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1. Secure monitoring system for industrial Internet of Things using searchable encryption, access control and machine learning

Author

Aljabri, Jawhara Bader

Subjects
QA75 Electronic computers. Computer science
Abstract

This thesis is an alternative format submission comprising a set of publications and a comprehensive literature review, an introduction, and a conclusion. Continuous compliance with data protection legislation on many levels in the Industrial Internet of Things (IIoT) is a significant challenge. Automated continuous compliance should also consider adaptable security compliance management for multiple users. The IIoT should automate compliance with corporate rules, regulations, and regulatory frameworks for industrial applications. Thus, this thesis aims to improve continuous compliance by introducing an edge-server architecture which incorporates searchable encryption with multi-authority access to provide access to useful data for various stakeholders in the compliance domain. In this thesis, we propose an edge lightweight searchable attribute-based encryption system (ELSA). The ELSA system leverages cloud-edge architecture to improve search time beyond a previous state-ofthe-art encryption solution. The main contributions of the first paper are as follows. First, we npresent an untrusted cloud and trusted edge architecture that processes data efficiently and optimises decision-making in the IIoT context. Second, we enhanced the search performance over the current state-of-the-art (LSABE-MA) regarding order of magnitude. We achieved this enhancement by storing keywords only on the trusted edge server and introducing a query optimiser to achieve better-than-linear search performance. The query optimiser uses k-means clustering to improve the efficiency of range queries, removing the need for a linear search. As a result, we achieved higher performance without sacrificing result accuracy. In the second paper, we extended ELSA to illustrate the correlation between the number of keywords and ELSA performance. This extension supports annotating records with multiple keywords in trapdoor and record storage and enables the record to be returned with single keyword queries. In addition, the experiments demonstrated the scalability and efficiency of ELSA with an increasing number of keywords and complexity. Based on the experimental results and feedback received from the publication and presentation of this work, we published our third technical paper. In this paper, we improved ELSA by minimising the lookup table size and summarising the data records by integrating machine-learning (ML) methods suitable for execution at the edge. This integration removes records of unnecessary data by evaluating added value to further processing. This process results in the minimisation of the lookup table size, the cloud storage, and the network traffic, taking full advantage of the edge architecture benefits. We demonstrated the mini-ELSA expanded method on two well-known IIoT datasets. Our results reveal a reduction of storage requirements by > 21% while improving execution time by > 1.39× and search time by > 50% and maintaining an optimal balance between prediction accuracy and space reduction. In addition, we present the computational complexity analysis that reinforces these experimental results.

Published
2023
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2. mini-ELSA: using Machine Learning to improve space efficiency in Edge Lightweight Searchable Attribute-based encryption for Industry 4.0

Author

Aljabri, Jawhara, Michala, Anna Lito, Singer, Jeremy, and Vourganas, Ioannis

Subjects
Computer Science - Machine Learning
Abstract

In previous work a novel Edge Lightweight Searchable Attribute-based encryption (ELSA) method was proposed to support Industry 4.0 and specifically Industrial Internet of Things applications. In this paper, we aim to improve ELSA by minimising the lookup table size and summarising the data records by integrating Machine Learning (ML) methods suitable for execution at the edge. This integration will eliminate records of unnecessary data by evaluating added value to further processing. Thus, resulting in the minimization of both the lookup table size, the cloud storage and the network traffic taking full advantage of the edge architecture benefits. We demonstrate our mini-ELSA expanded method on a well-known power plant dataset. Our results demonstrate a reduction of storage requirements by 21% while improving execution time by 1.27x.

