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Generative Adversarial Networks and Image-Based Malware Classification
[article]
2022
arXiv
pre-print
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We then focus on Generative Adversarial Networks (GAN) for multiclass classification and compare our GAN results to other popular machine learning techniques, including Support Vector Machine (SVM), XGBoost ...
This result indicates that our GAN generated images would be of little value in adversarial attacks. ...
Machine Learning Models As its name suggests, the 𝑘-Nearest Neighbors (𝑘-NN) classifier simply uses the 𝑘 nearest neighbors from the training set to classify a new sample. ...
arXiv:2207.00421v1
fatcat:aomfb7mx2zb4nhrxvtv5fkqlhm
Adversarial Reciprocal Points Learning for Open Set Recognition
[article]
2021
arXiv
pre-print
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To further estimate the unknown distribution from open space, an instantiated adversarial enhancement method is designed to generate diverse and confusing training samples, based on the adversarial mechanism ...
Extensive experimental results on various benchmark datasets indicate that the proposed method is significantly superior to other existing approaches and achieves state-of-the-art performance. ...
The best-known prototype learning method is k-nearest-neighbors (KNN). ...
arXiv:2103.00953v3
fatcat:klobayh4zra7no6q5djo273wzy
The Next Generation Cognitive Security Operations Center: Adaptive Analytic Lambda Architecture for Efficient Defense against Adversarial Attacks
2019
Big Data and Cognitive Computing
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Specifically, it uses an Extreme Learning Machine neural network with Gaussian Radial Basis Function kernel (ELM/GRBFk) for the batch data analysis and a Self-Adjusting Memory k-Nearest Neighbors classifier ...
The proposed λ-Architecture Network Flow Forensics Framework (λ-ΝF3) is an efficient cybersecurity defense framework against adversarial attacks. ...
the Euclidean distance between two data points and N k (x, Z) returns the k nearest neighbors of x in Z. ...
doi:10.3390/bdcc3010006
fatcat:qskf3u5xkfephh5tcis3ibo35i
A novel semi-supervised framework for UAV based crop/weed classification
2021
PLoS ONE
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In this study, the development of an optimized semi-supervised learning approach is proposed, offering a semi-supervised generative adversarial network for crops and weeds classification at early growth ...
The proposed algorithm consists of a generator that provides extra training data for the discriminator, which distinguishes weeds and crops using a small number of image labels. ...
Materials & methods
Semi-Supervised Generative Adversarial Network (SGAN) Generative Adversarial Network (GAN) frameworks were first introduced for training deep generative models using game theory ...
doi:10.1371/journal.pone.0251008
pmid:33970938
pmcid:PMC8109769
fatcat:dusllikrxfex5chgtn3jv27odm
A General Framework for Adversarial Label Learning
2021
Journal of machine learning research
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We propose a weakly supervised method-adversarial label learning-that trains classifiers to perform well when noisy and possibly correlated labels are provided. ...
We consider the task of training classifiers without fully labeled data. ...
Acknowledgments We thank NVIDIA for their support through the GPU Grant Program and Amazon for their support via the AWS Cloud Credits for Research program. ...
dblp:journals/jmlr/ArachieH21
fatcat:ob46gpzd55boreptmr7erlabbu
GAN based Augmentation for Improving Anomaly Detection Accuracy in Host-based Intrusion Detection Systems
2020
International journal of engineering research and technology
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This study proposes a methodology for anomaly detection in HIDS using supervised and semi-supervised anomaly detection approaches by applying GAN (Generative Adversarial Network) based data augmentation ...
To avoid the problem and to enhance the low predictive accuracy, it might need to augment minority datasets through the creation of new samples. ...
Many studies have improved HIDSs by evaluating the recognition of abnormal patterns using HMM (Hidden Markov Model), KNN (K-Nearest Neighbor), Logistic Regression, SVM (Support Vector Machine), Ensemble ...
doi:10.37624/ijert/13.11.2020.3987-3996
fatcat:p3vvyz3omfhpzlhghodcw5hnp4
Oversampling Adversarial Network for Class-Imbalanced Fault Diagnosis
[article]
2020
arXiv
pre-print
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Class-imbalance problem requires a robust learning system which can timely predict and classify the data. We propose a new adversarial network for simultaneous classification and fault detection. ...
We empirically demonstrate that; (i) the discriminator trained with a generator to generates samples from a mixture of normal and faulty data distribution which can be considered as a fault detector; ( ...
