Detecting Man-in-the-Middle Attack in Fog Computing for Social Media

Aliyu, Farouq; Sheltami, Tarek R.; Mahmoud, Ashraf; Al-Awami, Louai; Yasar, Ansar-Ul-Haque

doi:10.32604/cmc.2021.016938

Cited by 11 publications

(4 citation statements)

References 51 publications

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“…Virtual technologies like fog computing and edge computing are prone to security and privacy challenges (Aliyu et al, 2021). Hathaliya et al (2019) proposed a Permissioned blockchain-based healthcare architecture to overcome these challenges.…”

Section: Blockchain Monitoringmentioning

confidence: 99%

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Shaik

Tao

Higgins

et al. 2023

WIREs Data Min & Knowl

142

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The adoption of artificial intelligence (AI) in healthcare is growing rapidly.Remote patient monitoring (RPM) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in-home care, and even hospitalized patients. The reliability of manual patient monitoring systems depends on staff time management which is dependent on their workload. Conventional patient monitoring involves invasive approaches which require skin contact to monitor health status. This study aims to do a comprehensive review of RPM systems including adopted advanced technologies, AI impact on RPM, challenges and trends in AI-enabled RPM. This review explores the benefits and challenges of patient-centric RPM architectures enabled with Internet of Things wearable devices and sensors using the cloud, fog, edge, and blockchain technologies. The role of AI in RPM ranges from physical activity classification to chronic disease monitoring and vital signs monitoring in emergency settings. This review results show that AI-enabled RPM architectures have transformed healthcare monitoring applications because of their ability to detect early deterioration in patients' health, personalize individual patient health parameter monitoring using federated learning, and learn human behavior patterns using techniques such as reinforcement learning. This review discusses the challenges and trends to adopt AI to RPM systems and implementation issues. The future directions of AI in RPM applications are analyzed based on the challenges and trends.

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Section: Blockchain Monitoringmentioning

confidence: 99%

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Shaik

Tao

Higgins

et al. 2023

WIREs Data Min & Knowl

142

View full text Add to dashboard Cite

show abstract

“…An attacker controls the communication messages; he or she intercepts the messages communicated between the user and the server. 87,88 Then the attacker interjects other information or steals the secure information from that message. Conti et al 89 in their study about man-in-the-middle attacks discussed that this type of attack is vulnerable and compromises integrity, availability, and confidentiality.…”

Section: Manipulation Related Attacksmentioning

confidence: 99%

“…This type of attack is mainly initiated for hacking public keys between sender and receiver. An attacker controls the communication messages; he or she intercepts the messages communicated between the user and the server 87,88 . Then the attacker interjects other information or steals the secure information from that message.…”

Section: Privacy and Security Threats In Osnsmentioning

confidence: 99%

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

Bhattacharya

Roy

Chattopadhyay

et al. 2022

Security and Privacy

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Over the past few years, online social networks (OSNs) have become an inseparable part of people's daily lives. Instead of being passive readers, people are now enjoying their role as content contributors. OSN has permitted its users to share their information including the multimedia content. OSN users can express themselves in virtual communities by providing their opinions and interacting with others. As a consequence, the privacy and security threats in OSNs have emerged as a major concern to the research and business world. In the recent past, a number of survey works have been conducted to discuss different security and privacy threats in OSNs. However, till date, no survey work has been conducted that aims to classify and analyze various machine learning (ML)‐based solutions adapted for the security defense of OSNs. In this survey article, we present a detailed taxonomy with a classification of various works done on various security attacks in OSNs. We then review and summarize the existing state of art survey works on OSN security, and indicate the merits and limitations of these survey works. Next, we review all recent works that aim to provide ML‐based solutions toward defense of security attacks on OSNs. Finally, we discuss the future road‐map on OSN security and provide a comprehensive analysis on the open research issues with feasible measurements and possible solutions.

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“…However, it can create several challenges as well for ensuring delay-less requirements. The author of [44] proposes an Intrusion Detection and Prevention (IDPS) mechanism to deal with the man-in-the-middle attacks in the fog network. This mechanism consists of IDS nodes that detect any anomaly happening in the network by monitoring it after regular time intervals.…”

mentioning

confidence: 99%

An Overview of Fog Computing and Edge Computing Security and Privacy Issues

Alwakeel

2021

Sensors

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With the advancement of different technologies such as 5G networks and IoT the use of different cloud computing technologies became essential. Cloud computing allowed intensive data processing and warehousing solution. Two different new cloud technologies that inherit some of the traditional cloud computing paradigm are fog computing and edge computing that is aims to simplify some of the complexity of cloud computing and leverage the computing capabilities within the local network in order to preform computation tasks rather than carrying it to the cloud. This makes this technology fits with the properties of IoT systems. However, using such technology introduces several new security and privacy challenges that could be huge obstacle against implementing these technologies. In this paper, we survey some of the main security and privacy challenges that faces fog and edge computing illustrating how these security issues could affect the work and implementation of edge and fog computing. Moreover, we present several countermeasures to mitigate the effect of these security issues.

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Detecting Man-in-the-Middle Attack in Fog Computing for Social Media

Cited by 11 publications

References 51 publications

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

An Overview of Fog Computing and Edge Computing Security and Privacy Issues

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