Published
2022

5. ELSA: A Keyword-based Searchable Encryption for Cloud-edge assisted Industrial Internet of Things

Author

Aljabri, Jawhara, Michala, Anna Lito, and Singer, Jeremy

Abstract

The Industrial Internet of Things (IIoT) plays a powerful role in smart manufacturing by performing real-time analysis for large volumes of data. In addition, IIoT systems can monitor several factors, such as data accuracy, network bandwidth and operations latency. To perform these operations securely and in a privacy-preserving manner, one solution is to use cryptographic primitives. However, most cryptographic solutions add performance overhead causing latency. In this paper, we propose an Edge Lightweight Searchable Attribute-based encryption system (ELSA). ELSA leverages the cloud-edge architecture to improve search time beyond the state-of-the-art. The main contributions of this paper are as follows. First, we present an untrusted cloud/trusted edge architecture, which optimises the efficiency of data processing and decision making in the IIoT context. Second, we enhance search performance over current state-of-the-art (LSABE-MA) by an order of magnitude. We achieve this by improving the organisation of the data to provide better than linear search performance. We leverage the edge server to cluster data indices by keyword and introduce a query optimiser. The query optimiser uses k-means clustering to improve the efficiency of range queries, removing the need for linear search. In addition, we achieve this without sacrificing accuracy over the results.

Published
2022
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7. ENHANCING PEPPER LEAF DISEASE DETECTION USING DEEP TRANSFER LEARNING FOR SUSTAINABLE AGRICULTURAL SECTOR IN KSA.

Author

ALAZWARI, SANA, ZANIN, SAMAH AL, ALJABRI, JAWHARA, and ALNEIL, AMANI A.

Subjects
*SUSTAINABLE agriculture, *VERTICAL farming, *FARM produce, *PLANT classification, *AGRICULTURE
Abstract

Sustainable agriculture in the Kingdom of Saudi Arabia (KSA) intends to develop farming practices that maintain productivity while promoting economic viability and preserving environmental resources. Moreover, the country invests in advanced technologies, which include vertical farming and hydroponics, to reduce environmental impact and enhance food security. Globally, the pepper crop is one of the leading agricultural products of human food security. However, it is vulnerable to various diseases such as gray leaf spots, powdery mildew symptoms on pepper leaf, blight leaf disease, common rust, fruit rot disease, etc. Usually, farmers identify the disease through visual inspection; however, this has its drawbacks as it is generally time-consuming and inaccurate. Several research workers have previously presented different pepper plant disease classification techniques, mainly deep learning (DL) and image processing approaches. This paper introduces a novel Pepper Leaf Disease Detection using the Optimal Deep Transfer Learning (PLDD-ODTL) technique for the sustainable agricultural sector in KSA. As a preliminary preprocessing stage, the PLDD-ODTL technique initially utilizes an adaptive window filtering (AWF) approach to eliminate the noise in the plant images. Next, the PLDD-ODTL approach involves a feature fusion process comprising three DL approaches: residual neural network (ResNet), VGG-19, and DenseNet models. To enhance the performance of the DL techniques in biological systems modeling, the hyperparameter selection process is done by a hybrid artificial ecosystem fractal optimizer using a chaos game optimization (ACGO) technique. Finally, a deep belief network (DBN) is applied to classify the disease. A series of experiments were conducted to illustrate the enhanced performance of the PLDD-ODTL technique. The experimental results of the PLDD-ODTL technique portrayed a superior accuracy outcome of 99.71% over existing approaches. [ABSTRACT FROM AUTHOR]

Published
2024
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9. FROM PIXELS TO PREDICTIONS: ROLE OF BOOSTED DEEP LEARNING-ENABLED OBJECT DETECTION FOR AUTONOMOUS VEHICLES ON LARGE SCALE CONSUMER ELECTRONICS ENVIRONMENT.

Author

ALKHONAINI, MIMOUNA ABDULLAH, MENGASH, HANAN ABDULLAH, NEMRI, NADHEM, EBAD, SHOUKI A., ALOTAIBI, FAIZ ABDULLAH, ALJABRI, JAWHARA, ALZAHRANI, YAZEED, and ALNFIAI, MRIM M.

