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Density-Based LLE Algorithm for Network Forensics Data
2011
International Journal of Modern Education and Computer Science
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In this paper, a schema is proposed to reduce the data of the forensics using manifold learning. Manifold learning is a popular recent approach to nonlinear dimensionality reduction. ...
Index Terms --Data Reduction, Network Forensics, Manifold Learning, LLE I INTRODUCE With the enormous growth of computer networks usage and the huge increase in the number of applications running on top ...
V FUTURE WORK Manifold learning is a good way to reduce the dimensional, especially, the manifold learning can maintain the topology of the data set, which can supply rich information for the data clustering ...
doi:10.5815/ijmecs.2011.01.08
fatcat:2diwnjsemrhx7bytgkaoekuzni
Developing New Approaches for Intrusion Detection in Converged Networks
[chapter]
2011
VoIP Technologies
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Dimensionality reduction and statistical methods for intrusion detection Dimensionality reduction methods allow detection and estimation in a manifold of smaller dimension than the data stream. ...
The collected forensic data is sent to a network forensics analyzer for further analysis. This data is used to discover and reconstruct attacking behaviors. ...
the Internet functions as a best-effort network without Quality of Service guarantee and voice data cannot be retransmitted. ...
doi:10.5772/13885
fatcat:jtuc2ttovfhafezsvvxjxfpndy
Automated identification of transiting exoplanet candidates in NASA Transiting Exoplanets Survey Satellite (TESS) data with machine learning methods
[article]
2021
arXiv
pre-print
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Existing and new features of the data, based on various observational parameters, are constructed and used in the AI/ML analysis by employing semi-supervised and unsupervised machine learning techniques ...
A novel artificial intelligence (AI) technique that uses machine learning (ML) methodologies combines several algorithms, which were developed by ThetaRay, Inc., is applied to NASA's Transiting Exoplanets ...
Acknowledgment We acknowledge the use of public TOI Release data from pipelines at the TESS Science Office and at the TESS Science Processing Operations Center. ...
arXiv:2102.10326v2
fatcat:ayhwdzr4ejhmlmupvv2qwvyusa
Intrusion Detection and Ubiquitous Host to Host Encryption
[article]
2017
arXiv
pre-print
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In an encrypted world, signature based event detection is unlikely to prove useful. ...
How can network administrators and operators, responsible for securing networks against hostile activity, protect a network they cannot see? ...
By the assumption that there is underlying structure/patterns in the data, the problem can be formulated as a manifold learning task. ...
arXiv:1711.08075v1
fatcat:2bp3fhykorb3piyg5aizwoxv2e
The Role of Hyperspectral Imaging: A Literature Review
2018
International Journal of Advanced Computer Science and Applications
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The proposed idea can be useful for further research in the field of hyperspectral imaging using deep learning. ...
Moreover, in the forensic context, the novel methods involving deep neural networks are elaborated in this paper. ...
For better understanding about hyperspectral imaging using deep learning, we demonstrated the process of feature extraction using the deep neural network. ...
doi:10.14569/ijacsa.2018.090808
fatcat:54bc7yptrrddhcqd4snkbkuxna
Source Generator Attribution via Inversion
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1905.02259v1 fatcat:jizskg6fmfhyhkkz3wmrt7ycku
With advances in Generative Adversarial Networks (GANs) leading to dramatically-improved synthetic images and video, there is an increased need for algorithms which extend traditional forensics to this ...
Source camera attribution algorithms using various cues have addressed this need for imagery captured by a camera, but there are fewer options for synthetic imagery. ...
Acknowledgements We thank Ning Yu for sharing data and pre-trained face generators used in this work, and Asongu Tambo for helpful suggestions. ...
arXiv:1905.02259v2
fatcat:5w3ykygs6zfrja2dagiekswujq
Data-Driven Multi-Microphone Speaker Localization on Manifolds
2020
Foundations and Trends® in Signal Processing
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pertain to a low-dimensional manifold that can be inferred from data using nonlinear dimensionality reduction techniques. ...
term, imposing a smoothness constraint on possible solutions with respect to a manifold learned in a data-driven manner. ...
doi:10.1561/2000000098
fatcat:a7et5bmprvcvxajwsx73j3lywy
Multi-Source Multi-Domain Data Fusion for Cyberattack Detection in Power Systems
2021
IEEE Access
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Still, manifold learning is explored for the purpose of feature reduction to train the supervised learning classifier. ...
