Investigation of heuristic search functions for nonlinear substitutions for symmetric cryptography

Nonlinear substitutions (S-boxes) are used in most modern symmetric cryptoalgorithms. They are designed to mix input data and play a significant role in ensuring resistance against known cryptanalytic attacks (differential, linear, algebraic and other cryptanalysis methods). However, random generation of nonlinear substitutions with the desired indicators is an extremely difficult mathematical problem. This article explores the heuristic techniques for S-boxes informed search, in particular, discusses various cost functions used in most of the known algorithms (for example, local search, hill climbing, simulated annealing, genetic search, etc.). The aim of the study is to determine the specific parameters of heuristic functions, which, on the one hand, do not reduce the degree of awareness of the search nodes, and on the other hand, do not require significant computational costs. The article examines the influence of individual parameters on the value of the cost function and complexity of its calculation. It also provides specific recommendations for the formation of parameters for heuristic search for S-boxes, which significantly affect the efficiency of generating nonlinear substitutions for symmetric cryptography.

Feature Selection and Ensemble-Based Intrusion Detection System: An Efficient and Comprehensive Approach

The emergence of ground-breaking technologies such as artificial intelligence, cloud computing, big data powered by the Internet, and its highly valued real-world applications consisting of symmetric and asymmetric data distributions, has significantly changed our lives in many positive aspects. However, it equally comes with the current catastrophic daily escalating cyberattacks. Thus, raising the need for researchers to harness the innovative strengths of machine learning to design and implement intrusion detection systems (IDSs) to help mitigate these unfortunate cyber threats. Nevertheless, trustworthy and effective IDSs is a challenge due to low accuracy engendered by vast, irrelevant, and redundant features; inept detection of all types of novel attacks by individual machine learning classifiers; costly and faulty use of labeled training datasets cum significant false alarm rates (FAR) and the excessive model building and testing time. Therefore, this paper proposed a promising hybrid feature selection (HFS) with an ensemble classifier, which efficiently selects relevant features and provides consistent attack classification. Initially, we harness the various strengths of CfsSubsetEval, genetic search, and a rule-based engine to effectively select subsets of features with high correlation, which considerably reduced the model complexity and enhanced the generalization of learning algorithms, both of which are symmetry learning attributes. Moreover, using a voting method and average of probabilities, we present an ensemble classifier that used K-means, One-Class SVM, DBSCAN, and Expectation-Maximization, abbreviated (KODE) as an enhanced classifier that consistently classifies the asymmetric probability distributions between malicious and normal instances. HFS-KODE achieves remarkable results using 10-fold cross-validation, CIC-IDS2017, NSL-KDD, and UNSW-NB15 datasets and various metrics. For example, it outclassed all the selected individual classification methods, cutting-edge feature selection, and some current IDSs techniques with an excellent performance accuracy of 99.99%, 99.73%, and 99.997%, and a detection rate of 99.75%, 96.64%, and 99.93% for CIC-IDS2017, NSL-KDD, and UNSW-NB15, respectively based on only 11, 8, 13 selected relevant features from the above datasets. Finally, considering the drastically reduced FAR and time, coupled with no need for labeled datasets, it is self-evident that HFS-KODE proves to have a remarkable performance compared to many current approaches.

Multiple endocrine neoplasia type 1 with a frameshift mutation in its gene accompanied by a giant cervical lipoma and multiple fatty deposits in the pancreas: case report

Background Multiple endocrine neoplasia type 1 (MEN1) is a syndrome characterized by pituitary neoplasia, primary hyperparathyroidism and pancreatic endocrine tumor. Here we show a case of MEN1 with a germline frameshift mutation in its gene accompanied by a giant cervical lipoma and multiple fatty deposits in the pancreas. Case presentation A 28-year-old man noticed the decreased visual acuity of both eyes and visited our institution. Since he was diagnosed as visual disturbance and brain computer tomography (CT) showed a mass in the pituitary fossa, he was hospitalized in our institution. Endoscopic trans-sphenoidal hypophysectomy and total parathyroidectomy with auto-transplantation were performed, and a giant cervical lipoma was resected. Furthermore, in genetic search, we found a germline frameshift mutation in MEN1 gene leading to the appearance of a new stop codon. Conclusions We should bear in m ind that giant skin lipoma and multiple abnormal fatty deposits in the pancreas could be complicated with MEN1.

