The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

International Journal of Computer and Applications

Introduction to R.

In this paper, a novel image encryption algorithm is proposed based on the combination of the chaos sequence and the modified AES algorithm. In this method, the encryption key is generated by Arnold chaos sequence. Then, the original image is encrypted using the modified AES algorithm and by implementing the round keys produced by the chaos system. The proposed approach not only reduces the time complexity of the algorithm but also adds the diffusion ability to the proposed algorithm, which make the encrypted images by the proposed algorithm resistant to the differential attacks. The key space of the proposed method is large enough to resist the brute-force attacks. This method is so sensitive to the initial values and input image so that the small changes in these values can lead to significant changes in the encrypted image. Using statistical analyses, we show that this approach can protect the image against the statistical attacks. The entropy test results illustrate that the entropy values are close to the ideal, and hence, the proposed algorithm is secure against the entropy attacks. The simulation results clarify that the small changes in the original image and key result in the significant changes in the encrypted image and the original image cannot be accessed.

/pdf/an-image-encryption-method-based-on-chaos-system-and-aes-565le5oy8z.pdf

An image encryption method based on chaos system and AES algorithm

In the era of big data and networking, it is necessary to develop a secure and robust digital watermarking scheme with high computational efficiency to protect copyrights of digital works. However, most of the existing methods focus on robustness and embedding capacity, losing sight of security or requiring significant computational resources in the encryption process. This paper proposed a new digital image watermarking model based on scrambling algorithm Logistic and RSA asymmetric encryption algorithm to guarantee the security of the hidden data at the foundation of large embedding capacity, good robustness and high computational efficiency. The experiments involved applying the encryption algorithms of Logistic and RSA to the watermark image and performing the hybrid decomposition of Discrete wavelet transform (DWT) and Singular Value Decomposition (SVD) on the host image, and the watermark was embedded into the low-frequency sub-band of the host. The values of PSNR and NCC were measured to estimate the imperceptibility and robustness of the proposed watermarking scheme, and the CPU running time was recorded to measure the complexity of the proposed main algorithm in execution time. Experimental results showed the superiority of the proposed watermarking scheme.

Secure and robust digital image watermarking scheme using logistic and RSA encryption

In today’s digital world healthcare is one core area of the medical domain. A healthcare system is required to analyze a large amount of patient data which helps to derive insights and assist the prediction of diseases. This system should be intelligent in order to predict a health condition by analyzing a patient’s lifestyle, physical health records and social activities. The health recommender system (HRS) is becoming an important platform for healthcare services. In this context, health intelligent systems have become indispensable tools in decision making processes in the healthcare sector. Their main objective is to ensure the availability of the valuable information at the right time by ensuring information quality, trustworthiness, authentication and privacy concerns. As people use social networks to understand their health condition, so the health recommender system is very important to derive outcomes such as recommending diagnoses, health insurance, clinical pathway-based treatment methods and alternative medicines based on the patient’s health profile. Recent research which targets the utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed which reduces the workload and cost in health care. In the healthcare sector, big data analytics using recommender systems have an important role in terms of decision-making processes with respect to a patient’s health. This paper gives a proposed intelligent HRS using Restricted Boltzmann Machine (RBM)-Convolutional Neural Network (CNN) deep learning method, which provides an insight into how big data analytics can be used for the implementation of an effective health recommender engine, and illustrates an opportunity for the health care industry to transition from a traditional scenario to a more personalized paradigm in a tele-health environment. By considering Root Square Mean Error (RSME) and Mean Absolute Error (MAE) values, the proposed deep learning method (RBM-CNN) presents fewer errors compared to other approaches.

DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

In Agriculture, mostly crops are loss due to the erroneous selection of the crop to be grown in a particular land holding. The farmers are generally not aware of the requirements of the crop i.e. the minerals, soil moisture and other soil requirements. This can cause a distress to farmer both mentally and financially. One more problem that a farmer generally encounters is the pest and diseases that can affect the crops they grow, which they are generally unaware of in an early stage. This problem of farmer is addressed in our paper and we have tried to solve it with the help of a Recommendation System. By the help of our model, we predict the best suitable crop to the farmer and detect the pest that may affect as well as suggest the pest control techniques. In this paper, we have applied SVM classification algorithm, Decision Tree algorithm and Logistic Regression algorithm and we have found that SVM classification model gives the better accuracy as compared to other algorithms.

