Dr. Iqbal H. Sarker

Edith Cowan University, Cybersecurity CRC, Post-Doc

Swinburne University of Technology, Computer Science & Software Engineering, Graduate Student

University of Technology Sydney, Computer Science, Adjunct

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Dr. Iqbal H. Sarker is a Research Fellow at the Centre for Securing Digital Futures, Edith Cowan University (ECU), Australia. He obtained his Ph.D. in Computer Science from Swinburne University of Technology, Melbourne, Australia. He also worked as a Postdoctoral Fellow at the Cyber Security Cooperative Research Centre (CSCRC), Australia, through an academia-industry collaboration. He is also an Honorary Fellow at the School of Computer Science, University of Technology Sydney (UTS), Australia.

Dr. Sarker's research focuses on AI and Data-driven technologies to tackle real-world cybersecurity challenges. He has authored over 100 research papers in top-tier journals and conferences published by renowned scientific publishers such as Elsevier, Springer Nature, IEEE, ACM, and Oxford University Press. He is also the lead author of two Springer books: (i) Context-Aware Machine Learning and Mobile Data Analytics; and (ii) AI-Driven Cybersecurity and Threat Intelligence. In recognition of his contributions, Dr. Sarker has been listed among the world's top 2% most-cited scientists, as published by Elsevier and Stanford University, USA.

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Google Scholar: https://scholar.google.com.au/citations?user=FR9o1hcAAAAJ&hl=en
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Email contact (m.sarker@ecu.edu.au)

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Papers by Dr. Iqbal H. Sarker

LLM Potentiality and Awareness: A Position Paper from the Perspective of Trustworthy and Responsible AI Modeling

Large Language Models (LLMs) are an exciting breakthrough in the rapidly growing field of artific... more Large Language Models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, business, healthcare, cybersecurity, and so on. However, concerns regarding their trustworthiness and ethical implications have become increasingly prominent as these models are considered black-box and continue to progress. This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness. Towards this, we highlight not only the technical challenges but also the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, transparency, explainability, trust and accountability. We conclude this paper by summarizing potential research scopes with direction. Overall, the purpose of this position paper is to contribute to the ongoing discussion of LLM potentiality and awareness from the perspective of trustworthiness and responsibility in AI.

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Discretization of Time-Series Behavioral Data and Rule Generation based on Temporal Context

Springer eBooks, 2021

An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham -A Large Language Model Approach

by Suhaima Jamal, Hayden Wimmer, and Dr. Iqbal H. Sarker

Phishing and spam detection is long standing challenge that has been the subject of much academic... more Phishing and spam detection is long standing challenge that has been the subject of much academic research. Large Language Models (LLM) have vast potential to transform society and provide new and innovative approaches to solve well-established challenges. Phishing and spam have caused financial hardships and lost time and resources to email users all over the world and frequently serve as an entry point for ransomware threat actors. While detection approaches exist, especially heuristic-based approaches, LLMs offer the potential to venture into a new unexplored area for understanding and solving this challenge. LLMs have rapidly altered the landscape from business, consumers, and throughout academia and demonstrate transformational potential for the potential of society. Based on this, applying these new and innovative approaches to email detection is a rational next step in academic research. In this work, we present IPSDM, our model based on fine-tuning the BERT family of models to specifically detect phishing and spam email. We demonstrate our fine-tuned version, IPSDM, is able to better classify emails in both unbalanced and balanced datasets. This work serves as an important first step towards employing LLMs to improve the security of our information systems.

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Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects

Due to the digitization and Internet of Things revolutions, the present electronic world has a we... more Due to the digitization and Internet of Things revolutions, the present electronic world has a wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a growing concern in today's cyber security industry all over the world. Traditional security solutions are insufficient to address contemporary security issues due to the rapid proliferation of many sorts of cyber-attacks and threats. Utilizing artificial intelligence knowledge, especially machine learning technology, is essential to providing a dynamically enhanced, automated, and up-to-date security system through analyzing security data. In this paper, we provide an extensive view of machine learning algorithms, emphasizing how they can be employed for intelligent data analysis and automation in cybersecurity through their potential to extract valuable insights from cyber data. We also explore a number of potential real-world use cases where data-driven intelligence, automation, and decision...