Modern ideas about the clinic, diagnosis and therapy of Fabry disease

Обзор литературы посвящён болезни Фабри (БФ) - редкому наследственному заболеванию. В нем представлен всесторонний анализ современных эпидемиологических данных и особенностей этиопатогенеза БФ, а также даны клинико-патогенетические характеристики различных типов БФ. Подробно описана роль фермента α-галактозидазы А и биомаркера глоботриаозилсфингозина (лизо-Гб3) при БФ. Изложены и указаны ключевые этапы биохимического и молекулярно-генетического поиска в диагностике данной патологии, описаны современные возможности терапии. The focus of the article is Fabry disease - a rare enough hereditary pathology. The authors present the most up-to-date epidemiological data and features of Fabry disease etiopathogenesis. The offer clinical characteristics of the various types of this disease. The role of enzyme α-galactosidase A and biomarker globotriaosylsphingosine in Fabry disease is describes in detail. The key stages of the biochemical and molecular genetic search in the diagnosis of this pathology are outlined and indicated, the modern possibilities of therapy are described.

First trimester screening with biochemical markers and ultrasound in relation to non-invasive prenatal testing (NIPT)

Non-invasive prenatal testing (NIPT) is often erroneously received as a diagnostic procedure due to its high discriminatory power in the field of fetal trisomy 21 diagnosis (wording: “NIPT replaces amniocentesis”). Already a look at the methodology of NIPT (statistical gene dose comparison of a primarily maternofetal DNA mixture information at selected sites of the genome) easily reveals that NIPT cannot match the gold standard offered by cytogenetic and molecular genetic analysis procedures from the matrix of the entire human genome (origin: vital fetal cells), neither in diagnostic breadth nor in diagnostic depth. In fact, NIPT in fetal medicine in its current stage of development is a selective genetic search procedure, which can be applied in primary (without indication) or secondary (indication-related) screening. Thus, NIPT competes with established search procedures for this field. Here, the combined nuchal translucency (NT) test according to Nicolaides has become the worldwide standard since 2000. The strength of this procedure is its broad predictive power: NT addresses not only the area of genetics, but also the statistically 10 times more frequent structural fetal defects. Thus, NIPT and NT have large overlaps with each other in the field of classical cytogenetics, with slightly different weighting in the fine consideration. However, NIPT without a systematic accompanying ultrasound examination would mean a step back to the prenatal care level of the 1980s. In this respect, additional fine ultrasound should always be required in the professional application of NIPT. NIPT can thus complement NT in wide areas, but not completely replace it.

The Evolution of Complementary Cognition: Humans Cooperatively Adapt and Evolve through a System of Collective Cognitive Search

We propose a new theory of human cognitive evolution, which we term Complementary Cognition. We build on evidence for individual neurocognitive specialization regarding search abilities in the modern population, and propose that our species cooperatively searches and adapts through a system of group-level cognition. This paper sets out a coherent theory to explain why Complementary Cognition evolved and the conditions responsible for its emergence. Using the framework of search, we show that Complementary Cognition can be contextualized as part of a hierarchy of systems including genetic search and cognitive search. We propose that, just as genetic search drives phenotypic adaptation and evolution, complementary cognitive search is central to understanding how our species adapts and evolves through culture. Complementary Cognition has far-reaching implications since it may help to explain the emergence of behavioural modernity and provides a new explanatory framework for why language and many aspects of cooperation evolved. We believe that Complementary Cognition underpins our species’ success and has important implications for how modern-day systems are designed.

