scholarly journals Secure Continuous-Variable Quantum Key Distribution with Machine Learning

Photonics ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 511
Author(s):  
Duan Huang ◽  
Susu Liu ◽  
Ling Zhang
Keyword(s):  
Machine Learning ◽  
Quantum Key Distribution ◽  
Key Distribution ◽  
Continuous Variable ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The Common ◽  
Practical Security

Quantum key distribution (QKD) offers information-theoretical security, while real systems are thought not to promise practical security effectively. In the practical continuous-variable (CV) QKD system, the deviations between realistic devices and idealized models might introduce vulnerabilities for eavesdroppers and stressors for two parties. However, the common quantum hacking strategies and countermeasures inevitably increase the complexity of practical CV systems. Machine-learning techniques are utilized to explore how to perceive practical imperfections. Here, we review recent works on secure CVQKD systems with machine learning, where the methods for detections and attacks were studied.

scholarly journals Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway Scenarios

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 136994-137004
Author(s):  
Hasan Abbas Al-Mohammed ◽  
Afnan Al-Ali ◽  
Elias Yaacoub ◽  
Uvais Qidwai ◽  
Khalid Abualsaud ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantum Key Distribution ◽  
Key Distribution ◽  
Machine Learning Techniques ◽  
Learning Techniques

Discrete-modulated continuous-variable quantum key distribution with a machine-learning-based detector

2018 ◽  
Vol 57 (06) ◽  
pp. 1 ◽  
Author(s):  
Jiawei Li ◽  
Ying Guo ◽  
Xudong Wang ◽  
Cailang Xie ◽  
Ling Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantum Key Distribution ◽  
Key Distribution ◽  
Continuous Variable

scholarly journals Grain Yield Estimation in Cultivated Land Using Machine Learning Techniques

2021 ◽  
Vol 36 (2) ◽  
pp. 70-75
Author(s):  
Dr.K. Venkata Nagendra ◽  
Dr.B. Prasad ◽  
K.T.P.S. Kumar ◽  
K.S. Raghuram ◽  
Dr.K. Somasundaram
Keyword(s):  
Machine Learning ◽  
Market Price ◽  
Specific Area ◽  
Continuous Variable ◽  
Machine Learning Techniques ◽  
Accurate Information ◽  
Agricultural Crop ◽  
Agricultural Field ◽  
Learning Techniques ◽  
Selection Of

Agriculture contributes approximately 28 percent of India's GDP, and agriculture employs approximately 65 percent of the country's labor force. India is the world's second-largest agricultural crop producer. Agriculture is not only an important part of the expanding economy, but it is also necessary for our survival. The technological contribution could assist the farmer in increasing his yield. The selection of each crop is critical in the planning of agricultural production. The selection of crops will be influenced by a variety of factors, including market price, production rate, and the policies of the various government departments. Numerous changes are required in the agricultural field in order to improve the overall performance of our Indian economy. By using machine learning techniques that are easily applied to the farming sector we can improve agriculture. Along with all of the advancements in farming machinery and technology, the availability of useful and accurate information about a variety of topics plays an important role in the success of the industry. It is a difficult task to predict agricultural output since it depends on a number of variables, such as irrigation, ultraviolet (UV), insect killers, stimulants & the quantity of land enclosed in that specific area. It is proposed in this article that two distinct Machine Learning (ML) methods be used to evaluate the yields of the crops. The two algorithms, SVR and Linear Regression, have been well suited to validate the variable parameters of the continuous variable estimate with 185 acquired data points.

scholarly journals Analytical Statistics Techniques of Classification and Regression in Machine Learning

2020 ◽  
Author(s):  
Pramod Kumar ◽  
Sameer Ambekar ◽  
Manish Kumar ◽  
Subarna Roy
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Machine Learning Techniques ◽  
Statistical Machine Learning ◽  
Building Models ◽  
Learning Techniques ◽  
Implementation Techniques ◽  
Regression Techniques ◽  
The Common ◽  
Classification And Regression

This chapter aims to introduce the common methods and practices of statistical machine learning techniques. It contains the development of algorithms, applications of algorithms and also the ways by which they learn from the observed data by building models. In turn, these models can be used to predict. Although one assumes that machine learning and statistics are not quite related to each other, it is evident that machine learning and statistics go hand in hand. We observe how the methods used in statistics such as linear regression and classification are made use of in machine learning. We also take a look at the implementation techniques of classification and regression techniques. Although machine learning provides standard libraries to implement tons of algorithms, we take a look on how to tune the algorithms and what parameters of the algorithm or the features of the algorithm affect the performance of the algorithm based on the statistical methods.

scholarly journals Practical Security Analysis of Reference Pulses for Continuous-Variable Quantum Key Distribution

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Wei Zhao ◽  
Ronghua Shi ◽  
Duan Huang
Keyword(s):  
Quantum Key Distribution ◽  
Beam Splitter ◽  
Security Analysis ◽  
Key Distribution ◽  
Continuous Variable ◽  
Excess Noise ◽  
Secret Key ◽  
Bayesian Algorithm ◽  
Robustness To Noise ◽  
Practical Security

AbstractBy manipulating the reference pulses amplitude, a security vulnerability is caused by self-reference continuous-variable quantum key distribution. In this paper, we formalize an attack strategy for reference pulses, showing that the proposed attack can compromise the practical security of CVQKD protocol. In this scheme, before the beam splitter attack, Eve intercepts the reference pulses emitted by Alice, using Bayesian algorithm to estimate phase shifts. Subsequently, other reference pulses are re-prepared and resubmitted to Bob. In simulations, Bayesian algorithm effectively estimates the phase drifts and has the high robustness to noise. Therefore, the eavesdropper can bias the excess noise due to the intercept-resend attack and the beam splitter attack. And Alice and Bob believe that their excess noise is below the null key threshold and can still share a secret key. Consequently, the proposed attack shows that its practical security can be compromised by transmitting the reference pulses in the continuous-variable quantum key distribution protocol.

An Overview of Machine Learning in Medical Image Analysis

2017 ◽  
pp. 36-58 ◽  
Author(s):  
Anand Narasimhamurthy
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Medical Imaging ◽  
Health Informatics ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The Common ◽  
Applications Of Machine Learning

Medical image analysis is an area which has witnessed an increased use of machine learning in recent times. In this chapter, the authors attempt to provide an overview of applications of machine learning techniques to medical imaging problems, focusing on some of the recent work. The target audience comprises of practitioners, engineers, students and researchers working on medical image analysis, no prior knowledge of machine learning is assumed. Although the stress is mostly on medical imaging problems, applications of machine learning to other proximal areas will also be elucidated briefly. Health informatics is a relatively new area which deals with mining large amounts of data to gain useful insights. Some of the common challenges in health informatics will be briefly touched upon and some of the efforts in related directions will be outlined.

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