scholarly journals Hybrid Deep-Learning Model to Detect Botnet Attacks over Internet of Things Environments

2021 ◽  
Author(s):  
Mohammed Y. Alzahrani ◽  
Alwi M Bamhdi
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Short Term Memory ◽  
Weighted Average ◽  
Learning Model ◽  
Attack Detection ◽  
Data Sets ◽  
Intelligence Gathering ◽  
Deep Learning Model

Abstract In recent years, the use of the internet of things (IoT) has increased dramatically, and cybersecurity concerns have grown in tandem. Cybersecurity has become a major challenge for institutions and companies of all sizes, with the spread of threats growing in number and developing at a rapid pace. Artificial intelligence (AI) in cybersecurity can to a large extent help face the challenge, since it provides a powerful framework and coordinates that allow organisations to stay one step ahead of sophisticated cyber threats. AI provides real-time feedback, helping rollover daily alerts to be investigated and analysed, effective decisions to be made and enabling quick responses. AI-based capabilities make attack detection, security and mitigation more accurate for intelligence gathering and analysis, and they enable proactive protective countermeasures to be taken to overwhelm attacks. In this study, we propose a robust system specifically to help detect botnet attacks of IoT devices. This was done by innovatively combining the model of a convolutional neural network with a long short-term memory algorithm mechanism to detect two common and serious IoT attacks (BASHLITE and Mirai) on four types of security camera. The data sets, which contained normal malicious network packets, were collected from real-time lab-connected camera devices in IoT environments. The results of the experiment showed that the proposed system achieved optimal performance, according to evaluation metrics. The proposed system gave the following weighted average results for detecting the botnet on the Provision PT-737E camera: camera precision: 88%, recall: 87% and F1 score: 83%. The results of system for classifying botnet attacks and normal packets on the Provision PT-838 camera were 89% for recall, 85% for F1 score and 94%, precision. The intelligent security system using the advanced deep learning model was successful for detecting botnet attacks that infected camera devices connected to IoT applications.

Deep Learning Model for Spatial Interpolation of Real-Time Seismic Intensity

2020 ◽  
Vol 91 (6) ◽  
pp. 3433-3443
Author(s):  
Ryota Otake ◽  
Jun Kurima ◽  
Hiroyuki Goto ◽  
Sumio Sawada
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Weighted Average ◽  
Learning Model ◽  
Seismic Intensity ◽  
Validation Data ◽  
Seismic Stations ◽  
Geological Information ◽  
Deep Learning Model ◽  
Better Than

Abstract Spatial distribution of seismic intensity plays an important role in emergency response during and immediately after an earthquake. In this study, we propose a deep learning model to predict the seismic intensity based on only the observation records at the seismic stations in a surrounding area. The deep learning model is trained using the observation records at both the input and target stations, and no geological information is used. Once the model is developed, for example, using the data from a temporal seismic array, the model can spatially interpolate the seismic intensity from the sparse layout of the seismic stations. The model consists of long short-term memory cells, which are well-established neural network components for time series analysis. We used observed seismograms in 1996 through 2019 at the Kyoshin Network (K-NET) and Kiban–Kyoshin Network (KiK-net) stations located in the northeastern part of Japan. In our deep learning model, approximately 85% of validation data is successfully classified into seismic intensity scales, which is better than adopting either the maximum or weighted average of the input data. We also apply the deep learning model to earthquake early warning (EEW). The model can predict the seismic intensity accurately and provides a long warning time. We concluded that our approach is a possible future solution for increasing the accuracy of EEW.

scholarly journals Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5606
Author(s):  
Yung-Hui Li ◽  
Latifa Nabila Harfiya ◽  
Kartika Purwandari ◽  
Yue-Der Lin
Keyword(s):  
Blood Pressure ◽  
Deep Learning ◽  
Real Time ◽  
Short Term Memory ◽  
Blood Pressure Monitoring ◽  
Time Estimation ◽  
Learning Model ◽  
Accurate Estimation ◽  
Arterial Blood ◽  
Deep Learning Model

Blood pressure monitoring is one avenue to monitor people’s health conditions. Early detection of abnormal blood pressure can help patients to get early treatment and reduce mortality associated with cardiovascular diseases. Therefore, it is very valuable to have a mechanism to perform real-time monitoring for blood pressure changes in patients. In this paper, we propose deep learning regression models using an electrocardiogram (ECG) and photoplethysmogram (PPG) for the real-time estimation of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. We use a bidirectional layer of long short-term memory (LSTM) as the first layer and add a residual connection inside each of the following layers of the LSTMs. We also perform experiments to compare the performance between the traditional machine learning methods, another existing deep learning model, and the proposed deep learning models using the dataset of Physionet’s multiparameter intelligent monitoring in intensive care II (MIMIC II) as the source of ECG and PPG signals as well as the arterial blood pressure (ABP) signal. The results show that the proposed model outperforms the existing methods and is able to achieve accurate estimation which is promising in order to be applied in clinical practice effectively.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

scholarly journals Application of Rough and Fuzzy Set Theory for Prediction of Stochastic Wind Speed Data Using Long Short-Term Memory

