Machine learning-based intrusion detection for SCADA systems in healthcare

Öztürk, Tolgahan; Turgut, Zeynep; Akgün, Gökçe; Köse, Cemal

doi:10.1007/s13721-022-00390-2

Cited by 10 publications

(10 citation statements)

References 15 publications

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“…With a sub-eld called Deep Learning, deep structured networks can produce outstanding results by performing feature extraction on large and complex datasets. Used in many elds such as natural language processing, and game strategies, ANNs play an important role in processing large amounts of data and understanding complex relationships [40].…”

Section: Arti Cial Neural Networkmentioning

confidence: 99%

RETRACTED: A novel robust LSTM model on Churn and comparative analysis versus deep learning, machine learning and ensemble learning: Telecommunications Case

Başarslan,

ÜNAL,

KAYAALP

2023

Preprint

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Customer churn is an important issue in increasing both the long and short-term revenues of a company. If companies identify customers’ churn behavior, they can prevent customer loss, ensure customer loyalty, and in turn gain better financial returns. The telecommunication sector is a customer-oriented sector that requires customer retention to survive in the market and, in this sector customer loss is observed at a high level. In recent years, artificial intelligence-based customer churn analysis has been widely used to predict customers’ churn behavior. In this study, a customer churn analysis was conducted with publicly shared Telco telecom data. Predictive models were built with machine learning (LR, KNN, SVM, DT, RF, ANN), ensemble learning (XGBoost, Voting), and deep learning (LSTM) methods. Besides, a 3-layered LSTM model was proposed. Accuracy, F1, precision, and recall rates were used to evaluate the models. As a result, the novel 3-layered LSTM model achieved 91% accuracy, 87% precision, 84% recall, and 89% F1 values. The proposed model is competitive with the literature.

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Section: Arti Cial Neural Networkmentioning

confidence: 99%

RETRACTED: A novel robust LSTM model on Churn and comparative analysis versus deep learning, machine learning and ensemble learning: Telecommunications Case

Başarslan,

ÜNAL,

KAYAALP

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition to the selection of multiple sub-models, STCK allows you to specify an additional model to learn how to best combine the predictions of the sub-models. Because a metamodel is used to best combine the predictions of the sub-models, this method is sometimes referred to as mixture, as is mixture of predictions [32]. The STCK process is illustrated in Figure 10.…”

Section: Stackingmentioning

confidence: 99%

Sentiment analysis of coronavirus data with ensemble and machine learning methods

Başarslan,

Kayaalp

2024

Turkish Journal of Engineering

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The coronavirus pandemic has distanced people from social life and increased the use of social media. People's emotions can be determined with text data collected from social media applications. This is used in many fields, especially in commerce. This study aims to predict people's sentiments about the pandemic by applying sentiment analysis to Twitter tweets about the pandemic using single machine learning classifiers (Decision Tree-DT, K-Nearest Neighbor-KNN, Logistic Regression-LR, Naïve Bayes-NB, Random Forest-RF) and ensemble learning methods (Majority Voting (MV), Probabilistic Voting (PV), and Stacking (STCK)). After vectorizing the tweets using two predictive methods, Word2Vec (W2V) and Doc2Vec, and two traditional word representation methods, Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BOW), classification models built using single machine learning classifiers were compared to models built using ensemble learning methods (MV, PV and STCK) by heterogeneously combining single machine classifier algorithms. Accuracy (ACC), F-measure (F), precision (P), and recall (R) were used as performance measures, with training/test separation rates of 70%-30% and 80%-20%, respectively. Among these models, the ACC of ensemble learning models ranged from 89% to 73%, while the ACC of single classifier models ranged from 60% to 80%. Among the ensemble learning methods, STCK with Doc2Vec text representation/embedding method gave the best ACC result of 89%. According to the experimental results, ensemble models built with heterogeneous machine learning classifier algorithms gave better results than single machine learning classifier algorithms.

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“…There are 41 characteristics in total, 38 of which are numeric, and 3 of which are not (protocol type, service type, and fag). There are also fundamental traffic features (23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41), content features (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22), features (1-10), and a class label for each item.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…Several researchers have suggested using AI in the smart healthcare system as a reliable and practical security solution [14]. Many of the relevant studies in the literature have developed computerized simulations for network intrusion recognition using machine learning techniques, such as K-nearest neighbor (KNN) [15], Naïve Bayes [16], Support Vector Machine (SVM) [17], Random Forest (RF) [18], etc.…”

Section: Introductionmentioning

confidence: 99%

A Double-Layer Indemnity Enhancement Using LSTM and HASH Function Technique for Intrusion Detection System

Ali,

Alqurashi,

Alsolami

et al. 2023

Mathematics

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The Intrusion Detection System (IDS) is the most widely used network security mechanism for distinguishing between normal and malicious traffic network activities. It aids network security in that it may identify unforeseen hazards in network traffic. Several techniques have been put forth by different researchers for network intrusion detection. However, because network attacks have increased dramatically, making it difficult to execute precise detection rates quickly, the demand for effectively recognizing network incursion is growing. This research proposed an improved solution that uses Long Short-Term Memory (LSTM) and hash functions to construct a revolutionary double-layer security solution for IoT Network Intrusion Detection. The presented framework utilizes standard and well-known real-time IDS datasets such as KDDCUP99 and UNSWNB-15. In the presented framework, the dataset was pre-processed, and it employed the Shuffle Shepherd Optimization (SSO) algorithm for tracking the most informative attributes from the filtered database. Further, the designed model used the LSTM algorithm for classifying the normal and malicious network traffic precisely. Finally, a secure hash function SHA3-256 was utilized for countering the attacks. The intensive experimental assessment of the presented approach with the conventional algorithms emphasized the efficiency of the proposed framework in terms of accuracy, precision, recall, etc. The analysis showed that the presented model attained attack prediction accuracy of 99.92% and 99.91% for KDDCUP99 and UNSWNB-15, respectively.

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Machine learning-based intrusion detection for SCADA systems in healthcare

Cited by 10 publications

References 15 publications

RETRACTED: A novel robust LSTM model on Churn and comparative analysis versus deep learning, machine learning and ensemble learning: Telecommunications Case

RETRACTED: A novel robust LSTM model on Churn and comparative analysis versus deep learning, machine learning and ensemble learning: Telecommunications Case

Sentiment analysis of coronavirus data with ensemble and machine learning methods

A Double-Layer Indemnity Enhancement Using LSTM and HASH Function Technique for Intrusion Detection System

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