A Deep Bidirectional LSTM-GRU Network Model for Automated Ciphertext Classification

Ahmadzadeh, Ezat; Kim, Hyunil; Jeong, Ongee; Kim, Namki; Moon, Inkyu

doi:10.1109/access.2022.3140342

Cited by 33 publications

(12 citation statements)

References 35 publications

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“…A BiLSTM [ 42 ] model contains two LSTM models where one LSTM takes the input information sequence in the forward direction (past to future) and the other LSTM in the backward direction (future to past). The BiLSTM input flow in both directions is used to preserve the future and past sequence information.…”

Section: Methodsmentioning

confidence: 99%

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Raza

Siddiqui²,

Munir³

et al. 2022

PLoS ONE

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Maternal health is an important aspect of women’s health during pregnancy, childbirth, and the postpartum period. Specifically, during pregnancy, different health factors like age, blood disorders, heart rate, etc. can lead to pregnancy complications. Detecting such health factors can alleviate the risk of pregnancy-related complications. This study aims to develop an artificial neural network-based system for predicting maternal health risks using health data records. A novel deep neural network architecture, DT-BiLTCN is proposed that uses decision trees, a bidirectional long short-term memory network, and a temporal convolutional network. Experiments involve using a dataset of 1218 samples collected from maternal health care, hospitals, and community clinics using the IoT-based risk monitoring system. Class imbalance is resolved using the synthetic minority oversampling technique. DT-BiLTCN provides a feature set to obtain high accuracy results which in this case are provided by the support vector machine with a 98% accuracy. Maternal health exploratory data analysis reveals that the health conditions which are the strongest indications of health risk during pregnancy are diastolic and systolic blood pressure, heart rate, and age of pregnant women. Using the proposed model, timely prediction of health risks associated with pregnant women can be made thus mitigating the risk of health complications which helps to save lives.

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Section: Methodsmentioning

confidence: 99%

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Raza

Siddiqui²,

Munir³

et al. 2022

PLoS ONE

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“…In this study, two different deep learning models based on GRU and LSTM, which make productivity estimation on wheat data of Konya province, are proposed to meet the stated requirement. Since the two proposed deep learning models are based on RNN [41][42][43][44], both the performance and training times of the models were compared. As a result of the comparison processes, it is seen that the results of the LSTM model are slightly better than the GRU model.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Buğday Veri̇m Tahmi̇ni̇ İçi̇n Yeni̇lemeli̇ Si̇ni̇r Aği Tabanli Model Geli̇şti̇rme

ÇETİNER

Kara

2022

Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi

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In the study carried out in line with the stated purposes, monthly rain, humidity and temperature data, wheat production amount, and wheat productivity data of Konya province between 1980-2020 were used. Using these data, wheat productivity estimation was performed with (Gated Recurrent Units) GRU and Long Short Term Memory (LSTM) methods, which are Recurrent Neural Network (RNN) based algorithms. When wheat productivity estimation performance was examined with the implemented GRU-based model, 0.9550, 0.0059, 0.0280, 0.0623, 7.45 values were obtained for the R 2 score, MSE, RMSE, MAE and MAPE values, respectively. In the performance results obtained with the LSTM method, which is another RNN-based method, 0.9667, 0.0054, 0.0280, 0.0614, 7.33 values were obtained for the R 2 score, MSE, RMSE, MAE and MAPE values, respectively. Although the LSTM method gave better results than the GRU method, the training modelling time of the LSTM method took longer than that of the GRU method.

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“…Ensuring the dataset is preprocessed correctly can lead to more accurate and efficient training, ultimately resulting in a more robust and reliable model [48]. By normalizing the features, handling categorical variables, selecting relevant features, and generating input sequences, we ensure that the LSTM model can effectively capture the underlying patterns and relationships in the data [49]. This, in turn, allows the model to generalize well to unseen data, providing a high intrusion detection accuracy and minimizing false alarms.…”

Section: Feature Extraction and Preprocessingmentioning

confidence: 99%

Deep-IDS: A Real-Time Intrusion Detector for IoT Nodes Using Deep Learning

Racherla,

Sripathi,

Faruqui

et al. 2024

IEEE Access

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The Internet of Things (IoT) represents a swiftly expanding sector that is pivotal in driving the innovation of today's smart services. However, the inherent resource-constrained nature of IoT nodes poses significant challenges in embedding advanced algorithms for cybersecurity, leading to an escalation in cyberattacks against these nodes. Contemporary research in Intrusion Detection Systems (IDS) predominantly focuses on enhancing IDS performance through sophisticated algorithms, often overlooking their practical applicability. This paper introduces Deep-IDS, an innovative and practically deployable Deep Learning (DL)-based IDS. It employs a Long-Short-Term-Memory (LSTM) network comprising 64 LSTM units and is trained on the CIC-IDS2017 dataset. Its streamlined architecture renders Deep-IDS an ideal candidate for edge-server deployment, acting as a guardian between IoT nodes and the Internet against Denial of Service (DoS), Distributed Denial of Service (DDoS), Brute Force (BRF), Man-in-the-Middle (MITM), and Replay (RP) Attacks. A distinctive aspect of this research is the trade-off analysis between the intrusion detection rate and the false alarm rate, facilitating the real-time performance of the Deep-IDS. The system demonstrates an exemplary detection rate of 96.8% and an overall classification accuracy of 97.67%. Furthermore, Deep-IDS achieves precision, recall, and F1-scores of 97.67%, 98.17%, and 97.91%, respectively. On average, Deep-IDS requires 1.49 seconds to identify and mitigate intrusion attempts, effectively blocking malicious traffic sources. The remarkable efficacy, swift response time, innovative design, and novel defense strategy of Deep-IDS not only secure IoT nodes but also their interconnected subnetworks, thereby positioning Deep-IDS as a leading intrusion detection system for IoT-enhanced computer networks.

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A Deep Bidirectional LSTM-GRU Network Model for Automated Ciphertext Classification

Cited by 33 publications

References 35 publications

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Buğday Veri̇m Tahmi̇ni̇ İçi̇n Yeni̇lemeli̇ Si̇ni̇r Aği Tabanli Model Geli̇şti̇rme

Deep-IDS: A Real-Time Intrusion Detector for IoT Nodes Using Deep Learning

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