RETRACTED ARTICLE: IoT-based smart environment using intelligent intrusion detection system

Kalnoor, Gauri; Gowrishankar, S.

doi:10.1007/s00500-021-06028-1

Cited by 25 publications

(8 citation statements)

References 35 publications

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“…e paper introduces the concept of credibility, quantifies the ability of security elements to influence each other, calculates the capability index, opportunity index, and intention index separately according to the influencing factors they are subject to, and finally integrates the above three indices to arrive at the overall network security posture value [9]. Given the complexity of the network in the era of big data, the speed, volume, and structure of the data generated make it difficult for the traditional network security situational awareness methods to effectively address this problem.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Network Security Measurement and Optimal Proactive Defense in Cloud Computing Environments

Xing

Zhang

2022

Security and Communication Networks

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This paper presents an in-depth study and analysis of hierarchical network security measurement and optimal active defense in the cloud computing environment. All the cloud platform security-related data collected through cloud platform monitoring is collected, and then the relevant security data is summarized and analyzed, so that the specific security posture index of the cloud platform can be derived, thus providing a reference for cloud platform managers to judge the security risks of the cloud platform. It provides a reference for cloud platform managers to judge the security risks of cloud platforms. Through the cloud platform security situation awareness system, we mainly study the construction of cloud platform, the construction of security situation awareness system, and the calculation of security situation value and use, thus greatly improving the stability, security, and reliability of the cloud platform. The application of the method avoids the drawbacks of traditional network security management, which relies entirely on past data and cannot sense changes in the security state of the system in real time. At the same time, the predicted results are added to the input of the fuzzy decision-making system, improving the accuracy of the assessment. The method improves the real-time and effectiveness of network security posture prediction, increases the convergence speed and prediction accuracy of the algorithm, and avoids the occurrence of overfitting. Simulation experiments based on the internet network security posture dataset show that this research method has less prediction error than the traditional machine learning methods and other deep learning methods, has higher learning efficiency, and is more rapid, accurate, and effective in predicting the trend of network security posture in the big data environment in the future period.

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Section: Related Workmentioning

confidence: 99%

Hierarchical Network Security Measurement and Optimal Proactive Defense in Cloud Computing Environments

Xing

Zhang

2022

Security and Communication Networks

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“…It proposes an energy-efficient model for IoT using compressive sensing, which is a technique that reduces the amount of data collected and transmitted by IoT devices. Many works are carried out in intrusion detection and their primary objective is the early detection and identification of any aberrant or suspicious behaviour, serving as crucial indicators of potential security breaches within the ecosystem [4]. The significance of IDSs lies in their proactive stance, enabling prompt responses to potential threats.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Feature Selection in Intrusion Detection Using Fisher Score Algorithm: An Analytical Study

Rathod,

Sabnis,

Jain

2024

Preprint

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Traditional intrusion detection methods often fail to meet the unique security requirements of IoT applications, raising serious security concerns due to the rapid expansion of the Internet of Things (IoT) -connectivity and its extensive property, complex structure, and complexity. Machine learning algorithms for intrusion detection have emerged as a solution, offering promise in protecting these complex systems. This paper examines a comprehensive analysis of an Intrusion Detection System (IDS) in an IoT system. Recognizing the important role of feature extraction in IDS, this study aims to help researchers by providing insights on data set selection and confirming the effectiveness of the Fisher Score algorithm. Through a careful comparative analysis using established selection methods—Mutual Information, Chi-Square, Principal Component Analysis (PCA), and Recursive Feature Elimination (RFE)— this study seeks to help researchers choose the most appropriate feature extraction technique in intrusion detection within the IoT framework. Using logistic regression as a classification model, this study allows a thorough analysis and comparison of different selection methods. The results highlight the importance and accuracy of the Fisher Score algorithm in selecting key features for Intrusion Detection in IoT systems. It is worth mentioning, that this research is limited to a specific dataset, NBaIot, which is considered the best and most up-to-date for Intrusion Detection. The specificity observed in this study is dependent on the number of items and data sets. Although the findings may be variable, the selected sets and item sizes have informed the identification of interventions in this study that are deemed optimal.

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“…Finally, any IoT system needs to be secure (unauthorized persons). For that, authors in [10] propose an intrusion detection system for the smart IoT environment. A system was developed to remotely manage IoT devices using 3G connectivity technology in [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Adopting the Internet of Things Technology to Remotely Monitor COVID-19 Patients

Saidi

Kacem

Tounsi

et al. 2022

Lecture Notes in Computer Science

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The coronavirus known as COVID-19 is the topic of the hour all over the world. This virus has invaded the world with its invariants, which are characterized by their rapid spread. COVID-19 has impacted the health of people and the economy of countries. For that, laboratories, researchers, and doctors are in a race against time to find a cure for this pandemic. To combat this virus, cutting-edge technologies such as artificial intelligence, cloud computing, and big data have been put in place. In our work, we use Internet of Things (IoT) technology. The use of IoT in an efficient way can lead to detecting infected people and avoiding being contaminated. In this paper, we are interested in the remote medical monitoring of patients who have tested positive for COVID-19. We propose a meta-modeling technique to model the IoT architecture. Then we implement two IoT solutions that permit the remote medical monitoring of patients infected with COVID-19 and the respect of social distancing by instantiating correct models that conform to the proposed meta-model in order to mitigate the COVID-19 outbreak.

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RETRACTED ARTICLE: IoT-based smart environment using intelligent intrusion detection system

Cited by 25 publications

References 35 publications

Hierarchical Network Security Measurement and Optimal Proactive Defense in Cloud Computing Environments

Hierarchical Network Security Measurement and Optimal Proactive Defense in Cloud Computing Environments

Optimizing Feature Selection in Intrusion Detection Using Fisher Score Algorithm: An Analytical Study

Adopting the Internet of Things Technology to Remotely Monitor COVID-19 Patients

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