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 924
Author(s):  
Moslem Imani ◽  
Hoda Fakour ◽  
Wen-Hau Lan ◽  
Huan-Chin Kao ◽  
Chi Ming Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set Theory ◽  
Short Term Memory ◽  
Learning Model ◽  
Short Term ◽  
Long Short Term Memory ◽  
Deep Learning Model

Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task. For improving the security and economic performance of power grids, accurate short-term wind power forecasting is crucial. In this paper, a deep learning model (Long Short-term Memory (LSTM)) has been proposed for wind speed prediction. Knowing that wind speed time series is nonlinear stochastic, the mutual information (MI) approach was used to find the best subset from the data by maximizing the joint MI between subset and target output. To enhance the accuracy and reduce input characteristics and data uncertainties, rough set and interval type-2 fuzzy set theory are combined in the proposed deep learning model. Wind speed data from an international airport station in the southern coast of Iran Bandar-Abbas City was used as the original input dataset for the optimized deep learning model. Based on the statistical results, the rough set LSTM (RST-LSTM) model showed better prediction accuracy than fuzzy and original LSTM, as well as traditional neural networks, with the lowest error for training and testing datasets in different time horizons. The suggested model can support the optimization of the control approach and the smooth procedure of power system. The results confirm the superior capabilities of deep learning techniques for wind speed forecasting, which could also inspire new applications in meteorology assessment.

AOHDL: Archimedes Optimization Based Hybrid Deep Learning Model for Soybean Plant Disease Classification

2021 ◽  
Author(s):  
J. Annrose ◽  
N. Herald Anantha Rufus ◽  
C. R. Edwin Selva Rex ◽  
D. Godwin Immanuel
Keyword(s):  
Deep Learning ◽  
Optimization Algorithm ◽  
Short Term Memory ◽  
Learning Model ◽  
Disease Classification ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Soybean Plant ◽  
Lower Accuracy ◽  
Deep Learning Model

Abstract Bean which is botanically called Phaseolus vulgaris L belongs to the Fabaceae family.During bean disease identification, unnecessary economical losses occur due to the delay of the treatment period, incorrect treatment, and lack of knowledge. The existing deep learning and machine learning techniques met few issues such as high computational complexity, higher cost associated with the training data, more execution time, noise, feature dimensionality, lower accuracy, low speed, etc. To tackle these problems, we have proposed a hybrid deep learning model with an Archimedes optimization algorithm (HDL-AOA) for bean disease classification. In this work, there are five bean classes of which one is a healthy class whereas the remaining four classes indicate different diseases such as Bean halo blight, Pythium diseases, Rhizoctonia root rot, and Anthracnose abnormalities acquired from the Soybean (Large) Data Set.The hybrid deep learning technique is the combination of wavelet packet decomposition (WPD) and long short term memory (LSTM). Initially, the WPD decomposes the input images into four sub-series. For these sub-series, four LSTM networks were developed. During bean disease classification, an Archimedes optimization algorithm (AOA) enhances the classification accuracy for multiple single LSTM networks. MATLAB software implements the HDL-AOA model for bean disease classification. The proposed model accomplishes lower MAPE than other exiting methods. Finally, the proposed HDL-AOA model outperforms excellent classification results using different evaluation measures such as accuracy, specificity, sensitivity, precision, recall, and F-score.

Application of YOLO Deep Learning Model for Real Time Abandoned Baggage Detection

2018 ◽  
Author(s):  
Tossaporn Santad ◽  
Piyarat Silapasupphakornwong ◽  
Worawat Choensawat ◽  
Kingkarn Sookhanaphibarn
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Model ◽  
Deep Learning Model

scholarly journals Soybean Plant Disease Classification using Archimedes Optimization Algorithm based Hybrid Deep Learning Model

2021 ◽  
Author(s):  
J. Annrose ◽  
N. Herald Anantha Rufus ◽  
C. R. Edwin Selva Rex ◽  
D. Godwin Immanuel
Keyword(s):  
Deep Learning ◽  
Optimization Algorithm ◽  
Short Term Memory ◽  
Learning Model ◽  
Disease Classification ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Soybean Plant ◽  
Lower Accuracy ◽  
Deep Learning Model

