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Continuous Mobile User Authentication Using a Hybrid CNN-Bi-LSTM Approach

Computers, Materials & Continua 2023年第4期75卷 651-667页

作者： Sarah Alzahrani Joud Alderaan Dalya Alatawi bandar alotaibi Department of Information Technology University of TabukTabuk71491Saudi Arabia Sensor Networks and Cellular Systems Research Center University of TabukTabuk71491Saudi Arabia

Internet of Things (IoT) devices incorporate a large amount ofdata in several fields, including those of medicine, business, and *** authentication is paramount in the IoT era to assure connecteddevices’ security. However, traditional authentication methods and conventionalbiometrics-based authentication approaches such as face recognition,fingerprints, and password are vulnerable to various attacks, including smudgeattacks, heat attacks, and shoulder surfing attacks. Behavioral biometrics isintroduced by the powerful sensing capabilities of IoT devices such as smartwearables and smartphones, enabling continuous authentication. ArtificialIntelligence (AI)-based approaches introduce a bright future in refining largeamounts of homogeneous biometric data to provide innovative user authenticationsolutions. This paper presents a new continuous passive authenticationapproach capable of learning the signatures of IoT users utilizing smartphonesensors such as a gyroscope, magnetometer, and accelerometer to recognizeusers by their physical activities. This approach integrates the convolutionalneural network (CNN) and recurrent neural network (RNN) models to learnsignatures of human activities from different users. A series of experiments areconducted using the MotionSense dataset to validate the effectiveness of theproposed method. Our technique offers a competitive verification accuracyequal to 98.4%.We compared the proposed method with several conventionalmachine learning and CNN models and found that our proposed modelachieves higher identification accuracy than the recently developed verificationsystems. The high accuracy achieved by the proposed method proves itseffectiveness in recognizing IoT users passively through their physical activitypatterns.

关键词： Human activity recognition recurrent neural network(RNN) internet of things(IoT) machine learning(ML)

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Self-Care Management Practices of Diabetic Patients Type 2 in Saudi Arabia

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Open Journal of Nursing 2020年第11期10卷 1013-1025页

作者： Budour bandar alotaibi Prince Sultan Military Medical City Riyadh KSA

Background & Aim: Patients’ self-care management practice in diabetes is considered a critical element to keep diabetes under control and the disease management is usually conducted by the patients themselves and/or their families. The main aim of this study is to assess the level of self-care management practices of diabetic patients type 2 in Saudi Arabia. Materials & Methods: A cross-sectional study design was utilized in the Prince Sultan Military Medical City in Riyadh on a convenience sampling method consisted of 377 diabetic patients type 2. The researcher used a self-administered questionnaire (Summery of Diabetes Self-care Activities) which was adopted from Toobert et al. (2000). Results: The study results revealed that the level of self-care management practices among patients with type 2 DM is not satisfactory, except in the medication domain. Conclusion & Recommendations: Policymakers should revise the education strategy for diabetic patients and develop health education campaigns that focus on the patient’s family as well as the individual with type-2 DM.

关键词： Diabetes Self-Care Management Practices

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Saudi Nurses Perception regarding Patient Safety in a Major Tertiary Hospital

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Open Journal of Nursing 2020年第7期10卷 657-664页

作者： Budour bandar alotaibi Alanoud Essam Almadani Olfat Salem Prince Sultan Military Medical City Riyadh KSA Al-Iman General Hospital Riyadh KSA King Saud University Riyadh KSA

Background: The absence of patient safety culture among nurses has been linked to increased errors and higher level of mortality and morbidity among patients. Higher culture of patient safety has been shown to be associated with better patient outcomes, and the implementation of improvements in organizational and safety culture enhances quality in healthcare. Aim: The main aim of this study is to assess the perception of nurses regarding patient safety among nurses at prince sultan military medical city in Saudi Arabia. Materials and Methods: Cross-sectional design was applied in this study among nurses who are working at prince sultan military medical city in Riyadh, Saudi Arabia. Sample was 215 nurses, selected by convenience sampling method. The Hospital Survey on Patient Safety Culture (HSOPS) survey was used in the current study to collect data. Data have been collected within seven weeks. Statistical package for social sciences has been used to analyze data using descriptive statistics and inferential statistics such as Independent sample t test and One-Way ANOVA. Results and Conclusion: The results can only be useful if data analysis is described. The highest domain of their perception was illustrated in “Hospital management support for patient safety” with a mean of 3.68 out of 5.0, while, the lowest mean for their perception was in the domain of “Teamwork across hospital units” with a mean 2.62. The total mean of nurses’ perception regarding patient safety was 3.19 out of 5.0. Also, there was a significant difference in the mean level of nurses’ perception of patient safety with regard to nurses’ gender. Encouraging patients’ safety culture among nurses is strongly recommended through effective communication and providing feedback.

