Hybrid Random Number Generation Architecture for Mobile Registration Controller: A Reconfigurable Hardware Realization

Mobile phones have become one of the mostly used gadgets in the world. The number of devices being used has been increasing tremendously and the concern for signal connectivity has been growing everyday. In this work, a mobile phone location registration model has been proposed using a hybrid random number generator (HRNG). Traffic of the cellular devices during the successive location registration with base station can be managed by incorporating a HRNG which produces different delays in different mobile phones. This HRNG was designed using ring oscillator, PLL and cellular automata. The developed HRNG was utilized to create non-overlapping pulses on Cyclone II FPGA EP2C20F484C7 which depict a part of mobile registration controller hardware. The proposed scheme utilized 1616 combinational functions and 1003 registers with a total power dissipation of 69.96 mW. The HRNG was analyzed with restart, entropy and NIST randomness analyses. The capability of mobile registration architecture was analyzed with correlation and random distribution analyses.

     

Tri-level scrambling and enhanced diffusion for DICOM image cipher- DNA and chaotic fused approach

Tele-health and e-healthcare are some of the innovative e-commerce appliances that can eliminate the barrier between time and distance among health care centres and patients. The proposed work approaches the obstacle to secure digital medical image data in a public cloud. The most crucial part of e-healthcare and telemedicine industries is cyber-attacks. To thwart cyber-attacks, it is necessary to protect the medical images and transmit them securely. In this paper, a novel way of scrambling and Deoxyribonucleic Acid (DNA) sequence operations is performed to encrypt the digital medical images. A chaotic tri-level scrambling is carried out by a two dimensional Tinkerbell map. Experimental outcomes and security analyses such as statistical, differential, keyspace, encryption quality, along with chosen-plaintext attack analysis have been perpetrated to determine the feasibility and potency of the proposed Digital Imaging and Communications in Medicine (DICOM) image encryption method. The algorithm attains average entropy of 7.99 and near-zero correlation with NPCR and UACI of 99.6 and 33.4, respectively. Further, the efficiency of the algorithm is compared with the state of the literature encryption techniques.

     

Efficient hybrid algorithm based on genetic with weighted fuzzy rule for developing a decision support system in prediction of heart diseases

The Journal of Supercomputing - In this article, the clinical decision support system is discussed under the weighted fuzzy rule approach and genetic algorithm for computer-aided heart disease...     

NB+: An improved Naïve Bayesian algorithm

A novel algorithm named NB⁺ which is an extended version of the traditional Naïve Bayesian algorithm has been presented in this paper. An exception occurs when there is an equal probability for the class label value in the Naïve Bayesian algorithm. The approach aims to suggest a solution with the help of a partial matching method. Consequently, the classification accuracy has drastically improved. Experimental evaluation has been done on various databases to show that NB⁺ algorithm outperforms the traditional Naïve Bayesian algorithm.     

Experimental investigation on the effect of alkanes blending on performance,combustion and emission characteristics of four-stroke diesel engine

In this experimental investigation, an attempt was made to increase the performance and reduce the emission by adding alkanes such as n-pentane and n-hexane separately at different proportions, such as 4%, 6% and 8% by volume, with diesel. The performance analysis reported that, at full load, the brake thermal efficiency was increased by 3.605%, 3.170%, 4.305%, 4.394%, 5.336% and 6.173% for the blending of 4% n-pentane, 6% n-pentane, 8% n-pentane, 4% n-hexane, 6% n-hexane and 8% n-hexane with diesel, respectively. The emission test concluded that the smoke density was increased by 9.915%, 9.905%, 6.325%, 9.573%, 6.154% and 5.983% for the blending of 4% n-pentane, 6% n-pentane, 8% n-pentane, 4% n-hexane, 6% n-hexane and 8% n-hexane with diesel, respectively. The NO_x emission was decreased by 8.265%, 8.674%, 17.430%, 5.401%, 5.810% and 7.529% for the blending of 4% n-pentane, 6% n-pentane, 8% n-pentane, 4% n-hexane, 6% n-hexane and 8% n-hexane with diesel, respectively.     

