Abstract Interpretation of Symbolic Execution with Explicit State Updates

Protecting information has become very important due to the safety-critical nature of many computer-based applications. Information flow analysis plays a very important role in quantifying information-related properties under external attacks. Traditionally, information flow analysis is performed using paper-and-pencil based proofs or computer simulations but due to their inherent nature, these methods are prone to errors and thus cannot guarantee accurate analysis. As an accurate alternative, we propose to conduct the information flow analysis within the sound core of a higher-order-logic theorem prover. For this purpose, some of the most commonly used information flow measures, including Shanon entropy, mutual information, min-entropy, belief min-entropy, have been formalized. In this paper, we use the Shannon entropy and mutual information formalizations to formally verify the Data Processing and Jensen’s inequalities. Moreover, we extend the security model for the case of the partial guess scenario to formalize the gain min-entropy. These formalizations allow us to reason about the information flow of a wide range of systems within a theorem prover. For illustration purposes, we perform a formal comparison between the min-entropy leakage and the gain leakage.

It is important to show brain activities and their relations visually for the elucidation of the processing mechanism and for the diagnosis of diseases in the human brain. We developed a three-dimensional displaying tool by estimating dipoles to show the activities and by analysing information flow between them to show the relations. At first, we estimate dipoles (via 3-layered concentric spherical model, 2-dipole estimation) from evoked potentials. Secondary, using derived 2 dipole locations and moments as loci and quantities of brain activities, we applied stationary analysis for the information flow between the two time-series of the 1st and the 2nd dipole moments. Therefore, we obtain bi-directional information flows between the neuronal activities localized in 3D space of the brain with respect to somatosensory evoked potentials measured with 21 electrodes arranged according to the international 10-20 standard. Furthermore, we tried non-stationary analysis for the information flow with simulation data.

Decision makers in an organization’s top management use a multitude of information to substantiate decisions. Execution staff also needs a multitude of inside information about the development of the production process, the way the tasks and objectives are met, the situation of the material and energy resources stocks, the functioning of the machines and the installations, the degree of fulfilment and the quality level of production, etc. The organization’s information system can be defined by all the data, information, flow and information circuits, information handling procedures and their means of application. All this information is processed and directed to potential users in order to be used to achieve the company’s goals. Incidentally, this information can be distorted by other factors such as: illegible printing, loss of information on computer’s magnetic supports due to wear, viruses or miscellaneous defects. Within the information system, it may often happen that we have to deal with the transmission of more information, from design to production and vice versa, for the conformation of the finished product.

Hardware designers need to precisely analyze high-level descriptions for illegal information flows. Language-based information flow analyses can be applied to hardware description languages, but a straight-forward application either conservatively rules out many secure hardware designs, or constrains the designers to work at impractically low levels of abstraction. We demonstrate that choosing the right level of abstraction for the analysis, by working on Finite State Machines instead of the hardware code, allows both precise information flow analysis and high-level programmability.

This paper aims to empirically examine long memory and bi-directional information flow between estimated volatilities of highly volatile time series datasets of five cryptocurrencies. We propose the employment of Garman and Klass (GK), Parkinson’s, Rogers and Satchell (RS), and Garman and Klass-Yang and Zhang (GK-YZ), and Open-High-Low-Close (OHLC) volatility estimators to estimate cryptocurrencies’ volatilities. The study applies methods such as mutual information, transfer entropy (TE), effective transfer entropy (ETE), and Rényi transfer entropy (RTE) to quantify the information flow between estimated volatilities. Additionally, Hurst exponent computations examine the existence of long memory in log returns and OHLC volatilities based on simple R/S, corrected R/S, empirical, corrected empirical, and theoretical methods. Our results confirm the long-run dependence and non-linear behavior of all cryptocurrency’s log returns and volatilities. In our analysis, TE and ETE estimates are statistically significant for all OHLC estimates. We report the highest information flow from BTC to LTC volatility (RS). Similarly, BNB and XRP share the most prominent information flow between volatilities estimated by GK, Parkinson’s, and GK-YZ. The study presents the practicable addition of OHLC volatility estimators for quantifying the information flow and provides an additional choice to compare with other volatility estimators, such as stochastic volatility models.