MANIFOLD LEARNING PCA for feature reduction does not perform well when there are nonlinear relationships within the features. ...
doi:10.1109/access.2021.3106873
fatcat:4aemwsqnunhvpavu426fzkvhg4
Unsupervised Audiovisual Synthesis via Exemplar Autoencoders
[article]
2021
arXiv
pre-print
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training data for the input speaker. ...
We use Exemplar Autoencoders to learn the voice, stylistic prosody, and visual appearance of a specific target exemplar speech. ...
They have traditionally been used for dimensionality reduction or feature learning, though variational formulations have extended them to full generative models that can be used for probabilistic synthesis ...
arXiv:2001.04463v3
fatcat:ef7dbok5bjhn3or4bj5d45rtre
Face Processing for Security: A Short Review
[chapter]
2010
Advances in Intelligent and Soft Computing
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Face detection is the first step for the face recognition systems, posing its own challenges. Face recognition is essentially a classification problem, which can be a large multiclass problem. ...
Statistical learning for recognition algorithms Images of faces, represented as high-dimensional pixel arrays, often belong to a manifold of lower dimension. ...
The input of this 2D-HMM process is the output of the artificial neural network (ANN) applied to the input image to perform dimensionality reduction. ...
doi:10.1007/978-3-642-16626-6_10
dblp:conf/cisis-spain/MarquesG10
fatcat:4hwbsiw72fdjzdwkqan672cdlu
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
2019
IEEE Access
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In addition, unsupervised learning can unconstrain us from the need for labeled data and manual handcrafted feature engineering, thereby facilitating flexible, general, and automated methods of machine ...
Recently, there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services, such as traffic engineering, ...
Finally, denoising autoencoders are used to learn the mapping of a corrupted data point to its original location in the data space in an unsupervised manner for manifold learning and reconstruction distribution ...
doi:10.1109/access.2019.2916648
fatcat:xutxh3neynh4bgcsmugxsclkna
Deep Learning in Information Security
[article]
2018
arXiv
pre-print
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Consequently, representations that are used to solve a task are learned from the data instead of being manually designed. ...
Deep Learning is a sub-field of machine learning, which uses models that are composed of multiple layers. ...
Matching networks for one shot
learning. ...
arXiv:1809.04332v1
fatcat:xfb7lgrkw5cirdl3qvmg3ssnbi
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
[article]
2017
arXiv
pre-print
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Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly ...
Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of ...
[256] Manifold Learning Proposed a manifold learning based visualization tool for network traffic visualization and anomaly detection.
Internet Traffic Lopez et al. ...
arXiv:1709.06599v1
fatcat:llcg6gxgpjahha6bkhsitglrsm
The interface between forensic science and technology: how technology could cause a paradigm shift in the role of forensic institutes in the criminal justice system
2015
Philosophical Transactions of the Royal Society of London. Biological Sciences
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As the amounts of data grow very rapidly, there is a need for data reduction to remain cost effective. Several methods exist for intelligent data analysis and triage. ...
In deep learning, different representations can provide different explanations of the factors for the data. ...
We received no funding for this study. ...
doi:10.1098/rstb.2014.0264
pmid:26101289
pmcid:PMC4581008
fatcat:q3h4kjueybhbledqrt7dxv7dri
Association of Face and Facial Components Based on CNN and Transfer Subspace Learning for Forensics Applications
2020
SN Computer Science
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A major challenge during a forensic investigation is the association of facial components with a relevant face and this paper addresses this concern using the proposed transfer learning methodology. ...
Convolutional neural network (CNN) is used to extract the features of the biometrics and then they are given to a regularization framework for reducing the probability distribution difference between them ...
well as a forensic sketch photo recognition using deep learning networks [9] , matching the skull of a missing person with his/her photo [10] , etc. ...
doi:10.1007/s42979-020-00280-2
fatcat:qanw7rbe3jezdav77mfquocbau
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