Recommendation System for Crop Identification and Pest Control Technique in Agriculture

Now-a-days modern technology is used for health management and diagnostic strategy in the health sector. Machine learning usually helps in decision making for health issues using different models. Classification and prediction of disease are easily known with the help of machine learning techniques. The machine learning technique can be applied in various applications such as image segmentation, fraud detection, pattern recognition and disease prediction, etc. In the today’s world, maximum people are suffering from diabetes. The glucose factor in the blood is the main component of diabetes. Fluctuation of blood glucose level leads to diabetes. To predict the diabetes disease, machine learning and deep learning play major role which uses probability, statistics and neural network concepts, etc. Deep learning is the part of machine learning which uses different layers of neural network that decide classification and prediction of disease. In this chapter, we study and compare among different machine learning algorithms and deep neural networks for diabetes disease prediction, by measuring performance. The experiment results prove that convolution neural network based deep learning method provides the highest accuracy than other machine learning algorithms.

Performance Evaluation of Different Machine Learning Methods and Deep-Learning Based Convolutional Neural Network for Health Decision Making

DNA (Deoxyribonucleic Acid) Cryptography has revolutionized information security by combining rigorous biological and mathematical concepts to encode original information in terms of a DNA sequence. Such schemes are crucially dependent on corresponding DNA-based cryptographic keys. However, owing to the redundancy or observable patterns, some of the keys are rendered weak as they are prone to intrusions. This paper proposes a Genetic Algorithm inspired method to strengthen weak keys obtained from Random DNA-based Key Generators instead of completely discarding them. Fitness functions and the application of genetic operators have been chosen and modified to suit DNA cryptography fundamentals in contrast to fitness functions for traditional cryptographic schemes. The crossover and mutation rates are reducing with each new population as more keys are passing fitness tests and need not be strengthened. Moreover, with the increasing size of the initial key population, the key space is getting highly exhaustive and less prone to Brute Force attacks. The paper demonstrates that out of an initial 25 × 25 population of DNA Keys, 14 keys are rendered weak. Complete results and calculations of how each weak key can be strengthened by generating 4 new populations are illustrated. The analysis of the proposed scheme for different initial populations shows that a maximum of 8 new populations has to be generated to strengthen all 500 weak keys of a 500 × 500 initial population.

/pdf/best-fit-dna-based-cryptographic-keys-the-genetic-algorithm-1fq5wnzk.pdf

Best Fit DNA-Based Cryptographic Keys: The Genetic Algorithm Approach

In today's digital world, healthcare is one of the core areas in the medical domain. A healthcare system is required to analyze a large amount of patient data, which helps to derive insights and predictions of disease. This system should be intelligent and able to predict the patient's health condition by analyzing the patient's lifestyle, physical health records, and social activities. The health recommendation system (HRS) is becoming an important platform for healthcare services. In this context, health intelligent systems have become indispensable tools in decision-making processes in the healthcare sector. The main objective is to ensure the availability of valuable information at the right time by ensuring information quality, trustworthiness, authentication, and privacy. As people use social networks to learn about their health condition, so the HRS is very important to derive outcomes such as recommending diagnosis, health insurance, clinical pathway-based treatment methods, and alternative medicines based on the patient's health profile. In this chapter, we discuss recent research that targeted utilization of large volumes of medical data while combining multimodal data from disparate sources, which reduces the workload and cost in healthcare. In the healthcare sector, big data analytics using a recommendation system has an important role in terms of decision-making processes regarding the patient's health. This chapter presents a proposed intelligent HRS that provides an insight into how to use big data analytics for implementing an effective health recommendation engine and shows how to transform the healthcare industry from the traditional scenario to more personalized paradigm in a tele-health environment. Our proposed intelligent HRS resulted in lower MAE value when compared to existing approaches.

Chittaranjan Pradhan

Papers

DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

Recommendation System for Crop Identification and Pest Control Technique in Agriculture

Performance Evaluation of Different Machine Learning Methods and Deep-Learning Based Convolutional Neural Network for Health Decision Making

Best Fit DNA-Based Cryptographic Keys: The Genetic Algorithm Approach

Intelligence-Based Health Recommendation System Using Big Data Analytics