关键词： Perception Patient Safety Hospital

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Deep Learning and Entity Embedding-Based Intrusion Detection Model for Wireless Sensor Networks

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Computers, Materials & Continua 2021年第10期69卷 1343-1360页

作者： bandar Almaslukh Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Wireless sensor networks(WSNs)are considered promising for applications such as military surveillance and *** security of these networks must be ensured in order to have reliable *** such networks requires more attention,as they typically implement no dedicated security *** addition,the sensors have limited computing resources and power and storage,which makes WSNs vulnerable to various attacks,especially denial of service(DoS).The main types of DoS attacks against WSNs are blackhole,grayhole,flooding,and *** are two primary techniques to build an intrusion detection system(IDS):signature-based and *** study uses the data-driven approach since the signature-based method fails to detect a zero-day *** publications have proposed data-driven approaches to protect WSNs against such *** approaches are based on either the traditional machine learning(ML)method or a deep learning *** fundamental limitations of these methods include the use of raw features to build an intrusion detection model,which can result in low detection *** study implements entity embedding to transform the raw features to a more robust representation that can enable more precise detection and demonstrates how the proposed method can outperform state-of-the-art solutions in terms of recognition accuracy.

关键词： Wireless sensor networks intrusion detection deep learning entity embedding artificial neural networks

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A New Database Intrusion Detection Approach Based on Hybrid Meta-Heuristics

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Computers, Materials & Continua 2021年第2期66卷 1879-1895页

作者： Youseef alotaibi Department of Computer Science College of Computer and Information SystemsUmm Al Qura UniversityMakkah21421Saudi Arabia

A new secured database management system architecture using intrusion detection systems(IDS)is proposed in this paper for organizations with no previous role mapping for users.A simple representation of Structured Query Language queries is proposed to easily permit the use of the worked clustering algorithm.A new clustering algorithm that uses a tube search with adaptive memory is applied to database log files to create users’***,queries issued for each user are checked against the related user profile using a classifier to determine whether or not each query is *** IDS will stop query execution or report the threat to the responsible person if the query is malicious.A simple classifier based on the Euclidean distance is used and the issued query is transformed to the proposed simple representation using a classifier,where the Euclidean distance between the centers and the profile’s issued query is calculated.A synthetic data set is used for our experimental *** user access behavior in relation to the database is modelled using the data *** false negative(FN)and false positive(FP)rates are used to compare our proposed algorithm with other *** experimental results indicate that our proposed method results in very small FN and FP rates.

关键词： Adaptive search memory clustering database management system(DBMS) intrusion detection system(IDS) quiplets structured query language(SQL) tube search

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An Efficient Cyber Security and Intrusion Detection System Using CRSR with PXORP-ECC and LTH-CNN

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Computers, Materials & Continua 2023年第8期76卷 2061-2078页

作者： Nouf Saeed alotaibi Department of Computer Science College of Science and Humanities Al DawadmiShaqra UniversityShaqra11961Saudi Arabia

Intrusion Detection System(IDS)is a network security mechanism that analyses all users’and applications’traffic and detectsmalicious activities in *** existing IDSmethods suffer fromlower accuracy and lack the required level of security to prevent sophisticated *** problem can result in the system being vulnerable to attacks,which can lead to the loss of sensitive data and potential system ***,this paper proposes an Intrusion Detection System using Logistic Tanh-based Convolutional Neural Network Classification(LTH-CNN).Here,the Correlation Coefficient based Mayfly Optimization(CC-MA)algorithm is used to extract the input characteristics for the IDS from the input ***,the optimized features are utilized by the LTH-CNN,which returns the attacked and non-attacked *** that,the attacked data is stored in the log file and non-attacked data is mapped to the cyber security and data security *** prevent the system from cyber-attack,the Source and Destination IP address is converted into a complex binary format named 1’s Complement Reverse Shift Right(CRSR),where,in the data security phase the sensed data is converted into an encrypted format using Senders Public key Exclusive OR Receivers Public Key-Elliptic Curve Cryptography(PXORP-ECC)Algorithm to improve the data *** Security Laboratory-Knowledge Discovery inDatabases(NSLKDD)dataset and real-time sensor are used to train and evaluate the proposed *** suggested model is evaluated based on accuracy,sensitivity,and specificity,which outperformed the existing IDS methods,according to the results of the experiments.

关键词： Intrusion detection system logistic tanh-based convolutional neural network classification(LTH-CNN) correlation coefficient based mayfly optimization(CC-MA) cyber security

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Network Intrusion Detection Model Using Fused Machine Learning Technique

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Computers, Materials & Continua 2023年第5期75卷 2479-2490页

作者： Fahad Mazaed alotaibi Faculty of Computing and Information Technology in Rabigh(FCITR) King Abdulaziz UniversityJeddahSaudi Arabia

With the progress of advanced technology in the industrial revolution encompassing the Internet of Things(IoT)and cloud computing,cyberattacks have been increasing rapidly on a large *** rapid expansion of IoT and networks in many forms generates massive volumes of data,which are vulnerable to security *** a result,cyberattacks have become a prevalent and danger to society,including its infrastructures,economy,and citizens’privacy,and pose a national security risk ***,cyber security has become an increasingly important issue across all levels and *** progress is being made in developing more sophisticated and efficient intrusion detection and defensive *** the scale of complexity of the cyber-universe is increasing,advanced machine learning methods are the most appropriate solutions for predicting cyber *** this study,a fused machine learning-based intelligent model is proposed to detect intrusion in the early stage and thus secure networks from harmful *** results confirm the effectiveness of the proposed intrusion detection model,with 0.909 accuracy and a miss rate of 0.091.