Subjects
*OBJECT recognition (Computer vision), *SUSTAINABILITY, *CITY traffic, *SENSOR arrays, *SMART cities
Abstract

Consumer electronics (CE) companies have the potential to significantly contribute to the advancement of autonomous vehicles and their accompanying technology by providing security, connectivity, and efficiency. The Consumer Autonomous Vehicles market is set for significant growth, driven by growing awareness and implementation of sustainable practices using computing technologies for traffic flow optimization in smart cities. Businesses are concentrating more on eco-friendly solutions, using AI, communication networks, and sensors for autonomous city navigation, giving safer and more efficient mobility solutions in response to growing environmental concerns. Object detection is a crucial element of autonomous vehicles and complex systems, which enables them to observe and react to their surroundings in real-time. Multiple autonomous vehicles employ deep learning (DL) for detection and deploy specific sensor arrays custom-made to their use case or environment. DL processes sensory data for autonomous vehicles, enabling data-driven decisions on environmental reactions and obstacle recognition. This paper projects a Galactical Swarm Fractals Optimizer with DL-Enabled Object Detection for Autonomous Vehicles (GSODL-OOAV) model in Smart Cities. The presented GSODL-OOAV model enables the object identification for autonomous vehicles properly. To accomplish this, the GSODL-OOAV model initially employs a RetinaNet object detector to detect the objects effectively. Besides, the long short-term memory ensemble (BLSTME) technique was exploited to allot proper classes to the detected objects. A hyperparameter tuning procedure utilizing the GSO model is employed to enhance the classification efficiency of the BLSTME approach. The experimentation validation of the GSODL-OOAV technique is verified using the BDD100K database. The comparative study of the GSODL-OOAV approach illustrated a superior accuracy outcome of 99.06% over present innovative approaches. [ABSTRACT FROM AUTHOR]

Published
2024
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10. PEAK TO AVERAGE POWER COMPUTING AND OPTIMIZATION OF OPTICAL OTFS 5G WAVEFORM USING HYBRID FRACTAL-BASED SIGNAL PROCESSING ALGORITHM.

Author

KUMAR, ARUN, MAASHI, MASHAEL, ALSHAHRANI, HAYA MESFER, ARASI, MUNYA A., YAHYA, ABDULSAMAD EBRAHIM, NANTHAAMORNPHONG, AZIZ, ALJABRI, JAWHARA, and ALZAHRANI, YAZEED

Subjects
*BIT error rate, *SIGNAL processing, *DOPPLER effect, *OPTICAL computing, *POWER amplifiers
Abstract

Optical orthogonal time frequency space (OTFS) is better at handling Doppler shifts and multipath fading, making signals more reliable and valuable in places with much movement beyond the fifth generation (B5G). Using practical power amplifiers at the transmitter causes power inefficiency and signal distortion because of the OTFS system’s high peak-to-average power ratio (PAPR), severely reducing system efficiency. Combining partial transmit sequence (PTS) and selective mapping (SLM), a technique known as PTS+SLM, reduces peak power. While SLM generates numerous phase-modulated signal candidates and chooses the one with the lowest PAPR, PTS separates the signal into sub-blocks and optimizes their phases to decrease peak power. With few changes to the signal structure, this dual strategy effectively reduces PAPR while improving power spectral density (PSD) efficiency. As a result, we ensure the accuracy and dependability of the transferred data by maintaining the bit error rate (BER). Fractal optimization methods could be applied to these algorithms. For example, fractal-inspired optimization techniques might be used to explore the phase space more effectively or to discover new phase sequences that result in lower PAPR. According to the simulation findings, the suggested PTS+SLM method works better than the traditional PTS and SLM methods. [ABSTRACT FROM AUTHOR]

Published
2024
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11. ENHANCING NATURAL LANGUAGE PROCESSING USING WALRUS OPTIMIZER WITH SELF-ATTENTION DEEP LEARNING MODEL IN APPLIED LINGUISTICS.

Author

HASSAN, ABDULKHALEQ Q. A., ALSHAMMARI, ALYA, ZAQAIBEH, BELAL, ALZAIDI, MUHAMMAD SWAILEH A., ALLAFI, RANDA, ALAZWARI, SANA, ALJABRI, JAWHARA, and NOURI, AMAL M

Subjects
*NATURAL language processing, *CONVOLUTIONAL neural networks, *LANGUAGE models, *SOCIAL media, *RECURRENT neural networks, *DEEP learning, *INTERNET forums
Abstract