Acknowledgment This research is partly supported by NSFC, China (No: 61806125, 61977046), Committee of Science and Technology, Shanghai, China (No. 19510711200). ...
arXiv:2008.03071v1
fatcat:2iczojd6k5bh3hwcfun6cdeyvq
Open-world Machine Learning: Applications, Challenges, and Opportunities
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2105.13448v1 fatcat:y5u3uixeijgdxpybibivpatmaa
It will also help to select applicable methodologies and datasets to explore this further. ...
We have searched through different online digital libraries and scrutinized the work done in the last decade. This paper presents a systematic review of various techniques for OWML. ...
In [31] , authors proposed Open-Set Nearest Neighbor (OSNN) to address the issues in multiclass classifiers. It is an extension of the nearest neighbor (NN) [63] for open-set. ...
arXiv:2105.13448v2
fatcat:rv6f42sdvvajnhub4uguuhb2cy
Hyper-Parameter Optimization of Semi-Supervised GANs Based-Sine Cosine Algorithm for Multimedia Datasets
2022
Computers Materials & Continua
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Generative Adversarial Networks (GANs) are neural networks that allow models to learn deep representations without requiring a large amount of training data. ...
Semi-Supervised GAN Classifiers are a recent innovation in GANs, where GANs are used to classify generated images into real and fake and multiple classes, similar to a general multi-class classifier. ...
Then, k of its nearest neighbors is found (typically, k = 5). ...
doi:10.32604/cmc.2022.027885
fatcat:6yfmyjc4vvhllo3pfjsb4vjusy
Deep Learning and Open Set Malware Classification: A Survey
[article]
2020
arXiv
pre-print
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Under the situation of missing unknown training samples, the OSR system should not only correctly classify the known classes, but also recognize the unknown class. ...
This survey provides an overview of different deep learning techniques, a discussion of OSR and graph representation solutions and an introduction of malware classification systems. ...
Other than DNN, [26] introduces an extension for the Nearest Neighbors classifier.
LEARNING GRAPH REPRESENTATION Hamilton et al. ...
arXiv:2004.04272v1
fatcat:332sfs7davh2hkxoehiyjta2y4
Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGAN
[article]
2022
arXiv
pre-print
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To this end, a novel algorithm based on Conditional Generative Adversarial Networks (CGANs) is introduced. ...
The proposed method is validated on a real-world bearing dataset, and fault data are generated for different conditions. ...
, and collaboration of Fraunhofer IEM, Düspohl Gmbh, and Encoway Gmbh from Germany in this research. ...
arXiv:2206.12076v2
fatcat:zz2zwd5akvcedm6appme3pyhva
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
[article]
2019
arXiv
pre-print
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These statistics can be easily computed and calibrated by randomly corrupting inputs. ...
In our experiments, we show that it is even possible to correct test time predictions for adversarial attacks with high accuracy. ...
Acknowledgements We would like to thank Sebastian Nowozin, Aurelien Lucchi, Michael Tschannen, Gary Becigneul, Jonas Kohler and the dalab team for insightful discussions and helpful comments. ...
arXiv:1902.04818v2
fatcat:og63uhtpz5fy7mcyrg6lpsscem
Darknet Traffic Classification and Adversarial Attacks
[article]
2022
arXiv
pre-print
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This research aims to improve darknet traffic detection by assessing Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and Auxiliary-Classifier Generative Adversarial ...
We demonstrate that our best-performing classifier can be defeated by such attacks, and we consider ways to deal with such adversarial attacks. ...
They used k-Nearest Neighbors (kNN), Multi-layer Perceptron (MLP), RF, DT, and Gradient Boosting (GB) to do binary and multiclass classification. ...
arXiv:2206.06371v1
fatcat:eyb7aymv7jedlmmeiaphngfiaa
Applications of Multi-Label Classification
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
Preserved Fulltext
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The absence of labels and the bad quality of data is a prevailing challenge in numerous data mining and machine learning problems. ...
The performance of a model is limited by available data samples with few labels for training. These problems are ultra-critical in multi-label classification, which usually needs clean data. ...
Some classification algorithms or models have been adapted to the multi-label task, without requiring problem transformations such as boosting, k-nearest neighbors, decision trees, neural networks etc. ...
doi:10.35940/ijitee.d1008.0394s220
fatcat:4bdfhdhgkrgrhlris7nemahg3m
p-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning
[article]
2018
arXiv
pre-print
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Recent advances in learning Deep Neural Network (DNN) architectures have received a great deal of attention due to their ability to outperform state-of-the-art classifiers across a wide range of applications ...
engine queries over Tor, defeating fingerprinting defenses, and fingerprinting TLS-encrypted websites. ...
[38] utilized k-Nearest Neighbors (k-NN) with a new weight learning technique, Panchenko et al. ...
arXiv:1711.03656v2
fatcat:fhuh25pevvbrxd4p7gty36azaa
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