An approach towards preventing iot based sybil attack based on contiki framework through cooja simulator

The emergence of the Internet of Things (IoT) technology has brought about tremendous possibilities, but at the same time, it has opened up new vulnerabilities and attack vectors that could compromise the confidentiality, integrity, and availability of connected systems. Developing a secure IoT ecosystem is a daunting challenge that requires a systematic and holistic approach to identify and mitigate potential security threats. Cybersecurity research considerations play a critical role in this regard, as they provide the foundation for designing and implementing security measures that can address emerging risks. To achieve a secure IoT ecosystem, scientists and engineers must first define rigorous security specifications that serve as the foundation for developing secure devices, chipsets, and networks. Developing such specifications requires an interdisciplinary approach that involves multiple stakeholders, including cybersecurity experts, network architects, system designers, and domain experts. The primary challenge in IoT security is ensuring the system can defend against both known and unknown attacks. To date, the IoT research community has identified several key security concerns related to the architecture of IoT systems. These concerns include issues related to connectivity, communication, and management protocols. This research paper provides an all-inclusive and lucid review of the current state of anomalies and security concepts related to the IoT. We classify and analyze prevalent security distresses regarding IoT’s layered architecture, including connectivity, communication, and management protocols. We establish the foundation of IoT security by examining the current attacks, threats, and cutting-edge solutions. Furthermore, we set security goals that will serve as the benchmark for assessing whether a solution satisfies the specific IoT use cases.

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“…Sybil Attack [88][89][90] During a Sybil attack, the attacker mimics the identities of several different targets.…”

Section: Dos Attack [85-87]mentioning

confidence: 99%

A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review

Tariq

Ahmed

Bashir

et al. 2023

Sensors

114

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“…Real-time data analysis using algorithms and tools is prevalent. [41]. Previously, computational approaches were largely used in online ECG signal processing.…”

Section: Discussionmentioning

confidence: 99%

“…8). Random forest models scale effectively to big datasets with many independent variables [42]. Therefore, each sample had an average of 25 independent variables.…”

Section: Discussionmentioning

confidence: 99%

Electrocardiogram Feature Based Heart Arrhythmia Detection Using Machine Learning and Apache Spark

Singhal

Yadav

2023

Preprint

Self Cite

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Heart arrhythmias are the main cause of death worldwide. Electrocardiogram (ECG) results can be used to identify arrhythmias, or irregularities in the heart's rhythm. Because symptoms are not always present, the diagnosis is often off. To prevent a potentially catastrophic situation, patients using real-time ECG monitoring must identify arrhythmias early on. In this work, Structured Streaming, an open-source Apache Spark technology, was used. Finding a method to apply machine learning to detect cardiac arrhythmias in real-time is the goal of the project. Investigating how structured streaming affects metrics for content classification and how long it takes to find episodes was another goal. At MIT and BIH, we have been gathering ECG information. With this information, arrhythmias like RBBB and atrial fibrillation might be recognised and categorised. There are many methods for separating these erratic rhythms from one another. We used a multiclass classifier based on logistic regression, a random forest, and three different decision trees to categorise the data. The random forest classifier wins out when the three classification methods are compared. In comparison to other studies, this study demonstrated improved classification model performance metrics and decreased pipeline runtime.

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“…The use of blockchain is another tool that can be used to improve privacy when exchanging healthcare data. With the support of this system, since they are extracted from the blockchain network, healthcare organizations will not have to worry about the reliability of the Journal of Informatics Electrical and Electronics Engineering (JIEEE) A2Z Journals results, and therefore the consumers themselves will be able to handle them [54][55][56][57][58][59][60][61][62]. The multimedia data (e.g., images, sounds, and videos) is one form of healthcare data.…”

Section: Privacy and Security In The Healthcare Blockchainmentioning

confidence: 99%

The Impact of Blockchain on the Healthcare Environment

Praveen¹

2021

J. Infor. Electr. Electron. Eng.

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Bitcoin was the first electronic payment system to truly exploit the power of blockchain technology. There is currently the problem of health information inequality and health information leakage. Physicians should conduct essential routine work that wastes human and financial resources and delays treatment processes. Blockchain provides a trust-free and cost-reducing solution to manage and secure valuable health information. The aim of this study is to discuss research into blockchain healthcare applications. It addresses the management of medical data, as well as the sharing of medical information, the sharing of images, and the management of logs. We also discuss papers that overlap with other fields, such as the Internet of Things, information management, drug monitoring along their supply chain, and aspects of security and privacy. Finally, we analyze and compare the research papers in the medical area and also summarize the strategies used in healthcare with their pros and cons.

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An approach towards preventing iot based sybil attack based on contiki framework through cooja simulator

Cited by 10 publications

References 19 publications

A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review

A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review

Electrocardiogram Feature Based Heart Arrhythmia Detection Using Machine Learning and Apache Spark

The Impact of Blockchain on the Healthcare Environment

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