Smart City Data Science: Towards data-driven smart cities with open research issues

Sarker, Iqbal H.

doi:10.1016/j.iot.2022.100528

Cited by 76 publications

(43 citation statements)

References 70 publications

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“…The outcomes of any analytical algorithm are directly impacted by data preprocessing techniques such as data cleaning, and transforming, as well as ensuring data quality for modeling. To ensure that the data behave properly, it is crucial to carry out particular data pre-processing operations before feeding the data into the eventual AI model 155 . Therefore the question is -"How to effectively represent and feed cyber data to a machine learning or AI model by ensuring data quality for a better outcome in a particular cyber issue?…”

Section: Real-world Applications With Research Issuesmentioning

confidence: 99%

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

Sarker

2023

Security and Privacy

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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 paper, 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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Section: Real-world Applications With Research Issuesmentioning

confidence: 99%

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

Sarker

2023

Security and Privacy

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“…For instance, a random forest [40] technique consists of multiple decision trees is used to detect anomalies [41,42]. Similarly, detecting denial of service attack [43], intrusions [44,45], smart city anomalies [1,46] the most popular forest technique can be used. The authors in [47] also offer a Bayesian networkbased ensemble learning solution for detecting XSS attacks with domain knowledge and threat intelligence.…”

Section: Classification and Regression Analysis In Cybersecuritymentioning

confidence: 99%

“…We live in the digital age, which, like anything else, has its upsides and downsides. The main drawback is the security risk [1,2]. As more of our sensitive information transfers to the digital arena, security breaches are becoming more common and catastrophic.…”

Section: Introductionmentioning

confidence: 99%

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

Sarker

2022

Ann. Data. Sci.

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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-making enable next-generation cyber protection that is more proactive than traditional approaches. The future prospects of machine learning in cybersecurity are eventually emphasized based on our study, along with relevant research directions. Overall, our goal is to explore not only the current state of machine learning and relevant methodologies but also their applicability for future cybersecurity breakthroughs.

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“…For example, Sarker et al [ 18 ] explained the concept of a smart city in their research study. At first, they explained the concept of a smart city.…”

Section: Basics and The Backgroundmentioning

confidence: 99%

A Step toward Next-Generation Advancements in the Internet of Things Technologies

Amin

Abbasi

Mateen

et al. 2022

Sensors

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The Internet of Things (IoT) devices generate a large amount of data over networks; therefore, the efficiency, complexity, interfaces, dynamics, robustness, and interaction need to be re-examined on a large scale. This phenomenon will lead to seamless network connectivity and the capability to provide support for the IoT. The traditional IoT is not enough to provide support. Therefore, we designed this study to provide a systematic analysis of next-generation advancements in the IoT. We propose a systematic catalog that covers the most recent advances in the traditional IoT. An overview of the IoT from the perspectives of big data, data science, and network science disciplines and also connecting technologies is given. We highlight the conceptual view of the IoT, key concepts, growth, and most recent trends. We discuss and highlight the importance and the integration of big data, data science, and network science along with key applications such as artificial intelligence, machine learning, blockchain, federated learning, etc. Finally, we discuss various challenges and issues of IoT such as architecture, integration, data provenance, and important applications such as cloud and edge computing, etc. This article will provide aid to the readers and other researchers in an understanding of the IoT’s next-generation developments and tell how they apply to the real world.

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Smart City Data Science: Towards data-driven smart cities with open research issues

Cited by 76 publications

References 70 publications

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

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

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

A Step toward Next-Generation Advancements in the Internet of Things Technologies

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