Cyber-Physical System Security Based on Human Activity Recognition through IoT Cloud Computing

Achar, Sandesh; Faruqui, Nuruzzaman; Whaiduzzaman, Md; Awajan, Albara; Alazab, Moutaz

doi:10.3390/electronics12081892

Cited by 20 publications

(5 citation statements)

References 45 publications

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“…The model exhibits an excellent ability to generalize to new instances [49], as shown by the consistently high validation accuracy. The validation accuracy [50] slightly decreases compared to the corresponding training accuracy values, which is typical of a well-generalized model.…”

Section: Results Analysismentioning

confidence: 85%

Unmasking Banking Fraud: Unleashing the Power of Machine Learning and Explainable AI (XAI) on Imbalanced Data

Nobel,

Sultana,

Singha

et al. 2024

Information

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Recognizing fraudulent activity in the banking system is essential due to the significant risks involved. When fraudulent transactions are vastly outnumbered by non-fraudulent ones, dealing with imbalanced datasets can be difficult. This study aims to determine the best model for detecting fraud by comparing four commonly used machine learning algorithms: Support Vector Machine (SVM), XGBoost, Decision Tree, and Logistic Regression. Additionally, we utilized the Synthetic Minority Over-sampling Technique (SMOTE) to address the issue of class imbalance. The XGBoost Classifier proved to be the most successful model for fraud detection, with an accuracy of 99.88%. We utilized SHAP and LIME analyses to provide greater clarity into the decision-making process of the XGBoost model and improve overall comprehension. This research shows that the XGBoost Classifier is highly effective in detecting banking fraud on imbalanced datasets, with an impressive accuracy score. The interpretability of the XGBoost Classifier model was further enhanced by applying SHAP and LIME analysis, which shed light on the significant features that contribute to fraud detection. The insights and findings presented here are valuable contributions to the ongoing efforts aimed at developing effective fraud detection systems for the banking industry.

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Section: Results Analysismentioning

confidence: 85%

Unmasking Banking Fraud: Unleashing the Power of Machine Learning and Explainable AI (XAI) on Imbalanced Data

Nobel,

Sultana,

Singha

et al. 2024

Information

Self Cite

View full text Add to dashboard Cite

show abstract

“…Weight initialization impacts CNN training and model convergence [53] . Poor weight initialization can cause vanishing gradients, which make learning difficult, stall convergence, and trap the model in a suboptimal local minimum [54] .…”

Section: Proposed Methodologymentioning

confidence: 99%

Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

Faruqui,

Yousuf,

Kateb

et al. 2023

Heliyon

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“…It combines a memory cell to prevent vanishing gradient problems with a tri-gating mechanism to handle long sequences. These gates are defined by Equations ( 3)-( 5), which are input, forget, and output gates, respectively [28].…”

Section: Long Short-term Memory (Lstm) Networkmentioning

confidence: 99%

A Novel IDS with a Dynamic Access Control Algorithm to Detect and Defend Intrusion at IoT Nodes

Alazab,

Awajan,

Alazzam

et al. 2024

Sensors

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The Internet of Things (IoT) is the underlying technology that has enabled connecting daily apparatus to the Internet and enjoying the facilities of smart services. IoT marketing is experiencing an impressive 16.7% growth rate and is a nearly USD 300.3 billion market. These eye-catching figures have made it an attractive playground for cybercriminals. IoT devices are built using resource-constrained architecture to offer compact sizes and competitive prices. As a result, integrating sophisticated cybersecurity features is beyond the scope of the computational capabilities of IoT. All of these have contributed to a surge in IoT intrusion. This paper presents an LSTM-based Intrusion Detection System (IDS) with a Dynamic Access Control (DAC) algorithm that not only detects but also defends against intrusion. This novel approach has achieved an impressive 97.16% validation accuracy. Unlike most of the IDSs, the model of the proposed IDS has been selected and optimized through mathematical analysis. Additionally, it boasts the ability to identify a wider range of threats (14 to be exact) compared to other IDS solutions, translating to enhanced security. Furthermore, it has been fine-tuned to strike a balance between accurately flagging threats and minimizing false alarms. Its impressive performance metrics (precision, recall, and F1 score all hovering around 97%) showcase the potential of this innovative IDS to elevate IoT security. The proposed IDS boasts an impressive detection rate, exceeding 98%. This high accuracy instills confidence in its reliability. Furthermore, its lightning-fast response time, averaging under 1.2 s, positions it among the fastest intrusion detection systems available.

show abstract

Cyber-Physical System Security Based on Human Activity Recognition through IoT Cloud Computing

Cited by 20 publications

References 45 publications

Unmasking Banking Fraud: Unleashing the Power of Machine Learning and Explainable AI (XAI) on Imbalanced Data

Unmasking Banking Fraud: Unleashing the Power of Machine Learning and Explainable AI (XAI) on Imbalanced Data

Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

A Novel IDS with a Dynamic Access Control Algorithm to Detect and Defend Intrusion at IoT Nodes

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