Sentiment analysis (SA) is the popular natural language processing (NLP) problem utilized for analyzing texts, including uploaded reviews and user posts on e-commerce portals, forums, and social media platforms, regarding their opinions about the event or person, product, and service. The SA task comprises analyzing text to determine whether the sentiment expressed is negative, positive, or neutral, aiming for precise subjective data analysis. The deep learning (DL) technique enables various architectures to model SA tasks and has surpassed other machine learning (ML) techniques as the first method to perform SA tasks. Recent advancements in the DL model depart from the growing dominance of transformer language algorithms and convolutional neural network (CNN) and recurrent neural network (RNN) models. Utilizing pre-trained transformer language models to transfer knowledge to downstream tasks has emerged as a cutting-edge model in NLP. Therefore, the study designs a fractal walrus optimizer with self-attention DL-based SA in applied linguistics (WOSADL-SAAL) method. The WOSADL-SAAL method aims to recognize and classify the presence of sentiments in social media content. In the WOSADL-SAAL technique, data preprocessing is initially performed to transform the input dataset into a meaningful format. In addition, the WOSADL-SAAL technique uses the bag of words (BoW) model for extracting features. The self-attention bidirectional long short-term memory (SA-BiLSTM) network is applied to classify sentiment. The walrus optimizer (WO) model performs the hyperparameter tuning model to boost the detection outcomes of the SA-BiLSTM network. The performance evaluation of the WOSADL-SAAL method takes place under the benchmark SA dataset. The experimental analysis of the WOSADL-SAAL method exhibited a superior accuracy value of 99.07% and 99.24% under TUSA and IMDB datasets over existing approaches. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Secure monitoring system for industrial internet of things using searchable encryption, access control and machine learning

Author

Aljabri, Jawhara Bader and Aljabri, Jawhara Bader

Abstract

This thesis is an alternative format submission comprising a set of publications and a comprehensive literature review, an introduction, and a conclusion. Continuous compliance with data protection legislation on many levels in the Industrial Internet of Things (IIoT) is a significant challenge. Automated continuous compliance should also consider adaptable security compliance management for multiple users. The IIoT should automate compliance with corporate rules, regulations, and regulatory frameworks for industrial applications. Thus, this thesis aims to improve continuous compliance by introducing an edge-server architecture which incorporates searchable encryption with multi-authority access to provide access to useful data for various stakeholders in the compliance domain. In this thesis, we propose an edge lightweight searchable attribute-based encryption system (ELSA). The ELSA system leverages cloud-edge architecture to improve search time beyond a previous state-ofthe-art encryption solution. The main contributions of the first paper are as follows. First, we npresent an untrusted cloud and trusted edge architecture that processes data efficiently and optimises decision-making in the IIoT context. Second, we enhanced the search performance over the current state-of-the-art (LSABE-MA) regarding order of magnitude. We achieved this enhancement by storing keywords only on the trusted edge server and introducing a query optimiser to achieve better-than-linear search performance. The query optimiser uses k-means clustering to improve the efficiency of range queries, removing the need for a linear search. As a result, we achieved higher performance without sacrificing result accuracy. In the second paper, we extended ELSA to illustrate the correlation between the number of keywords and ELSA performance. This extension supports annotating records with multiple keywords in trapdoor and record storage and enables the record to be returned with single keyword queries

13. Secure monitoring system for industrial internet of things using searchable encryption, access control and machine learning

