Sequential fusion to defend against sensing data falsification attack for cognitive Internet of Things

Wu, Jun; Yu, Yue; Song, Tiecheng; Hu, Jing

doi:10.4218/etrij.2019-0388

Cited by 14 publications

(5 citation statements)

References 36 publications

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“…The authors of [28] have suggested a model of complex attack for falsifying sensing data. Afterward, they proposed a weighted sequential hypothesis test (WSHT) to enhance the detection accuracy of the prime user to reduce the sampling number, which involves a status-trust evaluation mechanism of data transmission to ensure the availability of sensing data as well as a test of sequential hypothesis.…”

Section: Related Workmentioning

confidence: 99%

A Smart Framework to Detect Threats and Protect Data of IoT based on Machine Learning

M. Almasabi,

Khemakhem,

Harbaoui

et al. 2023

Preprint

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The Internet of Things (IoT) has witnessed rapid and widespread adoption across various domains, including transportation, healthcare, education, agriculture, urban planning, smart homes, and more. Despite its transformative potential, this pervasive deployment of IoT devices has introduced new challenges, particularly concerning security and privacy threats such as unauthorized data access and device breaches. The excessive usage of these technological devices, coupled with the absence of robust security and privacy systems for user data, calls for a comprehensive approach to address these issues. In this study, we propose a novel framework designed to analyze, audit, test, and detect potential vulnerabilities within IoT environments and applications. The central components of the proposed framework include a machine learning algorithm for data classification and attack detection, along with the integration of Blockchain technology to enhance security measures. Specifically, the framework performs an in-depth analysis of user data to identify potential security or privacy vulnerabilities. Additionally, it conducts rigorous testing of smart services and automated data-collecting devices. To evaluate the effectiveness of our classification algorithm, we conducted a comprehensive implementation on a real-world IoT dataset. The results showcased the efficiency and accuracy of our approach in detecting and mitigating potential threats. Furthermore, based on our research findings, we provide valuable recommendations for enhancing security and privacy in IoT ecosystems. We also highlight emerging trends in the security and privacy domains, which can serve as valuable insights for researchers and practitioners. In conclusion, our proposed framework offers a robust and proactive approach to address the security and privacy challenges, such as unauthorized data access and device breaches posed by the widespread adoption of IoT devices. By combining machine learning algorithms and Blockchain technology, we contribute to safeguarding user data and fostering a secure environment for IoT applications. This study lays the groundwork for further advancements in the realm of IoT security and privacy, ensuring a safer and more resilient IoT landscape for the future.

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Section: Related Workmentioning

confidence: 99%

A Smart Framework to Detect Threats and Protect Data of IoT based on Machine Learning

M. Almasabi,

Khemakhem,

Harbaoui

et al. 2023

Preprint

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“…Smart home devices communicate with application server in order to achieve services. Attacker could compromise the gateway routing table and could collect packets that will permit them to get confidential information [87]. However, SSL (secure socket layer) technique [88] is used, an attacker can bypass the forged certificate.…”

Section: Falsificationmentioning

confidence: 99%

A Multicriteria Decision Making Taxonomy of IOT Security Challenging Factors

Akbar¹,

Alsanad

Mahmood

et al. 2021

IEEE Access

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Internet of things (IoT) is leading a new digital age. IoT is regarded as the significant frontier that can improve almost all aspect of our lives. Currently, the IoT technology faces several challenges to academic researchers and industry practitioners, mainly that related with security of data. The objective of this study is to develop a prioritization-based taxonomy of the challenging factors that could hinders the security of IoT. By conducting the literature review and questionnaire survey studies 21 challenging factors were identified that are reported in existing literature and in real-world practices. Moreover, the identified challenging factors are mapped in the core domain of IoT (i.e. smart city, smart home, smart wearable's and smart health care); and apply the fuzzy-AHP approach to rank the identified challenging factors with respect to their criticality for security of IoT technology. The application of fuzzy-AHP is novel in this research area as it is successfully applied in other domains of information technology to address the multi-criterion decision making problems. This study is contributing by providing a prioritization-based taxonomy of the IoT security challenging factors that could help the practitioners and research community to revise and develop the new strategies for the secure IoT.

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“…Such methods have been extensively employed for attack detection, for example: In [3] such an algorithm is used to detect a Link flooding attack on an IoT Network, in [4] Hypothesis Testing is used against spectrum sensing data falsification in cognitive IoT Networks while in [5] Li et. al use it to empower an distributed attack detection System.…”

Section: A Statistical Hypothesis Testing In the Context Of Iot Networkmentioning

confidence: 99%

A Hypothesis Testing tool for the comparison of different Cyber-Security Mitigation Strategies in IoT

Mpatziakas

Papadopoulos

Drosou

et al. 2021

2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)

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Internet of Things (IoT) is a field with tremendous growth that already shows great impact in numerous domains. Simultaneous with this development is the need for better Cyber-security: IoT systems are attacked by various adversaries targeting IoT services, platforms and networks, which can have disruptive consequences. These attacks can be countered using multiple strategies with different effects to the system. The following paper, proposes a novel approach based on Machine Learning and Statistical Hypothesis Testing, which allows the security operator to investigate how using different strategies affects various KPI related to the security of the IoT network and if the KPI resulting from modifications to a mitigation strategy are statistically different when compared to those occurring from a starting mitigation action set.

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Sequential fusion to defend against sensing data falsification attack for cognitive Internet of Things

Cited by 14 publications

References 36 publications

A Smart Framework to Detect Threats and Protect Data of IoT based on Machine Learning

A Smart Framework to Detect Threats and Protect Data of IoT based on Machine Learning

A Multicriteria Decision Making Taxonomy of IOT Security Challenging Factors

A Hypothesis Testing tool for the comparison of different Cyber-Security Mitigation Strategies in IoT

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