Security assessment framework for educational ERP systems

Ashraf, Hafsa; Alenezi, Mamdouh; Nadeem, Muhammad; Javid, Yasir

doi:10.11591/ijece.v9i6.pp5570-5585

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Perhaps this is the first study to look at libraries for large data analysis that are based on machine learning techniques. Big data analytics is primarily concerned with the advancement of computer infrastructures in such a way that data mining and analysis can be completed quickly and efficiently [45], [52]- [56]. It is the primary driving force behind the existing business.…”

Section: Introductionmentioning

confidence: 99%

Large scale data analysis using MLlib

et al. 2021

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Recent advancements in the internet, social media, and internet of things (IoT) devices have significantly increased the amount of data generated in a variety of formats. The data must be converted into formats that is easily handled by the data analysis techniques. It is mathematically and physically expensive to apply machine learning algorithms to big and complicated data sets. It is a resource-intensive process that necessitates a huge amount of logical and physical resources. Machine learning is a sophisticated data analytics technology that has gained in importance as a result of the massive amount of data generated daily that needs to be examined. Apache Spark machine learning library (MLlib) is one of the big data analysis platforms that provides a variety of outstanding functions for various machine learning tasks, spanning from classification to regression and dimension reduction. From a computational standpoint, this research investigated Apache Spark MLlib 2.0 as an open source, autonomous, scalable, and distributed learning library. Several real-world machine learning experiments are carried out in order to evaluate the properties of the platform on a qualitative and quantitative level. Some of the fundamental concepts and approaches for developing a scalable data model in a distributed environment are also discussed.

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Section: Introductionmentioning

confidence: 99%

Large scale data analysis using MLlib

et al. 2021

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“…Many software developers are not aware of and equipped with enough security education. In addition, many programming books do not teach how to write secure programs [1,2].…”

Section: Introductionmentioning

confidence: 99%

An automated approach to fix buffer overflows

Shahab¹,

Nadeem²,

Alenezi³

et al. 2020

IJECE

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Buffer overflows are one of the most common software vulnerabilities that occur when more data is inserted into a buffer than it can hold. Various manual and automated techniques for detecting and fixing specific types of buffer overflow vulnerability have been proposed, but the solution to fix Unicode buffer overflow has not been proposed yet. Public security vulnerability repository e.g., Common Weakness Enumeration (CWE) holds useful articles about software security vulnerabilities. Mitigation strategies listed in CWE may be useful for fixing the specified software security vulnerabilities. This research contributes by developing a prototype that automatically fixes different types of buffer overflows by using the strategies suggested in CWE articles and existing research. A static analysis tool has been used to evaluate the performance of the developed prototype tools. The results suggest that the proposed approach can automatically fix buffer overflows without inducing errors.

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Comparative Analysis of Open-Source Vulnerability Assessment Tools for Campus Area Network

Sharma

Pahuja

2023

2023 International Conference on Emerging Smart Computing and Informatics (ESCI)

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Security assessment framework for educational ERP systems

Cited by 3 publications

References 15 publications

Large scale data analysis using MLlib

Large scale data analysis using MLlib

An automated approach to fix buffer overflows

Comparative Analysis of Open-Source Vulnerability Assessment Tools for Campus Area Network

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