Detecting attacks on IoT devices with probabilistic Bayesian neural networks and hunger games search optimization approaches

2021 ◽  
Author(s):  
Mesut Toğaçar
Keyword(s):  
Neural Networks ◽  
Bayesian Neural Networks ◽  
Hunger Games ◽  
Search Optimization ◽  
Iot Devices

scholarly journals An efficient pruning scheme of deep neural networks for Internet of Things applications

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chen Qi ◽  
Shibo Shen ◽  
Rongpeng Li ◽  
Zhifeng Zhao ◽  
Qing Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Internet Of Things ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Superior Performance ◽  
Compact Structure ◽  
Resource Limited ◽  
Benchmark Datasets ◽  
Iot Devices

AbstractNowadays, deep neural networks (DNNs) have been rapidly deployed to realize a number of functionalities like sensing, imaging, classification, recognition, etc. However, the computational-intensive requirement of DNNs makes it difficult to be applicable for resource-limited Internet of Things (IoT) devices. In this paper, we propose a novel pruning-based paradigm that aims to reduce the computational cost of DNNs, by uncovering a more compact structure and learning the effective weights therein, on the basis of not compromising the expressive capability of DNNs. In particular, our algorithm can achieve efficient end-to-end training that transfers a redundant neural network to a compact one with a specifically targeted compression rate directly. We comprehensively evaluate our approach on various representative benchmark datasets and compared with typical advanced convolutional neural network (CNN) architectures. The experimental results verify the superior performance and robust effectiveness of our scheme. For example, when pruning VGG on CIFAR-10, our proposed scheme is able to significantly reduce its FLOPs (floating-point operations) and number of parameters with a proportion of 76.2% and 94.1%, respectively, while still maintaining a satisfactory accuracy. To sum up, our scheme could facilitate the integration of DNNs into the common machine-learning-based IoT framework and establish distributed training of neural networks in both cloud and edge.

scholarly journals Bayesian Neural Networks with Maximum Mean Discrepancy regularization

2021 ◽  
Author(s):  
Jary Pomponi ◽  
Simone Scardapane ◽  
Aurelio Uncini
Keyword(s):  
Neural Networks ◽  
Maximum Mean Discrepancy ◽  
Bayesian Neural Networks

Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization

2021 ◽  
Author(s):  
Jiaru Zhang ◽  
Yang Hua ◽  
Zhengui Xue ◽  
Tao Song ◽  
Chengyu Zheng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Bayesian Neural Networks

scholarly journals A Simple Method of Residential Electricity Load Forecasting by Improved Bayesian Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Shubin Zheng ◽  
Qianwen Zhong ◽  
Lele Peng ◽  
Xiaodong Chai
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Load Forecasting ◽  
Forecast Model ◽  
Time Range ◽  
Simple Method ◽  
Load Forecast ◽  
Bayesian Neural Networks ◽  
Electricity Load ◽  
Electricity Load Forecasting

Electricity load forecasting is becoming one of the key issues to solve energy crisis problem, and time-series Bayesian Neural Network is one popular method used in load forecast models. However, it has long running time and relatively strong dependence on time and weather factors at a residential level. To solve these problems, this article presents an improved Bayesian Neural Networks (IBNN) forecast model by augmenting historical load data as inputs based on simple feedforward structure. From the load time delays correlations and impact factors analysis, containing different inputs, number of hidden neurons, historic period of data, forecasting time range, and range requirement of sample data, some advices are given on how to better choose these factors. To validate the performance of improved Bayesian Neural Networks model, several residential sample datasets of one whole year from Ausgrid have been selected to build the improved Bayesian Neural Networks model. The results compared with the time-series load forecast model show that the improved Bayesian Neural Networks model can significantly reduce calculating time by more than 30 times and even when the time or meteorological factors are missing, it can still predict the load with a high accuracy. Compared with other widely used prediction methods, the IBNN also performs a better accuracy and relatively shorter computing time. This improved Bayesian Neural Networks forecasting method can be applied in residential energy management.

Learning Uncertainty in Market Trend Forecast Using Bayesian Neural Networks

2020 ◽  
pp. 210-218
Author(s):  
Iwao Maeda ◽  
Hiroyasu Matsushima ◽  
Hiroki Sakaji ◽  
Kiyoshi Izumi ◽  
David deGraw ◽  
...  
Keyword(s):  
Neural Networks ◽  
Bayesian Neural Networks

Bayesian Neural Networks with Weight Sharing Using Dirichlet Processes

2020 ◽  
Vol 42 (1) ◽  
pp. 246-252 ◽  
Author(s):  
Wolfgang Roth ◽  
Franz Pernkopf
Keyword(s):  
Neural Networks ◽  
Dirichlet Processes ◽  
Bayesian Neural Networks
Sign in / Sign up

Export Citation Format

Share Document