21) in the Appendix] introduce a wireless intrusion detection system using an anomaly behavior ...
doi:10.1109/tnsm.2021.3058742
fatcat:b6lx4i75krcovcqereinvjf6mu
A Detailed Analysis of Benchmark Datasets for Network Intrusion Detection System
2021
Asian Journal of Research in Computer Science
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As a result, organizations are working to increase the level of security by using attack detection techniques such as Network Intrusion Detection System (NIDS), which monitors and analyzes network flow ...
There are a lot of researches proposed to develop the NIDS and depend on the dataset for the evaluation. Datasets allow evaluating the ability in detecting intrusion behavior. ...
Mostly, NIDS follows one of the two major detection methods: Anomaly-based Intrusion Detection System (AIDS) and Signature-based Intrusion Detection System (SIDS). ...
doi:10.9734/ajrcos/2021/v7i430185
fatcat:ymje2jsbzje7nocqdxpsbpsvii
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