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Real-Time Detection of Dictionary DGA Network Traffic using Deep Learning
[article]
2020
arXiv
pre-print
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Bilbo is the first parallel usage of a convolutional neural network (CNN) and a long short-term memory (LSTM) network for DGA detection. ...
Common DGA detection techniques fail to reliably detect DGA variants that combine random dictionary words to create domain names that closely mirror legitimate domains. ...
Deep learning rst addressed DGA detection with work by Woodbridge et al. ...
arXiv:2003.12805v1
fatcat:ck5mlz52wrfudluau6xm73ztny
Real-Time Detection of Dictionary DGA Network Traffic Using Deep Learning
2021
SN Computer Science
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Bilbo is the first parallel usage of a convolutional neural network (CNN) and a long short-term memory (LSTM) network for DGA detection. ...
Common DGA detection techniques fail to reliably detect DGA variants that combine random dictionary words to create domain names that closely mirror legitimate domains. ...
To prevent DGA-based malware from exfiltrating, disabling, or tampering with assets, institutions must detect malicious traffic as soon as possible. ...
doi:10.1007/s42979-021-00507-w
fatcat:fdtqbuugk5erjnziwdslj6ehm4
Detection of Algorithmically Generated Domain Names Using the Recurrent Convolutional Neural Network with Spatial Pyramid Pooling
2020
Entropy
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Domain generation algorithms (DGAs) use specific parameters as random seeds to generate a large number of random domain names to prevent malicious domain name detection. ...
We also propose a recurrent convolutional neural network with spatial pyramid pooling (RCNN-SPP) to extract discriminative and distinctive class features. ...
[24] proposed a novel criterion for creating a noise-free DGA/non-DGA dataset from real traffic and a CNN-based DGA detection model. ...
doi:10.3390/e22091058
pmid:33286827
pmcid:PMC7597131
fatcat:7bpkcqgcnvhmveahjpmjojou7i
Weakly Supervised Deep Learning for the Detection of Domain Generation Algorithms
2019
IEEE Access
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We show through experiments on a dataset with 50 million domain names that such heuristically labeled data is very useful in practice to improve the predictive accuracy of deep learning-based DGA classifiers ...
In contrast with traditional machine learning models, deep networks do not rely on human engineered features. ...
and has broader coverage for DGA family variations. ...
doi:10.1109/access.2019.2911522
fatcat:zg3lpj4xozasbg26qvofs7guiu
Artificial Intelligence in the Cyber Domain: Offense and Defense
2020
Symmetry
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In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. ...
[54] adopted recurrent neural networks (RNN) to identify domain names generated by DGAs with high precision. ...
Self collection:
2.3 million samples
FPR: <=1%
[56]
2018
DGA
domains
detection
RNN, CNN
Strings
Self collection:
2 million samples
ACC: 97-98%
[57]
2018
DGA
botnet
detection
LSTM ...
doi:10.3390/sym12030410
fatcat:7gyse3gaxjguhgkvfnbi7knkf4
AI'S Contribution to Ubiquitous Systems and Pervasive Networks Security – Reinforcement Learning vs Recurrent Networks
2021
Journal of Ubiquitous Systems and Pervasive Networks
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In this paper, a systematic review of this research was performed in regard to various attacks and an analysis of the trends and future fields of interest for the RL and recurrent network-based research ...
In a benchmarking model, [40] demonstrate that the classification of DGA has the best precision (with more than 96%) of a CNN-LSTM model in comparison to a simple CNN or LSTM model, and [63] go a step ...
This development aims to create a specific DGA trained with little data without leading to the over-fitting of the detection model. ...
doi:10.5383/juspn.15.02.001
fatcat:tcfmazejvngihlmlqbt3gop72a
Network Attacks Detection Methods Based on Deep Learning Techniques: A Survey
2020
Security and Communication Networks
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Afterwards, we present some benchmark datasets with descriptions and compare the performance of representing approaches to show the current working state of attack detection methods with deep learning ...
In this paper, we offer a review on attack detection methods involving strength of deep learning techniques. ...
To achieve robust performance in attack detection with CNN structure, an end-to-end encrypted traffic classification method based on one-dimensional CNN is presented by Wang et al. ...
doi:10.1155/2020/8872923
fatcat:dr5syy4pdzgktmjrpeyc2njvoe
Towards resilient machine learning for ransomware detection
[article]
2019
arXiv
pre-print
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Our focus is to emphasize necessary defense improvement in ML-based approaches for ransomware detection before deployment in the wild. ...