Author

Aljabri, Jawhara Bader and Aljabri, Jawhara Bader

Abstract

This thesis is an alternative format submission comprising a set of publications and a comprehensive literature review, an introduction, and a conclusion. Continuous compliance with data protection legislation on many levels in the Industrial Internet of Things (IIoT) is a significant challenge. Automated continuous compliance should also consider adaptable security compliance management for multiple users. The IIoT should automate compliance with corporate rules, regulations, and regulatory frameworks for industrial applications. Thus, this thesis aims to improve continuous compliance by introducing an edge-server architecture which incorporates searchable encryption with multi-authority access to provide access to useful data for various stakeholders in the compliance domain. In this thesis, we propose an edge lightweight searchable attribute-based encryption system (ELSA). The ELSA system leverages cloud-edge architecture to improve search time beyond a previous state-ofthe-art encryption solution. The main contributions of the first paper are as follows. First, we npresent an untrusted cloud and trusted edge architecture that processes data efficiently and optimises decision-making in the IIoT context. Second, we enhanced the search performance over the current state-of-the-art (LSABE-MA) regarding order of magnitude. We achieved this enhancement by storing keywords only on the trusted edge server and introducing a query optimiser to achieve better-than-linear search performance. The query optimiser uses k-means clustering to improve the efficiency of range queries, removing the need for a linear search. As a result, we achieved higher performance without sacrificing result accuracy. In the second paper, we extended ELSA to illustrate the correlation between the number of keywords and ELSA performance. This extension supports annotating records with multiple keywords in trapdoor and record storage and enables the record to be returned with single keyword queries

14. Secure monitoring system for industrial internet of things using searchable encryption, access control and machine learning

Author

Aljabri, Jawhara Bader and Aljabri, Jawhara Bader

Abstract

This thesis is an alternative format submission comprising a set of publications and a comprehensive literature review, an introduction, and a conclusion. Continuous compliance with data protection legislation on many levels in the Industrial Internet of Things (IIoT) is a significant challenge. Automated continuous compliance should also consider adaptable security compliance management for multiple users. The IIoT should automate compliance with corporate rules, regulations, and regulatory frameworks for industrial applications. Thus, this thesis aims to improve continuous compliance by introducing an edge-server architecture which incorporates searchable encryption with multi-authority access to provide access to useful data for various stakeholders in the compliance domain. In this thesis, we propose an edge lightweight searchable attribute-based encryption system (ELSA). The ELSA system leverages cloud-edge architecture to improve search time beyond a previous state-ofthe-art encryption solution. The main contributions of the first paper are as follows. First, we npresent an untrusted cloud and trusted edge architecture that processes data efficiently and optimises decision-making in the IIoT context. Second, we enhanced the search performance over the current state-of-the-art (LSABE-MA) regarding order of magnitude. We achieved this enhancement by storing keywords only on the trusted edge server and introducing a query optimiser to achieve better-than-linear search performance. The query optimiser uses k-means clustering to improve the efficiency of range queries, removing the need for a linear search. As a result, we achieved higher performance without sacrificing result accuracy. In the second paper, we extended ELSA to illustrate the correlation between the number of keywords and ELSA performance. This extension supports annotating records with multiple keywords in trapdoor and record storage and enables the record to be returned with single keyword queries

15. Secure monitoring system for industrial internet of things using searchable encryption, access control and machine learning

Author

Aljabri, Jawhara Bader and Aljabri, Jawhara Bader

Abstract

This thesis is an alternative format submission comprising a set of publications and a comprehensive literature review, an introduction, and a conclusion. Continuous compliance with data protection legislation on many levels in the Industrial Internet of Things (IIoT) is a significant challenge. Automated continuous compliance should also consider adaptable security compliance management for multiple users. The IIoT should automate compliance with corporate rules, regulations, and regulatory frameworks for industrial applications. Thus, this thesis aims to improve continuous compliance by introducing an edge-server architecture which incorporates searchable encryption with multi-authority access to provide access to useful data for various stakeholders in the compliance domain. In this thesis, we propose an edge lightweight searchable attribute-based encryption system (ELSA). The ELSA system leverages cloud-edge architecture to improve search time beyond a previous state-ofthe-art encryption solution. The main contributions of the first paper are as follows. First, we npresent an untrusted cloud and trusted edge architecture that processes data efficiently and optimises decision-making in the IIoT context. Second, we enhanced the search performance over the current state-of-the-art (LSABE-MA) regarding order of magnitude. We achieved this enhancement by storing keywords only on the trusted edge server and introducing a query optimiser to achieve better-than-linear search performance. The query optimiser uses k-means clustering to improve the efficiency of range queries, removing the need for a linear search. As a result, we achieved higher performance without sacrificing result accuracy. In the second paper, we extended ELSA to illustrate the correlation between the number of keywords and ELSA performance. This extension supports annotating records with multiple keywords in trapdoor and record storage and enables the record to be returned with single keyword queries

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