AIMC Topic: Computer Security

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Fused federated learning framework for secure and decentralized patient monitoring in healthcare 5.0 using IoMT.

Scientific reports
Federated Learning (FL) enables artificial intelligence frameworks to train on private information without compromising privacy, which is especially useful in the medical and healthcare industries where the knowledge or data at hand is never enough. ...

Double reinforcement learning for cluster synchronization of Boolean control networks under denial of service attacks.

PloS one
This paper investigates the asymptotic cluster synchronization of Boolean control networks (BCNs) under denial-of-service (DoS) attacks, where each state node in the network experiences random data loss following a Bernoulli distribution. First, the ...

False-positive tolerant model misconduct mitigation in distributed federated learning on electronic health record data across clinical institutions.

Scientific reports
As collaborative Machine Learning on cross-institutional, fully distributed networks become an important tool in predictive health modeling, its inherent security risks must be addressed. One among such risks is the lack of a mitigation strategy agai...

A deep dive into artificial intelligence with enhanced optimization-based security breach detection in internet of health things enabled smart city environment.

Scientific reports
Internet of Health Things (IoHT) plays a vital role in everyday routine by giving electronic healthcare services and the ability to improve patient care quality. IoHT applications and devices become widely susceptible to cyber-attacks as the tools ar...

Enhanced security for medical images using a new 5D hyper chaotic map and deep learning based segmentation.

Scientific reports
Medical image encryption is important for maintaining the confidentiality of sensitive medical data and protecting patient privacy. Contemporary healthcare systems store significant patient data in text and graphic form. This research proposes a New ...

An explainable federated blockchain framework with privacy-preserving AI optimization for securing healthcare data.

Scientific reports
With the rapid growth of healthcare data and the need for secure, interpretable, and decentralized machine learning systems, Federated Learning (FL) has emerged as a promising solution. However, FL models often face challenges regarding privacy prese...

XAI-XGBoost: an innovative explainable intrusion detection approach for securing internet of medical things systems.

Scientific reports
The Internet of Medical Things (IoMT) has transformed healthcare delivery but faces critical challenges, including cybersecurity threats that endanger patient safety and data integrity. Intrusion Detection Systems (IDS) are essential for protecting I...

Design of Block-Scrambling-Based privacy protection mechanism in healthcare using fusion of transfer learning models with Hippopotamus optimization algorithm.

Scientific reports
In the human body, the skin is the main organ. Nearly 30-70% of individuals globally have skin-related health issues, for whom efficient and effective analysis is essential. A general method dermatologists use for analyzing skin illnesses is dermosco...

Blockchain-aided comparative study of heart disease detection using machine learning-based approaches with an expanded dataset.

Computers in biology and medicine
Heart disease, also known as cardiovascular disease (CVD), is a diverse set of conditions that disrupt the normal functioning of the cardiovascular system by narrowing the coronary arteries. These arteries are used for blood circulation and the deliv...

Adaptive DDoS detection mode in software-defined SIP-VoIP using transfer learning with boosted meta-learner.

PloS one
The Internet has continued to provision its infrastructure as a platform for competitive marketing, enhanced productivity, and monetization efficacy. However, it has become a means for adversaries to exploit unsuspecting users and, in turn, compromis...