National Institute of Standards and Technology における超伝導研究及び生活

In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

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A review on genetic algorithm: past, present, and future

This book treats nonlinear dynamics in both Hamiltonian and dissipative systems. The emphasis is on the mechanics for generating chaotic motion, methods of calculating the transitions from regular to chaotic motion, and the dynamical and statistical properties of the dynamics when it is chaotic. The book is intended as a self consistent treatment of the subject at the graduate level and as a reference for scientists already working in the field. It emphasizes both methods of calculation and results. It is accessible to physicists and engineers without training in modern mathematics. The new edition brings the subject matter in a rapidly expanding field up to date, and has greatly expanded the treatment of dissipative dynamics to include most important subjects. It can be used as a graduate text for a two semester course covering both Hamiltonian and dissipative dynamics.

Regular and Chaotic Dynamics

A novel image encryption scheme based on substitution-permutation network and chaos

Communication systems: An introduction to signals and noise in electrical communication

https://doi.org/10.1016/j.dcan.2022.11.002

Monitoring the security of audio biomedical signals communications in wearable IoT healthcare

Contemporary healthcare has witnessed a wide deployment of Implantable Cardioverter Defibrillators (ICDs), which have the capability to be controlled remotely, making them equally accessible from both home and hospitals. The therapeutic benefits of ICDs seem to outweigh potential security concerns, yet overlooking the presence of malicious attacks cannot be justified. This study investigates the scenario where an adversary falsifies a controller command and sends instructions to issue high electric shocks in succession. We propose a novel security hardening mechanism to protect data communications between ICD and controller from malicious data manipulations. Our proposed method verifies the correctness of an external command with respect to the history of heart rhythms. The proposed method is evaluated using real data. Multi-aspect analyses show the effectiveness of the proposed scheme.

/pdf/security-hardening-of-implantable-cardioverter-iyb77kbbve.pdf

Security Hardening of Implantable Cardioverter Defibrillators

The current ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus, has severely affected our daily life routines and behavior patterns. According to the World Health Organization, there have been 93 million confirmed cases with more than 1.99 million confirmed death around 235 Countries, areas or territories until 15 January 2021, 11:00 GMT+11. People who are affected with COVID-19 have different symptoms from people to people. When large amounts of patients are affected with COVID-19, it is important to quickly identify the health conditions of patients based on the basic information and symptoms of patients. Then the hospital can arrange reasonable medical resources for different patients. However, existing work has a low recall of 15.7% for survival predictions based on the basic information of patients (i.e., false positive rate (FPR) with 84.3%, FPR: actually survival but predicted as died). There is much room for improvement when using machine learning-based techniques for COVID-19 prediction. In this paper, we propose DeCoP to train a classifier to predict the survival of COVID-19 patients with high recall and F1 score. DeCoP is a deep learning (DL)-based scheme of Bidirectional Long Short-Term Memory (BiLSTM) along with Fuzzy-based Information Decomposition (FID) to predict the survival of patients. First of all, we apply FID oversampling to redistribute the training data of the Open COVID-19 Data Working Group. Then, we employ BiLSTM to learn the high-level feature representations from the redistributed dataset. After that, the high-level feature vector will be used to train the prediction model. Experimental results show that our proposed scheme achieves outstanding performances. Precisely, the improvement achieves about 19% and 18% in terms of recall and F1-measure.

DeCoP: Deep Learning for COVID-19 Prediction of Survival

Technology advances—such as improving processing power, battery life, and communication functionalities—contribute to making mobile devices an attractive research area. In 2008, in order to manage mobility, the Internet Engineering Task Force (IETF) developed Proxy Mobile IPv6, which is a network-based mobility management protocol to support seamless connectivity of mobile devices. This protocol can play a key role in volunteer computing paradigms as a user can seamlessly access computing resources. The procedure of user authentication is not defined in this standard; thus, many studies have been carried out to propose suitable authentication schemes. However, in the current authentication methods, with reduced latency and packet loss, some security and privacy considerations are neglected. In this study, we propose a secure and anonymous ticket-based authentication (SATA) method to protect mobile nodes against existing security and privacy issues. The proposed method reduces the overhead of handover authentication procedures using the ticket-based concept. We evaluated security and privacy strengths of the proposed method using security theorems and BAN logic.

A Secure Ticket-Based Authentication Mechanism for Proxy Mobile IPv6 Networks in Volunteer Computing

IoT devices usually have resource limitations and are prone to compromise. They are not well equipped with extra controls to promote their security. Green IoT (GIoT) devices are even more conservative in spending their resources. An effective way of promoting GIoT security at a low cost is removing program vulnerabilities. Fuzzing is a promising technique that exposes bugs in IoT programs. To evaluate a fuzzer performance, we make it run the test programs from some corpora to show its ability in detecting bugs. However, the contexts of bugs are not provided by the existing corpora, which may mislead the evaluation. Due to the complex logic of real-world programs, especially the GIoT ones, it is hard to obtain the bugs context. In this research, we propose to synthesize programs based on road-rock features that protect bugs from being exposed. Accordingly, we design a framework to generate a corpora that provide us with the contexts of bugs for more comprehensive fuzzing evaluation. As a case study, we evaluate AFL and AFLFast by using the synthesized corpora. The results show that AFLFast has the weakness of cycle explosion , which prevents fuzzing from examining more test cases. We developed AFLFast+ to overcome this issue.

Alireza Jolfaei

Papers

Monitoring the security of audio biomedical signals communications in wearable IoT healthcare

Security Hardening of Implantable Cardioverter Defibrillators

DeCoP: Deep Learning for COVID-19 Prediction of Survival

A Secure Ticket-Based Authentication Mechanism for Proxy Mobile IPv6 Networks in Volunteer Computing

Synthesized Corpora to Evaluate Fuzzing for Green Internet of Things Programs