A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems
2021
Applied Sciences
Preserved Fulltext
This work proposes a sequential approach and evaluates the performance of a Random Forest (RF), a Multi-Layer Perceptron (MLP), and a Long-Short Term Memory (LSTM) on the CIDDS-001 dataset. ...
Intrusion Detection Systems (IDS) are important security mechanisms that can perform the timely detection of malicious events through the inspection of network traffic or host-based logs. ...
The objectives of our study can be described as follows: • Compare single-flow and multi-flow approaches for attack detection in the context of network-based intrusion detection systems; • Understand the ...
doi:10.3390/app11041674
fatcat:wznl3crwanhtbji6xy57ospe3i
Human Action Recognition Using Deep Multilevel Multimodal (M2) Fusion of Depth and Inertial Sensors
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
These highly informative features are served as input to a multi-class Support Vector Machine (SVM). ...
In most of the existing works, fusion is performed at a single level (feature level or decision level), missing the opportunity to fuse rich mid-level features necessary for better classification. ...
In [22] , novel and robust unsupervised method based on fusion of depth and inertial sensor data for HAR is proposed. ...
arXiv:1910.11482v1
fatcat:kgdgvdyrinhrfppfapm3oniu2a
A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection
[chapter]
2003
Proceedings of the 2003 SIAM International Conference on Data Mining
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2005; you can also visit the original URL.
The file type is application/pdf.
To benefit the anomaly detection framework, a procedure for extracting additional useful features is also implemented. ...
Intrusion detection corresponds to a suite of techniques that can be used to identify attacks against computers and network infrastructures. ...
Acknowledgments The authors are grateful to Richard Lippmann and Daniel Barbara for providing data sets and their useful comments. ...
doi:10.1137/1.9781611972733.3
dblp:conf/sdm/LazarevicEKOS03
fatcat:o27py5gcsvb25i7cttrdfsw2mi
A Methodological Review on Prediction of Multi-stage Hypovigilance Detection Systems using Multimodal Features
2021
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
ACKNOWLEDGMENT The authors would like to thank King Abdulaziz City for Science and Technology (KACST) and Deanship of Scientific Research center at Al Imam Mohammad ibn Saud Islamic university, for financing ...
this project entitled "Analysis and Modeling of Cloud Computing for Drivers Fatigue and Vigilance Monitoring" under the grant no. (0001-008-11-17-3). ...
A novel computer vision-based technique was developed in [45] to detect driver sleepiness from a video taken by a camera. The suggested approach was tested based on the YawDD public video dataset. ...
doi:10.1109/access.2021.3068343
fatcat:rbqdxyabsvah3ginlkd3gqkrwu
SDN-Enabled Hybrid DL-Driven Framework for the Detection of Emerging Cyber Threats in IoT
2021
Electronics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
We present an SDN-enabled architecture leveraging hybrid deep learning detection algorithms for the efficient detection of cyber threats and attacks while considering the resource-constrained IoT devices ...
The diversity of the IoT, on the one hand, leads to the benefits of the integration of devices into a smart ecosystem, but the heterogeneous nature of the IoT makes it difficult to come up with a single ...
In [45] , the authors proposed a two-stage hierarchical network intrusion detection (H2ID) approach. ...
doi:10.3390/electronics10080918
fatcat:rj4h2wx3zvfedfp5ygzxhyxiya
DEEP-AD: A Multimodal Temporal Video Segmentation Framework for Online Video Advertising
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
The proposed algorithm exploits various deep convolutional neural networks, involved at several stages. The video stream is first divided into shots based on a graph partition method. ...
INDEX TERMS Multimodal temporal video segmentation, thumbnail extraction from video scenes, commercial advertisement insertion based on semantic criterions, deep convolutional neural networks. 99584 VOLUME ...
The face detection, performed for each individual shot, is based on the Faster R-CNN approach [40] utilized here with Region Proposal Networks (RPN) [41] . ...
doi:10.1109/access.2020.2997949
fatcat:odg577q6nbcqffnt3ufjw5dol4
The use of computational intelligence in intrusion detection systems: A review
2010
Applied Soft Computing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2013; you can also visit the original URL.
The file type is application/pdf.
Intrusion detection based upon computational intelligence is currently attracting considerable interest from the research community. ...
good intrusion detection model. ...
