On the use of artificial intelligence to deal with privacy in IoT systems: A systematic literature review

Giordano, Giammaria; Palomba, Fabio; Ferrucci, Filomena

doi:10.1016/j.jss.2022.111475

Cited by 11 publications

(9 citation statements)

References 142 publications

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“…Cybersecurity applications can more effectively find helpful information or intelligence in system log data by clustering. Clustering algorithms may be very helpful in resolving IoT security issues, such as identifying outliers, anomalies, signatures, fraud, and cyberattacks, by exposing previously hidden patterns and structures in IoT security data [ 113 ].…”

Section: Resultsmentioning

confidence: 99%

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Mazhar

Talpur

Shloul³

et al. 2023

Brain Sciences

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The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website’s content as a technical resource and reference.

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Section: Resultsmentioning

confidence: 99%

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Mazhar

Talpur

Shloul³

et al. 2023

Brain Sciences

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“…al. [24] study investigates the current research on using AI to protect privacy in IoT systems. The review disscuss about important discoveries from past research, like how well AI can find privacy breaches and how privacy protection affects how well IoT systems work.…”

Section: Related Workmentioning

confidence: 99%

“…al. [24]. AI systems can study information and find patterns to give quick warnings about possible problems, so companies can act fast to fix them.…”

Section: Opportunitiesmentioning

confidence: 99%

A Systematic Literature Review: AI, DL and Machine Learning inCyber Risk Management

Hamid,

Rahman

2024

Preprint

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The integration of AI and ML in both climate change and cyber risk management has significantly bolstered their effectiveness. These advanced technologies enable organizations to handle extensive datasets, whether related to climate patterns or cyber activities, with greater efficiency and making more informed decisions. AI and machine learning are utilized in cyber security to predict potential threats, automate tasks, and manage cyber security risks. Ensuring the accuracy of information, comprehending the functionality of the model, and safeguarding individuals' data pose challenges. It is very important to use personal information ethically and protect it. Researchers should prioritize accuracy in the information utilized, clarity in AI models, and fairness in AI decision-making in the future. New technologies like edge computing, federated learning, explainable AI, and quantum computing are changing the way we manage risk and keep our digital information safe. These new advancements provide opportunities to gain a better understanding of risks, identify threats more rapidly, and improve decision-making. Still, there are challenges concerning data accuracy, understanding model operations, and privacy issues that must be resolved before these technologies can effectively address cyber risks.

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“…Moreover, IoT implementation in agricultural supply chains places a significant emphasis on security requirements due to the potential risks associated with cyber-attacks and data breaches [50,88]. Security concerns in the agricultural sector extend to three key aspects: authentication, privacy, and access control [49,89].…”

Section: Iot Deployment Requirementsmentioning

confidence: 99%

Modelling Factors Influencing IoT Adoption: With a Focus on Agricultural Logistics Operations

Rajabzadeh,

Fatorachian

2023

Smart Cities

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Purpose- In recent years, there has been a notable surge in the utilization of emerging technologies, notably the Internet of Things (IoT), within the realm of business operations. However, empirical evidence has underscored a disconcerting trend whereby a substantial majority, surpassing 70%, of IoT adoption initiatives falter when confronted with the rigors of real-world implementation. Given the profound implications of IoT in augmenting product quality, this study endeavors to scrutinize the extant body of knowledge concerning IoT integration within the domain of agricultural logistics operations. Furthermore, it aims to discern the pivotal determinants that exert influence over the successful assimilation of IoT within business operations, with particular emphasis on logistics. Design/Methodology/Approach- The research utilizes a thorough systematic review methodology coupled with a meta-synthesis approach. In order to identify and clarify the key factors that influence IoT implementation in logistics operations, the study is grounded in the Resource-Based View theory. It employs rigorous grounded theory coding procedures, supported by the analytical capabilities of MAXQDA software. Findings- The culmination of the meta-synthesis endeavor culminates in the conceptual representation of IoT adoption within the agricultural logistics domain. This representation is underpinned by the identification of three overarching macro categories/constructs, namely: (1) IoT Technology Adoption, encompassing facets such as IoT implementation requisites, ancillary technologies essential for IoT integration, impediments encountered in IoT implementation, and the multifaceted factors that influence IoT adoption; (2) IoT-Driven Logistics Management, encompassing IoT-based warehousing practices, governance-related considerations, and the environmental parameters entailed in IoT-enabled logistics; and (3) the Prospective Gains Encompassing IoT Deployment, incorporating the financial, economic, operational, and sociocultural ramifications ensuing from IoT integration. The findings underscore the imperative of comprehensively addressing these factors for the successful assimilation of IoT within agricultural logistics processes. Originality- The originality of this research study lies in its pioneering effort to proffer a conceptual framework that furnishes a comprehensive panorama of the determinants that underpin IoT adoption, thereby ensuring its efficacious implementation within the ambit of agricultural logistics operations. Practical Implications- The developed framework, by bestowing upon stakeholders an incisive comprehension of the multifaceted factors that steer IoT adoption, holds the potential to streamline the IoT integration process. Moreover, it affords an avenue for harnessing the full spectrum of IoT-derived benefits within the intricate milieu of agricultural logistics operations.

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On the use of artificial intelligence to deal with privacy in IoT systems: A systematic literature review

Cited by 11 publications

References 142 publications

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

A Systematic Literature Review: AI, DL and Machine Learning inCyber Risk Management

Modelling Factors Influencing IoT Adoption: With a Focus on Agricultural Logistics Operations

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