Binary Decision Tree Construction using the Hybrid Swarm Intelligence

Solving the problem of a classification model construction is presented in the form of a sequence of considered attributes and values thereof included in the Mk route from the root to the dangling vertex. Decision tree developed interpretation is presented as a pair of chromosomes (Sk, Wk). The Sk chromosome list of genes corresponds to the list of all attributes included in the Mk route in the decision tree. The Wk chromosome gene values correspond to the attribute values included in the Mk route. Unification of data structures, search space and modernization of integrable algorithms was carried out for hybridization. Hybrid algorithm operators are using the integer parameters and synthesize new integer parameter values. Method was developed to account for simultaneous attraction of the αi particle to three xi (t), x*i (t), x*(t) attractors dislocating from the xi (t) position to the xi (t + 1) position. Modified hybrid metaheuristic of the search algorithm is proposed for constructing a classification model using recombination of swarm and genetic search algorithms. The first approach uses genetic algorithm initially and then the particle swarm algorithm. The second approach uses the high-level nesting hybridization method based on combination of genetic algorithm and particle swarm algorithm. The proposed approach to constructing a modified paradigm uses chromosomes with integer parameter values in the indicated hybrid algorithm and operators, which assist chromosomes to evolve according to the rules of particle swarm and genetic search

Profit optimization for multi-mode repetitive construction project with cash flows using metaheuristics

The article presents the profit optimization model for multi-unit construction projects. Such projects constitute a special case of repetitive projects and are common in residential, commercial, and industrial construction projects. Due to the specific character of construction works, schedules of such projects should take into account many different aspects, including durations and costs of construction works, the possibility of selecting alternative execution modes, and specific restrictions (e.g., deadlines for the completion of units imposed by the investor). To solve the NP-hard problem of choosing the order of units’ construction and the best variants of works, the authors used metaheuristic algorithms (simulated annealing and genetic search). The objective function in the presented optimization model was the total profit of the contractor determined on the basis of the mathematical programming model. This model takes into account monthly cash flows subject to direct and indirect costs, penalties for missing deadlines, costs of work group discontinuities, and borrowing losses. The presented problem is very important for maintaining a good financial condition of the enterprise carrying out construction projects. In the article, an experimental analysis of the proposed method of solving the optimization task was carried out in a model that showed high efficiency in obtaining suboptimal solutions. In addition, the operation of the proposed model has been presented on a calculation example. The results obtained in it are fully satisfying.

A Hybrid Supervised Machine Learning Classifier System for Breast Cancer Prognosis Using Feature Selection and Data Imbalance Handling Approaches

Nowadays, breast cancer is the most frequent cancer among women. Early detection is a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, the methods to improve the accuracy of ML classification models for the prognosis of breast cancer are investigated. Wrapper-based feature selection approach along with nature-inspired algorithms such as Particle Swarm Optimization, Genetic Search, and Greedy Stepwise has been used to identify the important features. On these selected features popular machine learning classifiers Support Vector Machine, J48 (C4.5 Decision Tree Algorithm), Multilayer-Perceptron (a feed-forward ANN) were used in the system. The methodology of the proposed system is structured into five stages which include (1) Data Pre-processing; (2) Data imbalance handling; (3) Feature Selection; (4) Machine Learning Classifiers; (5) classifier’s performance evaluation. The dataset under this research experimentation is referred from the UCI Machine Learning Repository, named Breast Cancer Wisconsin (Diagnostic) Data Set. This article indicated that the J48 decision tree classifier is the appropriate machine learning-based classifier for optimum breast cancer prognosis. Support Vector Machine with Particle Swarm Optimization algorithm for feature selection achieves the accuracy of 98.24%, MCC = 0.961, Sensitivity = 99.11%, Specificity = 96.54%, and Kappa statistics of 0.9606. It is also observed that the J48 Decision Tree classifier with the Genetic Search algorithm for feature selection achieves the accuracy of 98.83%, MCC = 0.974, Sensitivity = 98.95%, Specificity = 98.58%, and Kappa statistics of 0.9735. Furthermore, Multilayer Perceptron ANN classifier with Genetic Search algorithm for feature selection achieves the accuracy of 98.59%, MCC = 0.968, Sensitivity = 98.6%, Specificity = 98.57%, and Kappa statistics of 0.9682.