These approaches have achieved significant improvement in detection rates and lower false positive rates at large scale compared with traditional malware analysis methods. ...
XGB • Text-CNN detects 12.73% correctly and Random forest • Text-CNN detects 36.35% correctly. ...
arXiv:1812.09400v2
fatcat:mhcoygbisvbqpcah5pcf4ofcoa
Multistage speaker diarization of broadcast news
2006
IEEE Transactions on Audio, Speech, and Language Processing
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The baseline partitioner provides a high cluster purity, but has a tendency to split data from speakers with a large quantity of data into several segment clusters. ...
Second an additional clustering stage has been added, using a GMM-based speaker identification method. ...
The c-bic system also provides a high purity, with a much better coverage (2.9% purity error and 9.8% coverage error), reducing the overall error rate by almost 50%. ...
doi:10.1109/tasl.2006.878261
fatcat:wwgmunw3qnfd5gtkw6fvgtg4yq
Complex Document Classification and Localization Application on Identity Document Images
2017
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
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Our system is evaluated on several datasets with up to 3042 real documents (representing 64 classes) achieving an accuracy of 96.6%. ...
Then, the query image is matched against all models in the base. Unknown documents are rejected using an estimated quality based on the extracted document. ...
Our system is compared to CNN-based classification using the 'fast' network [5] on both FRA DB and BEL DB. ...
doi:10.1109/icdar.2017.77
dblp:conf/icdar/AwalGSF17
fatcat:ksxqwvw655fe7nwcbobrg4vzu4
Coral-Segmentation: Training Dense Labeling Models with Sparse Ground Truth
2017
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
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We fine tune state-of-the-art encoder-decoder CNN models for semantic segmentation thanks to a new proposed augmented labeling strategy. ...
Transfer learning techniques allow us to adapt existing deep learning models to new domains, even with small amounts of training data. ...
Acknowledgments This research has been partially funded by the European Union (CHIST-ERA IGLU), Spanish Government (projects DPI2015-65962-R, DPI2015-69376-R) and Aragon regional government (Grupo DGA ...
doi:10.1109/iccvw.2017.339
dblp:conf/iccvw/AlonsoCMTM17
fatcat:pjjv7m2jdrbzvce7rar75d2iju
Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions
[article]
2021
arXiv
pre-print
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Deep neural networks (DNNs) have largely boosted their performances on many concrete tasks, with the help of large amounts of training data and new powerful computation resources. ...
In this paper, we present the review of the recent advances with our suggestions on the new possible directions towards improving the efficiency of DNN-related visual recognition approaches. ...
) on three vision tasks including classification, detection, and segmentation with extensive experiments, and concluded that the use of a pretrained CNN with adequate fine-tuning outperformed or, in the ...
arXiv:2108.13055v2
fatcat:nf3lymdbvzgl7otl7gjkk5qitq
Classification of Hyperspectral Reflectance Images With Physical and Statistical Criteria
2020
Remote Sensing
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are pre-defined; (iii) includes a reject class, this class reduces misclassifications; (iv) high precision and recall, F 1 score is generally above 0.9 in our test. ...
Each class is characterized by physical criteria (detection of specific absorptions or shape features) or statistical criteria (use of dedicated spectral indices) over spectral reflectance. ...
funded by the DGA. ...
doi:10.3390/rs12142335
fatcat:nx3gttyd7jcrxlxe3iu7stkdrq
Revisiting Model's Uncertainty and Confidences for Adversarial Example Detection
[article]
2021
arXiv
pre-print
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The detection method is called Selective and Feature based Adversarial Detection (SFAD). ...
Moreover, results show that SFAD is fully robust against High Confidence Attacks (HCAs) for MNIST and partially robust for CIFAR10 datasets. ...
Acknowledgement The project is funded by both Région Bretagne (Brittany region), France, and direction générale de l'armement (DGA). ...
arXiv:2103.05354v2
fatcat:6wqxyjsdivcijafts6uee2ppgu
A system for the detection of polyphonic sound on a university campus based on CapsNet-RNN
2021
IEEE Access
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In recent decades, surveillance and home security systems based on video analysis have been proposed for the automatic detection of abnormal situations. ...
of event detection. ...
Ravi in [27] were introduced, namely DGA-Based Botnets and DNS Homographs Detection. ...
doi:10.1109/access.2021.3123970
fatcat:3j4oneylirhfxgxwsheor7pzwi
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