Iqbal H. Sarker

Dr. Iqbal H. Sarker

Edith Cowan University, Perth, Western Australia, Australia
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Research Focus & Metrics

Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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22 March 2021 in SN Computer Science
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Journal Article
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Iqbal H. SarkerIqbal H. Sarker
Iqbal H. Sarker


10 February 2022 in SN Computer Science
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Iqbal H. SarkerIqbal H. Sarker
Iqbal H. Sarker
Artificial intelligence (AI) is a leading technology of the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR), with the capability of incorporating human behavior and intelligence into machines or systems. Thus, AI-based modeling is the key to build automated, intelligent, and smart systems according to today’s needs. To solve real-world issues, various types of AI such as analytical, functional, interactive, textual, and visual AI can be applied to enhance the intelligence and capabilities of an application. However, developing an effective AI model is achallenging taskdue to the dynamic nature and variation in real-world problems and data. In this paper, we present a comprehensive view on“AI-based Modeling”with the principles and capabilities of potentialAI techniquesthat can play an important role in developing intelligent and smart systems in variousreal-world applicationareas including business, finance, healthcare, agriculture, smart cities, cybersecurity and many more. We also emphasize and highlight theresearch issueswithin the scope of our study. Overall, the goal of this paper is to provide a broad overview of AI-based modeling that can be used as areference guideby academics and industry people as well as decision-makers in various real-world scenarios and application domains.

1 July 2020 in Journal of Big Data
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Journal Article
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Iqbal H. SarkerIqbal H. Sarker
Iqbal H. Sarker
A. S. M. KayesA. S. M. Kayes
A. S. M. Kayes
SBShahriar Badsha
Shahriar Badsha
HAHamed Alqahtani
Hamed Alqahtani
Paul WattersPaul Watters
Paul Watters
ANAlex Ng
Alex Ng
In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extractingsecurity incident patternsor insights from cybersecurity data and building correspondingdata-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss oncybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement thelatest data-driven patternsfor providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associatedresearch issues and future directions. Furthermore, we provide amachine learningbasedmulti-layered frameworkfor the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.


26 March 2021 in SN Computer Science
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Journal Article
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PDF file
Iqbal H. SarkerIqbal H. Sarker
Iqbal H. Sarker
HFHasan Furhad
Hasan Furhad
RNRaza Nowrozy
Raza Nowrozy