In our view, these approaches contribute to intrusion detection in different ways. ...
doi:10.1016/j.asoc.2009.06.019
fatcat:5ntbfbejrveyzhmmelfh34qkiy
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2022 pre-print arXiv:2203.06935v1 fatcat:hzl5xptarfg53ncfus7jt4ezl4Other Versions
Affective computing is realized based on unimodal or multimodal data, primarily consisting of physical information (e.g., textual, audio, and visual data) and physiological signals (e.g., EEG and ECG signals ...
baseline dataset, fusion strategies for multimodal affective analysis, and unsupervised learning models. ...
Except for the efficient network architectures with attention networks or loss functions [272] , there is a fast and light manifold convolutional neural network (FLM-CNN) based on multi-scale encoding ...
arXiv:2203.06935v3
fatcat:h4t3omkzjvcejn2kpvxns7n2qe
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs). ...
In this paper, we propose E-GraphSAGE, a GNN approach that allows capturing both the edge features of a graph as well as the topological information for network intrusion detection in IoT networks. ...
CONCLUSIONS AND FUTURE WORK This paper presents a novel approach to network intrusion detection based on GNNs. ...
arXiv:2103.16329v8
fatcat:tvjjjufp6bglpnm23645rxb3he
Deep Structured Cross-Modal Anomaly Detection
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
A vast majority of existing anomaly detection methods predominately focused on data collected from a single source. ...
To this end, we propose a novel deep structured anomaly detection framework to identify the cross-modal anomalies embedded in the data. ...
CONCLUSIONS AND FUTURE WORK In this paper, we propose an novel cross-modal anomaly detection approach CMAD based on deep neural networks. ...
arXiv:1908.03848v1
fatcat:lh6u54buvfcidokxc4lvi6mcry
Continuous user authentication using multi-modal biometrics
2015
Computers & security
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Based on these findings, a novel architecture of multi-modal biometric authentication on mobile devices is proposed. ...
This research investigated three behavioural biometric techniques based on SMS texting activities and messages, looking to apply these techniques as a multi-modal biometric authentication method for mobile ...
Finally, the study based on the use of multi-modal biometric technique is demonstrated. Chapter 7 presents a novel mechanism for composite authentication. ...
doi:10.1016/j.cose.2015.06.001
fatcat:as4gtrnngbgmlmcay27kvezeba
A Novel RNN-GBRBM Based Feature Decoder for Anomaly Detection Technology in Industrial Control Network
2017
IEICE transactions on information and systems
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
In this paper, a novel method of anomaly detection in industrial control network is proposed based on RNN-GBRBM feature decoder. ...
However, traditional Network Intrusion Detection System makes significant use of pattern matching to identify malicious behaviors and have bad performance on detecting zero-day exploits in which a new ...
Given the promising capabilities of anomaly based network intrusion detection system (A-NIDS), this approach is currently a principal focus of research and development in the field of industrial control ...
doi:10.1587/transinf.2016icp0005
fatcat:aw5bcazimbdmbnkibzbwmu7qry
A review of novelty detection
2014
Signal Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Yeung and Ding [83] use HMMs for detection of intrusions based on shell command sequences within the network security domain. ...
Although the HMM is better suited for intrusion detection based on Unix system calls, the static modelling approach based on the information-theoretic technique outperformed the dynamic modelling approach ...
doi:10.1016/j.sigpro.2013.12.026
fatcat:ha6kc4bzhbajxbo2mdyh5cw5hu
CCTV Surveillance System, Attacks and Design Goals
2018
International Journal of Electrical and Computer Engineering (IJECE)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
In this paper, a 360-degree view presented on the assessment of the diverse CCTV video surveillance systems (VSS) of recent past and present in accordance with technology. ...
They have also been on the receiving end of bad press when some consider intrusiveness has outweighed the benefits. ...
[18] have proposed multi object tracking method is based on a generalized color histogram a novel DCH that is shown to be robust to color and brightness changes. ...
doi:10.11591/ijece.v8i4.pp2072-2082
fatcat:y2dzsbopxzg3rhxqp5rv362i5a
Data Analytics in the Internet of Things: A Survey
2019
Scalable Computing : Practice and Experience
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
A comprehensive survey on the identified key enablers including their role in IoT data analytics, use cases in which they have been applied and the corresponding IoT applications for the use cases is presented ...
Motivated to resolve this concern, this work investigates the key enablers for performing desired data analytics in IoT applications. ...
Accuracy: 64.88 per-
cent
[140] Activity
recog-
nition based on
multi-sensor data
Recurrent Neu-
ral Network
To predict future activities
of a resident
MIT dataset for activity
Accuracy: 90.85 ...
doi:10.12694/scpe.v20i4.1562
fatcat:y2fiya3q2bawhdg6hhczfpdbee
« Previous
Showing results 1 — 15 out of 914 results