Abstract Bean which is botanically called Phaseolus vulgaris L belongs to the Fabaceae family.During bean disease identification, unnecessary economical losses occur due to the delay of the treatment period, incorrect treatment, and lack of knowledge. The existing deep learning and machine learning techniques met few issues such as high computational complexity, higher cost associated with the training data, more execution time, noise, feature dimensionality, lower accuracy, low speed, etc. To tackle these problems, we have proposed a hybrid deep learning model with an Archimedes optimization algorithm (HDL-AOA) for bean disease classification. In this work, there are five bean classes of which one is a healthy class whereas the remaining four classes indicate different diseases such as Bean halo blight, Pythium diseases, Rhizoctonia root rot, and Anthracnose abnormalities acquired from the Soybean (Large) Data Set.The hybrid deep learning technique is the combination of wavelet packet decomposition (WPD) and long short term memory (LSTM). Initially, the WPD decomposes the input images into four sub-series. For these sub-series, four LSTM networks were developed. During bean disease classification, an Archimedes optimization algorithm (AOA) enhances the classification accuracy for multiple single LSTM networks. MATLAB software implements the HDL-AOA model for bean disease classification. The proposed model accomplishes lower MAPE than other exiting methods. Finally, the proposed HDL-AOA model outperforms excellent classification results using different evaluation measures such as accuracy, specificity, sensitivity, precision, recall, and F-score.

scholarly journals Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics

Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 12
Author(s):  
Jose M. Castillo T. ◽  
Muhammad Arif ◽  
Martijn P. A. Starmans ◽  
Wiro J. Niessen ◽  
Chris H. Bangma ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Characteristic Curve ◽  
Model Development ◽  
Learning Model ◽  
Multiparametric Mri ◽  
Data Sets ◽  
Data Set ◽  
Test Sets ◽  
Deep Learning Model

The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning- and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods, using various external data sets is crucial. While both deep-learning and radiomics approaches have been compared based on the same data set of one center, the comparison of the performances of both approaches on various data sets from different centers and different scanners is lacking. The goal of this study was to compare the performance of a deep-learning model with the performance of a radiomics model for the significant-PCa diagnosis of the cohorts of various patients. We included the data from two consecutive patient cohorts from our own center (n = 371 patients), and two external sets of which one was a publicly available patient cohort (n = 195 patients) and the other contained data from patients from two hospitals (n = 79 patients). Using multiparametric MRI (mpMRI), the radiologist tumor delineations and pathology reports were collected for all patients. During training, one of our patient cohorts (n = 271 patients) was used for both the deep-learning- and radiomics-model development, and the three remaining cohorts (n = 374 patients) were kept as unseen test sets. The performances of the models were assessed in terms of their area under the receiver-operating-characteristic curve (AUC). Whereas the internal cross-validation showed a higher AUC for the deep-learning approach, the radiomics model obtained AUCs of 0.88, 0.91 and 0.65 on the independent test sets compared to AUCs of 0.70, 0.73 and 0.44 for the deep-learning model. Our radiomics model that was based on delineated regions resulted in a more accurate tool for significant-PCa classification in the three unseen test sets when compared to a fully automated deep-learning model.

scholarly journals Spatio-temporal deep learning model for distortion classification in laparoscopic video

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1010
Author(s):  
Nouar AlDahoul ◽  
Hezerul Abdul Karim ◽  
Abdulaziz Saleh Ba Wazir ◽  
Myles Joshua Toledo Tan ◽  
Mohammad Faizal Ahmad Fauzi
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Spatial Information ◽  
Short Term Memory ◽  
Learning Model ◽  
Motion Blur ◽  
Video Frame ◽  
Video Enhancement ◽  
Deep Learning Model ◽  
Laparoscopic Videos

Background: Laparoscopy is a surgery performed in the abdomen without making large incisions in the skin and with the aid of a video camera, resulting in laparoscopic videos. The laparoscopic video is prone to various distortions such as noise, smoke, uneven illumination, defocus blur, and motion blur. One of the main components in the feedback loop of video enhancement systems is distortion identification, which automatically classifies the distortions affecting the videos and selects the video enhancement algorithm accordingly. This paper aims to address the laparoscopic video distortion identification problem by developing fast and accurate multi-label distortion classification using a deep learning model. Current deep learning solutions based on convolutional neural networks (CNNs) can address laparoscopic video distortion classification, but they learn only spatial information. Methods: In this paper, utilization of both spatial and temporal features in a CNN-long short-term memory (CNN-LSTM) model is proposed as a novel solution to enhance the classification. First, pre-trained ResNet50 CNN was used to extract spatial features from each video frame by transferring representation from large-scale natural images to laparoscopic images. Next, LSTM was utilized to consider the temporal relation between the features extracted from the laparoscopic video frames to produce multi-label categories. A novel laparoscopic video dataset proposed in the ICIP2020 challenge was used for training and evaluation of the proposed method. Results: The experiments conducted show that the proposed CNN-LSTM outperforms the existing solutions in terms of accuracy (85%), and F1-score (94.2%). Additionally, the proposed distortion identification model is able to run in real-time with low inference time (0.15 sec). Conclusions: The proposed CNN-LSTM model is a feasible solution to be utilized in laparoscopic videos for distortion identification.

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