关键词： Cyberattack machine learning prediction solution intrusion detection

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A Coprocessor Architecture for 80/112-bit Security Related Applications

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Computers, Materials & Continua 2023年第3期74卷 6849-6865页

作者： Muhammad Rashid Majid alotaibi Department of Computer Engineering Umm Al-Qura UniversityMakkah21955Saudi Arabia

We have proposed a flexible coprocessor key-authentication architecture for 80/112-bit security-related applications over GF(2m)field by employing Elliptic-curve Diffie Hellman(ECDH)*** flexibility,a serial input/output interface is used to load/produce secret,public,and shared keys ***,to reduce the hardware resources and to achieve a reasonable time for cryptographic computations,we have proposed a finite field digit-serial multiplier architecture using combined shift and accumulate ***,two finite-statemachine controllers are used to perform efficient control *** proposed coprocessor architecture over GF(2^(163))and GF(2^(233))is programmed using Verilog and then implemented on Xilinx Virtex-7 FPGA(field-programmable-gate-array)*** GF(2^(163))and GF(2^(233)),the proposed flexible coprocessor use 1351 and 1789 slices,the achieved clock frequency is 250 and 235MHz,time for one public key computation is 40.50 and 79.20μs and time for one shared key generation is 81.00 and 158.40μ***,the consumed power over GF(2^(163))and GF(2^(233))is 0.91 and 1.37mW,*** proposed coprocessor architecture outperforms state-of-the-art ECDH designs in terms of hardware resources.

关键词： Coprocessor design key-authentication wireless sensor nodes RFID ECDH FPGA

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Variable viscosity effects on the flow of MHD hybrid nanofluid containing dust particles over a needle with Hall current——a Xue model exploration

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Communications in Theoretical Physics 2022年第5期74卷 121-131页

作者： Muhammad Ramzan Hammad alotaibi Department of Computer Science Bahria UniversityIslamabad44000Pakistan Department of Mathematics College of ScienceTaif UniversityPOBox 11099Taif 21944Saudi Arabia

This study scrutinizes the flow of engine oil-based suspended carbon nanotubes magnetohydrodynamics(MHD)hybrid nanofluid with dust particles over a thin moving needle following the Xue *** analysis also incorporates the effects of variable viscosity with Hall *** heat transfer analysis,the effects of the Cattaneo–Christov theory and heat generation/absorption with thermal slip are integrated into the temperature *** Tiwari–Das nanofluid model is used to develop the envisioned mathematical *** similarity transformation,the governing equations for the flow are translated into ordinary differential *** bvp4c method based on Runge–Kutta is used,along with a shooting *** are used to examine and depict the consequences of significant parameters on involved *** results revealed that the temperature of the fluid and boundary layer thickness is diminished as the solid volume fraction is ***,with an enhancement in the variable viscosity parameter,the velocity distribution becomes more *** results are substantiated by assessing them with an available study.

关键词： hybrid nanofluid dusty fluid variable viscosity Cattaneo-Christov heat flux model Hall current

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EMU-Net: Automatic Brain Tumor Segmentation and Classification Using Efficient Modified U-Net

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Computers, Materials & Continua 2023年第10期77卷 557-582页

作者： Mohammed Aly Abdullah Shawan alotaibi Department of Artificial Intelligence Faculty of Artificial IntelligenceEgyptian Russian UniversityBadr City11829Egypt Computer Science Department Shaqra UniversityShaqra City11961Saudi Arabia

Tumor segmentation is a valuable tool for gaining insights into tumors and improving treatment *** segmentation is crucial but *** learning methods have emerged as key players in automating brain tumor *** this paper,we propose an efficient modified U-Net architecture,called EMU-Net,which is applied to the BraTS 2020 *** approach is organized into two distinct phases:classification and *** this study,our proposed approach encompasses the utilization of the gray-level co-occurrence matrix(GLCM)as the feature extraction algorithm,convolutional neural networks(CNNs)as the classification algorithm,and the chi-square method for feature *** simulation results,the chi-square method for feature selection successfully identifies and selects four GLCM *** utilizing the modified U-Net architecture,we achieve precise segmentation of tumor images into three distinct regions:the whole tumor(WT),tumor core(TC),and enhanced tumor(ET).The proposed method consists of two important elements:an encoder component responsible for down-sampling and a decoder component responsible for *** components are based on a modified U-Net architecture and are connected by a bridge *** proposed CNN architecture achieves superior classification accuracy compared to existing methods,reaching up to 99.65%.Additionally,our suggested technique yields impressive Dice scores of 0.8927,0.9405,and 0.8487 for the tumor core,whole tumor,and enhanced tumor,***,the method presented demonstrates a higher level of trustworthiness and accuracy compared to existing *** promising accuracy of the EMU-Net study encourages further testing and evaluation in terms of extrapolation and generalization.

关键词： MR images GLCM SVM segmentation U-Net CNN

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