SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization

Electronics, Год журнала: 2023, Номер 12(17), С. 3541 - 3541

Опубликована: Авг. 22, 2023

The Internet of Medical Things (IoMT) has become an attractive playground to cybercriminals because its market worth and rapid growth. These devices have limited computational capabilities, which ensure minimum power absorption. Moreover, the manufacturers use simplified architecture offer a competitive price in market. As result, IoMTs cannot employ advanced security algorithms defend against cyber-attacks. IoMT easy prey for due access valuable data rapidly expanding market, as well being comparatively easier exploit.As intrusion rate is experiencing surge. This paper proposes novel Intrusion Detection System (IDS), namely SafetyMed, combining Convolutional Neural Networks (CNN) Long Short-Term Memory (LSTM) networks from sequential grid data. SafetyMed first IDS that protects malicious image network traffic. innovative ensures optimized detection by trade-off between False Positive Rate (FPR) (DR). It detects intrusions with average accuracy 97.63% precision recall, F1-score 98.47%, 97%, 97.73%, respectively. In summary, potential revolutionize many vulnerable sectors (e.g., medical) ensuring maximum protection intrusion.

Язык: Английский

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

Heliyon, Год журнала: 2023, Номер 9(11), С. e21520 - e21520

Опубликована: Окт. 27, 2023

The field of automated lung cancer diagnosis using Computed Tomography (CT) scans has been significantly advanced by the precise predictions offered Convolutional Neural Network (CNN)-based classifiers. Critical areas study include improving image quality, optimizing learning algorithms, and enhancing diagnostic accuracy. To facilitate a seamless transition from research laboratories to real-world applications, it is crucial improve technology's usability-a factor often neglected in current state-of-the-art research. Yet, this frequently overlooks need for expediting process. This paper introduces Healthcare-As-A-Service (HAAS), an innovative concept inspired Software-As-A-Service (SAAS) within cloud computing paradigm. As comprehensive service system, HAAS potential reduce mortality rates providing early opportunities everyone. We present HAASNet, cloud-compatible CNN that boasts accuracy rate 96.07%. By integrating HAASNet with physio-symptomatic data Internet Medical Things (IoMT), proposed model generates accurate reliable reports. Leveraging IoMT technology, globally accessible via Internet, transcending geographic boundaries. groundbreaking achieves average precision, recall, F1-scores 96.47%, 95.39%, 94.81%, respectively.

Язык: Английский

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Machine learning and deep learning for user authentication and authorization in cybersecurity: A state-of-the-art review

Computers & Security, Год журнала: 2024, Номер 140, С. 103747 - 103747

Опубликована: Фев. 12, 2024

Язык: Английский

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Enhancing healthcare in the digital era: A secure e-health system for heart disease prediction and cloud security

Expert Systems with Applications, Год журнала: 2024, Номер 255, С. 124479 - 124479

Опубликована: Июнь 26, 2024

Язык: Английский

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

Sensors, Год журнала: 2024, Номер 24(7), С. 2188 - 2188

Опубликована: Март 29, 2024

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

Язык: Английский

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

IEEE Access, Год журнала: 2024, Номер 12, С. 63584 - 63597

Опубликована: Янв. 1, 2024

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

Язык: Английский

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AI-assisted ultrasonic wave analysis for automated classification of steel corrosion-induced concrete damage

Automation in Construction, Год журнала: 2024, Номер 167, С. 105704 - 105704

Опубликована: Авг. 25, 2024

Early detection of cracks in reinforced concrete caused by chloride intrusion is crucial for effective maintenance. This paper develops and compares AI-assisted models that use ultrasonic pulse waves to automatically assess early-stage damage, particularly steel corrosion. Data were collected from 108 cubes with various mixture designs, cover depths, corrosion levels induced the impressed current technique. The AI models, convolutional neural networks (CNNs), outperformed traditional regression achieving up 84% classification accuracy. systematic automated features algorithm enable reliable, consistent rapid wave data analysis, which especially beneficial condition assessment large infrastructure systems. advancement inspires further research into integrating IoT robotics-assisted systems comprehensive monitoring.

Язык: Английский

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

Information, Год журнала: 2024, Номер 15(6), С. 298 - 298

Опубликована: Май 23, 2024

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

Язык: Английский

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Adaptive protocols for hypervisor security in cloud infrastructure using federated learning-based anomaly detection

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 152, С. 110750 - 110750

Опубликована: Апрель 15, 2025

Язык: Английский

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Deep-Hill: An Innovative Cloud Resource Optimization Algorithm by Predicting SaaS Instance Configuration Using Deep Learning

IEEE Access, Год журнала: 2024, Номер 12, С. 92573 - 92584

Опубликована: Янв. 1, 2024

The integration of Artificial Intelligence (AI) services within the framework Software-as-a-Service (SaaS) cloud architecture has significantly permeated our everyday routines. These AI diverge from traditional applications by offering a more personalized user experience. That is why predefined instance configuration not an optimal approach for these applications. challenge further compounded unpredictable nature demand, making resource allocation to instances complex task. This paper introduces innovative algorithm, termed Deep-Hill, designed enhance through precise prediction SaaS configurations. It combination 5-layer Deep Neural Network (DNN) and Hill-Climbing algorithm. unique classifies in one five classes with 96.33% accuracy, 90.83% precision, 90.96% recall, 90.86% F1-score. On average, it reduces number active hosts four, contributing 13.33% less power consumption. remarkable performance Deep-Hill algorithm underscores its potential set new benchmark optimization resources. paves way cost-effective applications, marking significant step forward evolution computing.

Язык: Английский

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