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Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems

Nuno Oliveira, Isabel Praça, Eva Maia, Orlando Sousa
2021 Applied Sciences  
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]

Zeeshan Ahmad, Naimul Khan
2019 arXiv   pre-print
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]

Aleksandar Lazarevic, Levent Ertoz, Vipin Kumar, Aysel Ozgur, Jaideep Srivastava
2003 Proceedings of the 2003 SIAM International Conference on Data Mining  
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

Qaisar Abbas, Abdullah Alsheddy
2021 IEEE Access  
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

Danish Javeed, Tianhan Gao, Muhammad Taimoor Khan
2021 Electronics  
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

Ruxandra Tapu, Bogdan Mocanu, Titus Zaharia
2020 IEEE Access  
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

Shelly Xiaonan Wu, Wolfgang Banzhaf
2010 Applied Soft Computing  
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]

Yan Wang, Wei Song, Wei Tao, Antonio Liotta, Dawei Yang, Xinlei Li, Shuyong Gao, Yixuan Sun, Weifeng Ge, Wei Zhang, Wenqiang Zhang
2022 arXiv   pre-print
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]

Wai Weng Lo, Siamak Layeghy, Mohanad Sarhan, Marcus Gallagher, Marius Portmann
2022 arXiv   pre-print
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]

Yuening Li, Ninghao Liu, Jundong Li, Mengnan Du, Xia Hu
2019 arXiv   pre-print
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

Hataichanok Saevanee, Nathan Clarke, Steven Furnell, Valerio Biscione
2015 Computers & security  
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

Hua ZHANG, Shixiang ZHU, Xiao MA, Jun ZHAO, Zeng SHOU
2017 IEICE transactions on information and systems  
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

Marco A.F. Pimentel, David A. Clifton, Lei Clifton, Lionel Tarassenko
2014 Signal Processing  
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

Muthusenthil B., Hyun Sung Kim
2018 International Journal of Electrical and Computer Engineering (IJECE)  
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

Tausifa Jan Saleem, Mohammad Ahsan Chishti
2019 Scalable Computing : Practice and Experience  
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
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