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Multi-aspects AI-based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview

Security and Privacy, Wiley, 2023

Due to the rising dependency on digital technology, cybersecurity has emerged as a more prominent... more Due to the rising dependency on digital technology, cybersecurity has emerged as a more prominent field of research and application that typically focuses on securing devices, networks, systems, data and other resources from various cyber-attacks, threats, risks, damages, or unauthorized access. Artificial intelligence (AI), also referred to as a crucial technology of the current Fourth Industrial Revolution (Industry 4.0 or 4IR), could be the key to intelligently dealing with these cyber issues. Various forms of AI methodologies, such as analytical, functional, interactive, textual as well as visual AI can be employed to get the desired cyber solutions according to their computational capabilities. However, the dynamic nature and complexity of real-world situations and data gathered from various cyber sources make it challenging nowadays to build an effective AI-based security model. Moreover, defending robustly against adversarial attacks is still an open question in the area. In this article, we provide a comprehensive view on "Cybersecurity Intelligence and Robustness," emphasizing multi-aspects AI-based modeling and adversarial learning that could lead to addressing diverse issues in various cyber applications areas such as detecting malware or intrusions, zero-day attacks, phishing, data breach, cyberbullying and other cybercrimes. Thus the eventual security modeling process could be automated, intelligent, and robust compared to traditional security systems. We also emphasize and draw attention to the future aspects of cybersecurity intelligence and robustness along with the research direction within the context of our study. Overall, our goal is not only to explore AI-based modeling and pertinent methodologies but also to focus on the resulting model's applicability for securing our digital systems and society.

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OUP accepted manuscript

The Computer Journal, 2019

The electronic calendar is a valuable resource nowadays for managing our daily life appointments ... more The electronic calendar is a valuable resource nowadays for managing our daily life appointments or schedules, also known as events, ranging from professional to highly personal. Researchers have studied various types of calendar events to predict smartphone user behavior for incoming mobile communications. However, these studies typically do not take into account behavioral variations between individuals. In the real world, smartphone users can differ widely from each other in how they respond to incoming communications during their scheduled events. Moreover, an individual user may respond the incoming communications differently in different contexts subject to what type of event is scheduled in her personal calendar. Thus, a static calendar-based behavioral model for individual smartphone users does not necessarily reflect their behavior to the incoming communications. In this paper, we present a machine learning based context-aware model that is personalized and dynamically iden...

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Effectively predicting cyber‐attacks through isolation forest learning‐based outlier detection

SECURITY AND PRIVACY, 2022

Due to the popularity of Internet of Things devices, the exponential progress of computer network... more Due to the popularity of Internet of Things devices, the exponential progress of computer networks, and a plethora of associated applications, cybersecurity has recently attracted much attention in light of today's security problems. As a result, detecting various cyber‐attacks within a network and developing an effective cyber‐attacks prediction model that plays a crucial part in today's defense has become increasingly critical. Modeling cyber‐attacks effectively, on the other hand, is challenging because modern security datasets hold a large number of dimensions of security features and may contain outliers. To accomplish this, we provide an approach for categorizing cyber‐attacks effectively through isolation forest learning‐based outlier detection. Additionally, we apply a variety of popular machine learning approaches to assess the performance of cyber‐attacks prediction models, including logistic regression, support vector machine, AdaBoost classifier, naive Bayes, and K‐nearest neighbor. We evaluated the efficacy of our approach by running tests on three network intrusion datasets (KDD Cup 99, CIC‐IDS2017, and UNSW‐NB15) and computing the precision, recall, and accuracy. Experiments demonstrate that eliminating outliers improves the prediction accuracy of cyber‐attacks for different classifiers. Additionally, we compare the isolation forest learning‐based outlier detection model to other well‐known outlier detection techniques, DBSCAN and k‐means, and measure the effectiveness of our model.

ABC-RuleMiner: User Behavioral Rule-based Machine Learning Method for Context-Aware Intelligent Services

Journal of Network and Computer Applications, Elsevier, 2020

This paper formulates the problem of a rule-based machine learning method to discover the behavio... more This paper formulates the problem of a rule-based machine learning method to discover the behavioral rules of individual smartphone users to provide context-aware intelligent services. Smartphones nowadays are considered as one of the most important Internetof-Things (IoT) devices for providing various context-aware personalized services. These devices can record individuals' contextual data-for example, temporal, spatial, or social contexts, and their daily behavioral activity records. Association rule mining (ARM) is the most popular rule-based machine learning method for discovering rules for a particular constraint preference utilizing a given dataset. However, it generates numerous uninteresting contextual associations which lead to generate huge number of redundant rules that become useless in making context-aware decisions. This redundant generation makes not only the rule-set unnecessarily large but also makes the context-aware decision making process more complex and ineffective. To minimize these issues, in this paper, we propose a rule-based machine learning method "ABC-RuleMiner" that effectively identifies the redundancy in associations and discovers a set of non-redundant behavioral rules (IF-THEN) by taking into account the precedence of relevant contexts. Our experiments on individuals' contextual smartphone datasets show that this rule discovery approach is more effective while comparing with traditional rule-based methods.