Uncover the cover to recover the hidden secret - A separable reversible data hiding framework

The volatile development in the multimedia cognitive content is changing the global set-up towards a cloud-based architecture which is helped us with a massive amount of computer storage and the highest computational platform. Cost-saving and elasticity of services will be provided by progressive cloud computing technology for users. With the advancement in multimedia technology, the data owners outsource their private multimedia data on the hybrid cloud. Meantime the cloud servers also carry out some highly computationally expensive tasks. Nevertheless, there is an opportunity for security infracts possible in the public cloud environment. It makes an alarm for a cloud environment in security aspects. Before outsourcing multimedia data, an encryption technique is needed for safeguarding against several attacks. But performing the same is a significant challenge. A new research area was recently awakened on privacy-preserving Reversible Data Hiding (RDH) especially for multimedia data over the outsourced environment. A novel RDH for an encrypted image was proposed in this paper by using the (Most Significant Bit) MSB difference of the pixel value. By using this method, any third-party people can embed the ciphertext in the cipher image without the knowledge of the cover and secret. A person with decryption keys can get back the secret and the cover without any loss. The proposed work achieves the embedding capacity up to 1 bpp (bits per pixel) with the encryption quality of near-zero correlation and uniform histogram. The decrypted image is also retrieved with infinite Peak Signal to Noise Ratio (PSNR), unit Structural Similarity Index Metric (SSIM) and zero Bit Error Rate (BER).

     

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window-based attack detection in 6G

Regardless of the developments of networking and communication technologies, security is without exception a predominant feature to ensure network reliability. The future sixth-generation (6G) network is anticipated to be carried out with artificial intelligence (AI) powered communication via machine learning (ML), post-quantum cryptography, and so on. AI-powered communication has been in recent years utilized in enhancing network traffic performance with respect to resource management, optimal frequency spectrum design, security, and latency. The studies of modern wireless communications and anticipated features of 6G networks revealed a prerequisite for designing a trustworthy attack detection mechanism. In this work, a method called, Luong Attention and Hosmer Lemeshow Regression Window-based (LA-HLRW) attack detection in 6G is proposed. Initially, with the raw Botnet Attack dataset obtained as input, preprocessing is performed to normalize network traffic features. Next, the dimensionality of network traffic feature of large-scale network traffic data is reduced using the Luong Attention integrated with Long Short Term Memory (LSTM)-based Feature extraction model. Finally, with the objective of classifying network traffic samples for attack detection in 6G, we analyze the low dimensional network traffic feature set produced by Luong Attention integrated with LSTM using the Hosmer Lemeshow Logistic Regression Window-based Attack Detection model. Extensive experiments are performed with the Botnet Attack dataset to validate the efficiency of the proposed LA-HLRW method by using different parameters such as attack detection accuracy, attack detection time, precision, and recall. The overall analysis of proposed LA-HLRW results significantly reduced the attack detection time by 24%, and additionally improved attack detection accuracy, precision, and recall by 5%, 5%, and 6% as compared to existing attack detection methods respectively.     

A novel feature selection framework for automatic web page classification

The number of Internet users and the number of web pages being added to WWW increase dramatically every day.It is therefore required to automatically and e?ciently classify web pages into web directories.This helps the search engines to provide users with relevant and quick retrieval results.As web pages are represented by thousands of features,feature selection helps the web page classifiers to resolve this large scale dimensionality problem.This paper proposes a new feature selection method using Ward s minimum variance measure.This measure is first used to identify clusters of redundant features in a web page.In each cluster,the best representative features are retained and the others are eliminated.Removing such redundant features helps in minimizing the resource utilization during classification.The proposed method of feature selection is compared with other common feature selection methods.Experiments done on a benchmark data set,namely WebKB show that the proposed method performs better than most of the other feature selection methods in terms of reducing the number of features and the classifier modeling time.     

Application of Solid Me3SiZnI for the Synthesis of Aryl and Alkyl Trimethylsilanes

Silylzinc reagents are desirable for the synthesis of organosilanes due to their compatibility with functional groups; however, the use of pyrophoric silyllithium and dissolved lithium salts in their production hinders their development. Our solid Me₃SiZnI circumvents these limitations, and herein, we demonstrate its significance in the synthesis of aryl and alkyl trimethylsilanes via cross-coupling of aryl and alkyl bromides. The milder reaction condition tolerates functional groups such as MOM, Boc, Bpin, and aldehydes.