A bidirectional LSTM deep learning approach for intrusion detection

Imrana, Yakubu; Xiang, Yang; Ali, Liaqat; Abdul-Rauf, Zaharawu

doi:10.1016/j.eswa.2021.115524

Cited by 231 publications

(102 citation statements)

References 20 publications

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“…Unfortunately, the collected dataset of CS has imbalance classes and small number of samples which can cause the problem of biasness in the results. It has been studied from the literature that if ML models are trained on imbalance data, then models show biased performance by favoring the majority class while ignoring the minority class [ 38 , 39 ]. This is due to the fact that minority class samples are trained infrequently during the training process; thus, prediction of the minority class is rare, ignored, and undiscovered [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

A Clinical Decision Support System (CDSS) for Unbiased Prediction of Caesarean Section Based on Features Extraction and Optimized Classification

Javeed

Ali

Seid

et al. 2022

Computational Intelligence and Neuroscience

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Nowadays, caesarean section (CS) is given preference over vaginal birth and this trend is rapidly rising around the globe, although CS has serious complications such as pregnancy scar, scar dehiscence, and morbidly adherent placenta. Thus, CS should only be performed when it is absolutely necessary for mother and fetus. To avoid unnecessary CS, researchers have developed different machine-learning- (ML-) based clinical decision support systems (CDSS) for CS prediction using electronic health record of the pregnant women. However, previously proposed methods suffer from the problems of poor accuracy and biasedness in ML. To overcome these problems, we have designed a novel CDSS where random oversampling example (ROSE) technique has been used to eliminate the problem of minority classes in the dataset. Furthermore, principal component analysis has been employed for feature extraction from the dataset while, for classification purpose, random forest (RF) model is deployed. We have fine-tuned the hyperparameter of RF using a grid search algorithm for optimal classification performance. Thus, the newly proposed system is named ROSE-PCA-RF and it is trained and tested using an online CS dataset available on the UCI repository. In the first experiment, conventional RF model is trained and tested on the dataset while in the second experiment, the proposed model is tested. The proposed ROSE-PCA-RF model improved the performance of traditional RF by 4.5% with reduced time complexity, while only using two extracted features through the PCA. Moreover, the proposed model has obtained 96.29% accuracy on training data while improving the accuracy of 97.12% on testing data.

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Section: Methodsmentioning

confidence: 99%

A Clinical Decision Support System (CDSS) for Unbiased Prediction of Caesarean Section Based on Features Extraction and Optimized Classification

Javeed

Ali

Seid

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

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“…Recently, large number of diagnostic systems have been developed for automated diagnosis of different diseases like Parkinson's disease [ 15 – 19 ], hepatitis [ 20 ], carcinoma [ 21 ], lung cancer [ 22 ], and mortality prediction systems [ 23 , 24 ] using machine learning, deep learning [ 25 ], data mining [ 26 ], and optimization methods [ 27 – 30 ]. Heart disease detection through machine learning is not an exception, and recently, numerous approaches have also been successfully implemented on various datasets for automated heart disease detection [ 31 – 37 ].…”

Section: Machine Learning For Heart Disease Predictionmentioning

confidence: 99%

Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions

Javeed

Khan

Ali³

et al. 2022

Computational and Mathematical Methods in Medicine

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One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.

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“…Using more features may lead to better results. In [25], the authors used conventional LSTM and BiDLSTM. BiDLSTM achieved better results.…”

Section: Comparative Analysis and Discussionmentioning

confidence: 99%

“…The system achieved 85.56% accuracy on the NSL-KDD dataset. Imrana et al [25] developed an IDS using a bidirectional Long-Short-Term-Memory (BiDLSTM). The efficiency of the BiDLSTM technique was validated on the NSL-KDD dataset and outperformed the standard LSTM.…”

Section: Gao Et Al [6] Presented a Principal Adaptive Component (A-pc...mentioning

confidence: 99%

Smart Attacks Learning Machine Advisor System for Protecting Smart Cities from Smart Threats

2022

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The extensive use of Internet of Things (IoT) technology has recently enabled the development of smart cities. Smart cities operate in real-time to improve metropolitan areas’ comfort and efficiency. Sensors in these IoT devices are immediately linked to enormous servers, creating smart city traffic flow. This flow is rapidly increasing and is creating new cybersecurity concerns. Malicious attackers increasingly target essential infrastructure such as electricity transmission and other vital infrastructures. Software-Defined Networking (SDN) is a resilient connectivity technology utilized to address security concerns more efficiently. The controller, which oversees the flows of each appropriate forwarding unit in the SDN architecture, is the most critical component. The controller’s flow statistics are thought to provide relevant information for building an Intrusion Detection System (IDS). As a result, we propose a five-level classification approach based on SDN’s flow statistics to develop a Smart Attacks Learning Machine Advisor (SALMA) system for detecting intrusions and for protecting smart cities from smart threats. We use the Extreme Learning Machine (ELM) technique at all levels. The proposed system was implemented on the NSL-KDD and KDDCUP99 benchmark datasets, and achieved 95% and 99.2%, respectively. As a result, our approach provides an effective method for detecting intrusions in SDNs.

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A bidirectional LSTM deep learning approach for intrusion detection

Cited by 231 publications

References 20 publications

A Clinical Decision Support System (CDSS) for Unbiased Prediction of Caesarean Section Based on Features Extraction and Optimized Classification

A Clinical Decision Support System (CDSS) for Unbiased Prediction of Caesarean Section Based on Features Extraction and Optimized Classification

Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions

Smart Attacks Learning Machine Advisor System for Protecting Smart Cities from Smart Threats

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