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Designing architecture of a rule-based system for managing phone call interruptions

Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, 2017

Now-a-days, mobile phones are considered to be "always on, always connected" but mobile phone use... more Now-a-days, mobile phones are considered to be "always on, always connected" but mobile phone users are not always attentive and responsive to incoming phone calls. Incoming call notifications such as ringing at an inopportune moment (e.g., meeting) can cause interruptions for both the users and the surrounding people. In this paper, we present a system architecture for managing call interruptions according to individual's call response behavioral rules.

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An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies

Hybrid Intelligent Systems, 2021

Cybersecurity has recently gained considerable interest in today's security issues because of the... more Cybersecurity has recently gained considerable interest in today's security issues because of the popularity of the Internet-of-Things (IoT), the considerable growth of mobile networks, and many related apps. Therefore, detecting numerous cyber-attacks in a network and creating an effective intrusion detection system plays a vital role in today's security. However, it is difficult to accurately model cyber threats since modern security databases contain large number of security features that could include Outliers. In this paper, we present an Isolation Forest Learning-Based Outlier Detection Model for effectively classifying cyber anomalies. In order to evaluate the efficacy of the resulting Outlier Detection model, we also use several conventional machine learning approaches, such as Logistic Regression (LR), Support Vector Machine (SVM), AdaBoost Classifier (ABC), Naive Bayes (NB), and K-Nearest Neighbor (KNN). The effectiveness of our propsoed Outlier Detection model is evaluated by conducting experiments on Network Intrusion Dataset with evaluation metrics such as precision, recall, F1-score, and accuracy. Experimental results show that the classification accuracy of cyber anomalies has been improved after removing outliers.

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A Data-Driven Heart Disease Prediction Model Through K-Means Clustering-Based Anomaly Detection

SN Computer Science, 2021

Heart disease, alternatively known as cardiovascular disease, is the primary basis of death world... more Heart disease, alternatively known as cardiovascular disease, is the primary basis of death worldwide over the past few decades. To make an early diagnosis, a data-driven prediction model considering the associate risk factors in heart disease can play a significant role in healthcare domain. However, to build such an effective model based on machine learning techniques, the quality of the data, e.g., data without "anomalies" or outliers, is important. This research investigates anomaly detection in the healthcare domain to effectively predict heart disease using unsupervised K-means clustering algorithm. Our proposed model first determines an optimal value of K using the Silhouette method to form the clusters for finding the anomalies. After that, we eliminate the identified anomalies from the data and employ the five most popular machine learning classification techniques, such as K-nearest neighbor, random forest, support vector machine, naive Bayes, and logistic regression to build the resultant prediction model. The efficacy of the proposed methodology is justified using a standard heart disease dataset. We also take into account the data plotting to test the exactness of the detection of anomalies in our experimental analysis.

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AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems

Artificial Intelligence (AI) is a leading technology of the current age of the Fourth Industrial ... more 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 building 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 a challenging task due 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 potential AI techniques that can play an important role in developing intelligent and smart systems in various real-world application areas including business, finance, healthcare, agriculture, smart ...

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Contextual Mobile Datasets, Pre-processing and Feature Selection

Context-Aware Machine Learning and Mobile Data Analytics, 2021

Identifying Recent Behavioral Data Length in Mobile Phone Log

Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 2017

Mobile phone log data (e.g., phone call log) is not static as it is progressively added to day-by... more Mobile phone log data (e.g., phone call log) is not static as it is progressively added to day-by-day according to individual's diverse behaviors with mobile phones. Since human behavior changes over time, the most recent pattern is more interesting and significant than older ones for predicting individual's behavior. The goal of this poster paper is to identify the recent behavioral data length dynamically from the entire phone log for recency-based behavior modeling. To the best of our knowledge, this is the first dynamic recent log-based study that takes into account individual's recent behavioral patterns for modeling their phone call behaviors.

