Security and privacy aspects of cloud computing : a smart campus case study
- Gill, Sajid, Razzaq, Mirza, Ahmad, Muneer, Almansour, Fahad, Haq, Ikram
- Authors: Gill, Sajid , Razzaq, Mirza , Ahmad, Muneer , Almansour, Fahad , Haq, Ikram
- Date: 2022
- Type: Text , Journal article
- Relation: Intelligent Automation and Soft Computing Vol. 31, no. 1 (2022), p. 117-128
- Full Text:
- Reviewed:
- Description: The trend of cloud computing is accelerating along with emerging technologies such as utility computing, grid computing, and distributed computing. Cloud computing is showing remarkable potential to provide flexible, cost- effective, and powerful resources across the internet, and is a driving force in today’s most prominent computing technologies. The cloud offers the means to remotely access and store data while virtual machines access data over a network resource. Furthermore, cloud computing plays a leading role in the fourth industrial revolution. Everyone uses the cloud daily life when accessing Dropbox, various Google services, and Microsoft Office 365. While there are many advantages in such an environment, security issues such as data privacy, data security, access control, cyber-attacks, and data availability, along with performance and reliability issues, exist. Efficient security and privacy measures should be implemented by cloud service providers to ensure the privacy, confidentiality, integrity, and availability of data services. However, cloud service providers have not been providing enough secure and reliable services to end users. Blockchain is a technology that is improving cloud computing. This revolutionary technology offers persuasive data integrity properties and is used to tackle security problems. This research presents a detailed analysis of privacy and security challenges in the cloud. We demonstrate the importance of security challenges in a case study in the context of smart campus security, which will encourage researchers to examine security issues in cloud computing in the future. © 2022, Tech Science Press. All rights reserved. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Ikram Haq” is provided in this record**
- Authors: Gill, Sajid , Razzaq, Mirza , Ahmad, Muneer , Almansour, Fahad , Haq, Ikram
- Date: 2022
- Type: Text , Journal article
- Relation: Intelligent Automation and Soft Computing Vol. 31, no. 1 (2022), p. 117-128
- Full Text:
- Reviewed:
- Description: The trend of cloud computing is accelerating along with emerging technologies such as utility computing, grid computing, and distributed computing. Cloud computing is showing remarkable potential to provide flexible, cost- effective, and powerful resources across the internet, and is a driving force in today’s most prominent computing technologies. The cloud offers the means to remotely access and store data while virtual machines access data over a network resource. Furthermore, cloud computing plays a leading role in the fourth industrial revolution. Everyone uses the cloud daily life when accessing Dropbox, various Google services, and Microsoft Office 365. While there are many advantages in such an environment, security issues such as data privacy, data security, access control, cyber-attacks, and data availability, along with performance and reliability issues, exist. Efficient security and privacy measures should be implemented by cloud service providers to ensure the privacy, confidentiality, integrity, and availability of data services. However, cloud service providers have not been providing enough secure and reliable services to end users. Blockchain is a technology that is improving cloud computing. This revolutionary technology offers persuasive data integrity properties and is used to tackle security problems. This research presents a detailed analysis of privacy and security challenges in the cloud. We demonstrate the importance of security challenges in a case study in the context of smart campus security, which will encourage researchers to examine security issues in cloud computing in the future. © 2022, Tech Science Press. All rights reserved. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Ikram Haq” is provided in this record**
Fraud detection for online banking for scalable and distributed data
- Authors: Haq, Ikram
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: Online fraud causes billions of dollars in losses for banks. Therefore, online banking fraud detection is an important field of study. However, there are many challenges in conducting research in fraud detection. One of the constraints is due to unavailability of bank datasets for research or the required characteristics of the attributes of the data are not available. Numeric data usually provides better performance for machine learning algorithms. Most transaction data however have categorical, or nominal features as well. Moreover, some platforms such as Apache Spark only recognizes numeric data. So, there is a need to use techniques e.g. One-hot encoding (OHE) to transform categorical features to numerical features, however OHE has challenges including the sparseness of transformed data and that the distinct values of an attribute are not always known in advance. Efficient feature engineering can improve the algorithm’s performance but usually requires detailed domain knowledge to identify correct features. Techniques like Ripple Down Rules (RDR) are suitable for fraud detection because of their low maintenance and incremental learning features. However, high classification accuracy on mixed datasets, especially for scalable data is challenging. Evaluation of RDR on distributed platforms is also challenging as it is not available on these platforms. The thesis proposes the following solutions to these challenges: • We developed a technique Highly Correlated Rule Based Uniformly Distribution (HCRUD) to generate highly correlated rule-based uniformly-distributed synthetic data. • We developed a technique One-hot Encoded Extended Compact (OHE-EC) to transform categorical features to numeric features by compacting sparse-data even if all distinct values are unknown. • We developed a technique Feature Engineering and Compact Unified Expressions (FECUE) to improve model efficiency through feature engineering where the domain of the data is not known in advance. • A Unified Expression RDR fraud deduction technique (UE-RDR) for Big data has been proposed and evaluated on the Spark platform. Empirical tests were executed on multi-node Hadoop cluster using well-known classifiers on bank data, synthetic bank datasets and publicly available datasets from UCI repository. These evaluations demonstrated substantial improvements in terms of classification accuracy, ruleset compactness and execution speed.
- Description: Doctor of Philosophy
- Authors: Haq, Ikram
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: Online fraud causes billions of dollars in losses for banks. Therefore, online banking fraud detection is an important field of study. However, there are many challenges in conducting research in fraud detection. One of the constraints is due to unavailability of bank datasets for research or the required characteristics of the attributes of the data are not available. Numeric data usually provides better performance for machine learning algorithms. Most transaction data however have categorical, or nominal features as well. Moreover, some platforms such as Apache Spark only recognizes numeric data. So, there is a need to use techniques e.g. One-hot encoding (OHE) to transform categorical features to numerical features, however OHE has challenges including the sparseness of transformed data and that the distinct values of an attribute are not always known in advance. Efficient feature engineering can improve the algorithm’s performance but usually requires detailed domain knowledge to identify correct features. Techniques like Ripple Down Rules (RDR) are suitable for fraud detection because of their low maintenance and incremental learning features. However, high classification accuracy on mixed datasets, especially for scalable data is challenging. Evaluation of RDR on distributed platforms is also challenging as it is not available on these platforms. The thesis proposes the following solutions to these challenges: • We developed a technique Highly Correlated Rule Based Uniformly Distribution (HCRUD) to generate highly correlated rule-based uniformly-distributed synthetic data. • We developed a technique One-hot Encoded Extended Compact (OHE-EC) to transform categorical features to numeric features by compacting sparse-data even if all distinct values are unknown. • We developed a technique Feature Engineering and Compact Unified Expressions (FECUE) to improve model efficiency through feature engineering where the domain of the data is not known in advance. • A Unified Expression RDR fraud deduction technique (UE-RDR) for Big data has been proposed and evaluated on the Spark platform. Empirical tests were executed on multi-node Hadoop cluster using well-known classifiers on bank data, synthetic bank datasets and publicly available datasets from UCI repository. These evaluations demonstrated substantial improvements in terms of classification accuracy, ruleset compactness and execution speed.
- Description: Doctor of Philosophy
- «
- ‹
- 1
- ›
- »