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Cyber Threat Detection based on Artificial Neural Networks using Event Profiles

Jonghoon Lee, Jonghyun Kim, Ikkyun Kim, Kijun Han
2019 IEEE Access  
For this work, we developed an AI-SIEM system based on a combination of event profiling for data preprocessing and different artificial neural network methods, including FCNN, CNN, and LSTM.  ...  The proposed technique converts multitude of collected security events to individual event profiles and use a deep learning-based detection method for enhanced cyber-threat detection.  ...  , three-layer multi-layer perceptron (MLP) with a softmax function in the final layer is same as a multi-class logistic regression model.  ... 
doi:10.1109/access.2019.2953095 fatcat:xnbf6scmkbfw5ioivgvkrintu4

Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection

Aida Kamišalić, Renata Kramberger, Iztok Fister
2021 Applied Sciences  
Special attention was paid to anomaly detection and fraud detection, which were identified as the most prolific applications of applying data mining methods on blockchain data.  ...  Data stored in a blockchain can also be considered to be big data, whereas data mining methods can be applied to extract knowledge hidden in the blockchain.  ...  [90] proposed a Deep Learning based intrusion detection system for the IoT. They used the NSL-KDD data set, that contains different attack scenarios and classes.  ... 
doi:10.3390/app11177987 fatcat:w54qaqlvobfdloqsyqj3lnloyu

Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions

Salahadin Seid Musa, Marco Zennaro, Mulugeta Libsie, Ermanno Pietrosemoli
2022 Future Internet  
content distribution, scalability and security based on content names, regardless of their location.  ...  ITS are smart enough to make decisions based on the status of a great variety of inputs.  ...  In [85] , a deep-learning-based content caching framework, DeepCache, is proposed. The popularity of the content is predicted using an LSTM-based model.  ... 
doi:10.3390/fi14070192 fatcat:knlyn5uaurarlhq7a5p66rwrgi

Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks [article]

Alberto Mozo, Ángel González-Prieto, Antonio Pastor, Sandra Gómez-Canaval, Edgar Talavera
2021 arXiv   pre-print
Due to the growing rise of cyber attacks in the Internet, flow-based data sets are crucial to increase the performance of the Machine Learning (ML) components that run in network-based intrusion detection  ...  real data is not used in the training of the ML-based detector.  ...  LSTMs) respectively would not provide any advantage with respect to FCNNs.  ... 
arXiv:2107.14776v1 fatcat:cbphmf6cpzccfd5vj3ntfsdzha

Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases [chapter]

Rajesh Singh, Anita Gehlot, Dharam Buddhi
2022 Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases  
Precise diagnosis of these diseases on time is very significant for maintaining a healthy life.  ...  A comparative study of different machine learning classifiers for chronic disease prediction viz Heart Disease & Diabetes Disease is done in this paper.  ...  The region-based segmentation will segment the data dependent on the taken-out features using GLCM algorithm.  ... 
doi:10.13052/rp-9788770227667 fatcat:da47mjbbyzfwnbpde7rgbrlppe

Mining and Learning With Graphs and Tensors

Namyong Park
We also design a meta-learning based approach for automatic graph learning model selection, which is up to 15x more accurate than using popular methods consistently.  ...  frameworks for large-scale tensor factorization, which decompose and summarize large tensors up to 180x faster than existing methods, with near-linear scalability.  ...  For SCORINGNETWORK, a two-layer FCNN with an architecture of [N F , 0.75 × N F , 1] was used.  ... 
doi:10.1184/r1/19891765.v1 fatcat:w47xj5l3snehjkrdmp77yqphve

Gore Classification and Censoring in Images [article]

William Larocque, University, My
It can also be used to reduce processing time and storage space by ensuring the segmentation model does not need to generate a censored image for every image submitted to the pipeline.  ...  Both models use pretrained Convolutional Neural Network (CNN) architectures and weights as part of their design and are fine-tuned using Machine Learning (ML).  ...  [154] used pseudo-3D convolutional blocks and LSTM layers. Audio feature extraction using a "VGGish" network which is a modified VGG convolutional network for audio.  ... 
doi:10.20381/ruor-27201 fatcat:meen3fqunjajhawijw2hsoejla