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Discovering User Behavioral Rules Based on Multi-Dimensional Contexts

Context-Aware Machine Learning and Mobile Data Analytics, 2021

Behavior-Oriented Time Segmentation for Mining Individualized Rules of Mobile Phone Users

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2016

Mobile or cellular phones can record various types of context data related to a user's phone ... more Mobile or cellular phones can record various types of context data related to a user's phone call activities. In this paper, we present an approach to discovering individualized behavior rules for mobile users from their phone call records, based on the temporal context in which a user accepts, rejects or misses a call. One of the determinants of an individual's phone behavior is the various activities undertaken at various times of a day and days of the week. In many cases, such behavior will follow temporal patterns. Currently, researchers modeling user behavior using temporal context statically segment time into arbitrary categories (e.g., morning, evening) or periods (e.g., 1 hour). However, such time categorization does not necessarily map to the patterns of individual user activity and subsequent behavior. Therefore, we propose a behavior-oriented time segmentation (BOTS) technique that dynamically identifies diverse time segments for an individual user's behaviors based on the phone call records. Experiments on real datasets show that our proposed technique better captures the user's dominant call response behavior at various times of the day and week, thereby enabling more appropriate rules to be created for the purpose of automated handling of incoming calls, in an intelligent call interruption management system.

An Effective Heart Disease Prediction Model based on Machine Learning Techniques

This paper presents an effective heart disease prediction model through detecting the anomalies, ... more This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an \textit{optimal} value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.

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An Approach to Modeling Call Response Behavior on Mobile Phones Based on Multi-Dimensional Contexts

2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft), 2017

Due to the popularity of context-aware computingand the rapid growth of the smart phone devices, ... more Due to the popularity of context-aware computingand the rapid growth of the smart phone devices, modeling anindividual's phone call response behavior may assist them intheir daily activities for managing call interruptions. A key stepof such modeling is to discovering call response behavioral rulesbased on multi-dimensional contexts related to individual'sbehavior. Currently, researchers use classification rule learnersfor modeling individual's mobile phone behavior. However, theproblem is that such learning techniques produce only rulesthat include maximal number of contexts albeit ordered byrelevance. This results in many rules with low-reliability thatdecrease the accuracy of the modeling approach. In this paper, we propose an approach (Tmodel) to modeling individual'sphone call response behavior utilizing mobile phone data. Thisapproach produces not only general rules that capture individual'sbehavior at a particular level of confidence with a minimalnumber o...

Evidence-Based Behavioral Model for Calendar Schedules of Individual Mobile Phone Users

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2016

The electronic calendar usually serves as a personal organizer and is a valuable resource for man... more The electronic calendar usually serves as a personal organizer and is a valuable resource for managing daily activities or schedules of the users. Naturally, a calendar provides various contextual information about individual's scheduled events/appointments, e.g., meeting. A number of researchers have utilized such information to predict human behavior for mobile communication, by assuming a predefined event-behavior mapping which is static and non-personalized. However, in the real world, people differ from each other in how they respond to incoming calls during their scheduled events, even a particular individual may respond differently subject to what type of event is scheduled in the calendar. Thus a static behavioral model does not necessarily map to calendar schedules and corresponding phone call response behavior of individuals. Therefore, we propose an evidencebased behavioral model (EBM) that dynamically identifies the actual call response behavior of individuals for va...

An Improved Naive Bayes Classifier-Based Noise Detection Technique for Classifying User Phone Call Behavior

Communications in Computer and Information Science, 2018

The presence of noisy instances in mobile phone data is a fundamental issue for classifying user ... more The presence of noisy instances in mobile phone data is a fundamental issue for classifying user phone call behavior (i.e., accept, reject, missed and outgoing), with many potential negative consequences. The classification accuracy may decrease and the complexity of the classifiers may increase due to the number of redundant training samples. To detect such noisy instances from a training dataset, researchers use naive Bayes classifier (NBC) as it identifies misclassified instances by taking into account independence assumption and conditional probabilities of the attributes. However, some of these misclassified instances might indicate usages behavioral patterns of individual mobile phone users. Existing naive Bayes classifier based noise detection techniques have not considered this issue and, thus, are lacking in classification accuracy. In this paper, we propose an improved noise detection technique based on naive Bayes classifier for effectively classifying users' phone call behaviors. In order to improve the classification accuracy, we effectively identify noisy instances from the training dataset by analyzing the behavioral patterns of individuals. We dynamically determine a noise threshold according to individual's unique behavioral patterns by using both the naive Bayes classifier and Laplace estimator. We use this noise threshold to identify noisy instances. To measure the effectiveness of our technique in classifying user phone call behavior, we employ the most popular classification algorithm (e.g., decision tree). Experimental results on the real phone call log dataset show that our proposed technique more accurately identifies the noisy instances from the training datasets that leads to better classification accuracy.

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Dr. Iqbal H. Sarker

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