Blockchain Assisted Intrusion Detection System Using Differential Flower Pollination Model

Ahmed, Mohammed Altaf; Althubiti, Sara A.; Rao, D. N.; Lydia, E. Laxmi; Cho, Woong; Joshi, Gyanendra Prasad; Kim, Sung Won

doi:10.32604/cmc.2022.032083

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Lastly, sailfish optimizer (SFO) with RBM technique is utilized for effective identification of intrusions. In [12], the authors suggested BC deep-rooted Bi-level ID and graph-based mitigation model called Hybrid-Chain-IDS. Primarily, time-based authentication to validate the legal users utilizing the NIK512 hashing procedure is performed.…”

Section: Related Workmentioning

confidence: 99%

Blockchain Assisted Fireworks Optimization with Machine Learning based Intrusion Detection System (IDS)

2024

Teh. vjesn.

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In order to cope with the growing complexity of cyber attacks, it is imperative to have efficient intrusion detection systems (IDSs) that can monitor computer resources and produce data on abnormal or suspicious activities. The security of IoT networks is increasingly becoming a crucial concern as the Internet of Things (IoT) technology receives widespread use. Protecting the IoT framework with a conventional Intrusion Detection System (IDS) might be challenging due to the vast quantity and diversity of IoT devices. Traditional Intrusion Detection Systems (IDSs) face limitations when deployed in IoT networks due to resource limitations and the inherent complexity of these networks.This research proposed the Blockchain Assisted Fireworks Optimization with Machine Learning based Intrusion Detection System (BAFWO-MLIDS) technique in the healthcare platform. The major purpose of the BAFWO-MLIDS system is to apply BC technology (BCT) with IDS for enhanced security in the healthcare sector. The BCT enables to achieve secure data transmission in the healthcare platform. The BCT enables to achieve secure data broadcast in the healthcare environment. The BAFWO-MLIDS technique involves a three-stage procedure: FWO based FS process, ENN-based detection, and BO-based parameter optimization. In the proposed BAFWO-MLIDS technique, the FWO-based feature selection process is involved to select optimal features. For intrusion detection, the BAFWO-MLIDS technique uses Elman Neural Network (ENN) model. Finally, the Bayesian optimization (BO) technique is applied to modify the parameters compared with the ENN model and thereby it accomplishes enhanced detection performance. The simulation results of the BAFWO-MLIDS system can be inspected in a series of experiments and the obtained results ensured greater efficiency of the BAFWO-MLIDS methodology with other recent algorithms.

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Section: Related Workmentioning

confidence: 99%

Blockchain Assisted Fireworks Optimization with Machine Learning based Intrusion Detection System (IDS)

2024

Teh. vjesn.

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Tubulointerstitial Nephritis and Uveitis Syndrome During the COVID-19 Pandemic: A Case Series

Özdemir Yalçınsoy,

Güngör,

Karakaya

et al. 2024

tjo

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Objectives: To report the ocular findings, laboratory results, and management of patients with tubulointerstitial nephritis and uveitis syndrome (TINU), whose numbers increased during the 2019 coronavirus disease (COVID-19) pandemic. Materials and Methods: Demographic characteristics, ophthalmic examination findings, laboratory results including polymerase chain reaction (PCR) test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), serum SARS-CoV-2 immunoglobulin G (IgG) antibody, and treatment of patients diagnosed with TINU between March 2020 and March 2022 were evaluated retrospectively. Results: The study included 19 eyes of 10 patients (6 female/4 male). The mean age was 13.5±2.4 years (range: 8-16 years). The mean follow-up duration was 13.5±6.1 months (range: 6-24 months). All patients presented with anterior uveitis. Anterior uveitis was bilateral in 9 patients (90%) and unilateral in 1 patient (10%). Posterior segment findings were normal in 8 patients (80%), and bilateral optic disc edema was observed in only 2 patients (20%). None of the patients had a previous SARS-CoV-2 infection and/or vaccination history. The SARS-CoV-2 PCR test was negative in all patients at presentation. The SARS-CoV-2 IgG antibody test was reactive in 7 patients (70%). Recurrent uveitis developed in 8 patients (80%) during follow-up. Systemic immunomodulatory therapy was required for the control of ocular inflammation in 7 patients (70%) with severe uveitis flare-ups. Conclusion: TINU is a multisystemic autoimmune disease, especially in response to environmental triggering factors such as viral infections. Although TINU is a rare disease, the number of cases increased during the COVID-19 pandemic. SARS-CoV-2 antibodies were detected at a significant rate of 70% in these patients, who did not have a history of SARS-CoV-2 infection and vaccination. Previous asymptomatic SARS-CoV-2 infection in children may be a triggering factor in the development of TINU.

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Blockchain Assisted Intrusion Detection System Using Differential Flower Pollination Model

Cited by 2 publications

References 24 publications

Blockchain Assisted Fireworks Optimization with Machine Learning based Intrusion Detection System (IDS)

Blockchain Assisted Fireworks Optimization with Machine Learning based Intrusion Detection System (IDS)

Tubulointerstitial Nephritis and Uveitis Syndrome During the COVID-19 Pandemic: A Case Series

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