AESRSA: A New Cryptography Key for Electronic Health Record Security

Ajagbe, Sunday Adeola; Flórez, Héctor; Awotunde, Joseph Bamidele

doi:10.1007/978-3-031-19647-8_17

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…The algorithms developed in [28] can be extended and applied to the presented problem. The algorithms of the parallel machine problem [29][30][31][32] can be extended and reformulated to use the related algorithms for the studied problem.…”

Section: Related Workmentioning

confidence: 99%

An enhanced multilevel secure data dissemination approximate solution for future networks

Otoom,

Jemmali,

Sarhan

et al. 2024

PLoS ONE

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Sensitive data, such as financial, personal, or classified governmental information, must be protected throughout its cycle. This paper studies the problem of safeguarding transmitted data based on data categorization techniques. This research aims to use a novel routine as a new meta-heuristic to enhance a novel data categorization based-traffic classification technique where private data is classified into multiple confidential levels. As a result, two packets belonging to the same confidentiality level cannot be transmitted through two routers simultaneously, ensuring a high data protection level. Such a problem is determined by a non-deterministic polynomial-time hardness (NP-hard) problem; therefore, a scheduling algorithm is applied to minimize the total transmission time over the two considered routers. To measure the proposed scheme’s performance, two types of distribution, uniform and binomial distributions used to generate packets transmission time datasets. The experimental result shows that the most efficient algorithm is the Best-Random Algorithm (B R ˜), recording 0.028 s with an average gap of less than 0.001 in 95.1% of cases compared to all proposed algorithms. In addition, B R ˜ is compared to the best-proposed algorithm in the literature which is the Modified decreasing Estimated-Transmission Time algorithm (MDETA). The results show that B R ˜ is the best one in 100% of cases where MDETA reaches the best results in only 48%.

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Section: Related Workmentioning

confidence: 99%

An enhanced multilevel secure data dissemination approximate solution for future networks

Otoom,

Jemmali,

Sarhan

et al. 2024

PLoS ONE

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“…The multi-camera model of video surveillance was so well-designed that it can handle all three key tasks for normal police "stake-out", namely detection, representation, and recognition [35]- [37]. The detecting section combines video feed from numerous cameras to extract motion trajectories from videos quickly and accurately.…”

Section: Crime Prediction Approaches (Cv ML Dl)mentioning

confidence: 99%

Techniques for predicting dark web events focused on the delivery of illicit products and ordered crime

Rawat

Oki

Sankaran

et al. 2023

IJECE

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Malicious actors, specially trained professionals operating anonymously on the dark web (DW) platform to conduct cyber fraud, illegal drug supply, online kidnapping orders, CryptoLocker induction, contract hacking, terrorist recruitment portals on the online social network (OSN) platform, and financing are always a possibility in the hyperspace. The amount and variety of unlawful actions are increasing, which has prompted law enforcement (LE) agencies to develop efficient prevention tactics. In the current atmosphere of rapidly expanding cybercrime, conventional crime-solving methods are unable to produce results due to their slowness and inefficiency. The methods for accurately predicting crime before it happens "automated machine" to help police officers ease the burden on personnel while also assisting in preventing offense. To achieve and explain the results of a few cases in which such approaches were applied, we advise combining machine learning (ML) with computer vision (CV) strategies. This study's objective is to present dark web crime statistics and a forecasting model for generating alerts of illegal operations like drug supply, people smuggling, terrorist staffing and radicalization, and deceitful activities that are connected to gangs or organizations showing online presence using ML and CV to help law enforcement organizations identify, and accumulate proactive tactics for solving crimes.

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