Information systems in small manufacturing companies can have a big impact on increasing economy and productivity. Due to limited human and financial resources, these companies generally use cheaper commercial solutions or develop specialized information system solutions that are adapted to their needs. In order to properly solve the problem of introducing an information system, a thorough analysis of information flows in the company and corresponding business processes is necessary. The main subject and goal of the research in this work is related to the investigation of time losses due to the absence of an information system from the aspect of managing the flow of cutting tools, as well as the presentation of the foundations for its development in a small manufacturing company.

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) is a powerful tool to investigate brain excitability and information processing in brain networks. However, EEG-TMS recordings are challenging because EEG is contaminated by powerful TMS-related artifacts. Because of these artifacts, different EEG-driven analyses (for instance, source analysis and analysis of information flow on the sensors and source level) reveal incorrect results. The aim of this study was to remove ocular artifacts from TMS-EEG recordings following stimulation of motor cortex using three independent component analysis (ICA) algorithms and to evaluate the effectiveness of these algorithms. We showed that the temporal ICA algorithm better separates those components that contain time-locked eye blink artifacts.

Social networks are important part of society. Analysis of information flows and activity of users may provide different opportunities for scientist. However, existing methods and models for prediction of information flow couldn’t provide sufficient level of accuracy. Proposed method of information flow analysis is based on machine learning algorithms. First stage of research aims to analyze the features, that could be useful for describing activity of users and information in social networks. Data was assembled from social network “VKontakte”. Data includes main information about selected features for each person. On the second stage were conducted experiments, based on machine learning methods. Three main approaches were implemented in research - naive Bayes classifier, logistic regression analysis and k-means clustering. A comparison of different machine learning methods was conducted on last stage. Best results of predictions were reached by usage of naive Bayes classifier. Proposed method for prediction of information flow is usable for organizing users’ protection from different type of information attacks.

Merchants through smartphones and mobile applications measure and analyze users’ interests and behaviors, and identify users in applications and browsers to push services, updates, and advertisements to the users. This way of providing personalized services to smartphone users to attract potential customers has improved the user experience, created new opportunities for advertising, and changed the marketing trend of enterprises. However, the collection, processing, and analysis of user personal data has brought serious privacy issues to individuals and organizations. This paper introduces privacy risks, including user information collection and analysis of information flow between networks, analysis procedures and standards. Discussed the challenges of protecting user privacy, including privacy threats brought by user information and analysis, extraction, and exchange of private information between various advertising entities, privacy threats tracked by third parties, privacy information and related privacy issues. Next, based on these technologies, related underlying architecture, privacy mechanisms, and deployment scenarios, several technologies, and various suggestions for protecting user privacy are analyzed. Finally, the respect for people, regulations and laws, the limitations of technical solutions, ethical principles and moral dilemmas were discussed.

The chapter provides an outlook to contemporary innovative methods for detecting internal threats to the information security of critical infrastructure objects, mitigating these threats, as well as preventing the leakage of sensitive information. Internal threats are unpredictable and pose a major challenge to traditional IT security measures. A specific emphasis is placed on the insider threats problem emerging due to: careless behaviour of insiders, vendors and contractors, cybersecurity policies, e-identity theft, and malicious users. Methods for detecting and protecting internal threats encompass user behaviour analysis, consumer behaviour analysis, risk assessment and profiling, analysis of information flow within the organisation, and definition of sensitive information. Some useful methods for protecting sensitive data through a holistic approach that covers data both inside and outside the organization are also presented. Consumer activity monitoring systems and Data Leak Prevention (DLP) data leakage monitoring systems are finally discussed in the context of practical handling of internal threats.

Organization, planning and management at the enterprise directly related to the use of information flows, including the collection, transmission, processing, analysis of information and the adoption of appropriate management decisions. Economic activity is impossible without the effective use of information flows, which in turn will contribute to the optimization of production processes. To do this, it is necessary to consider the organizational structure of this unit, the systemic organization in it, the nature of subordination and accountability to each other, as well as a set of coordination and information links, the procedure for distributing management functions across various levels and subdivisions of the enterprise management hierarchy. Also to explore the functions of each unit of the structural unit, reflect the essence of their elements, the relationship between them. The study of the types and essence of information flows determines the maximum completeness of their receipt, and this will ensure their effective use at all levels of enterprise management. The important thing in the middle of all the storage elements in management is the loan organization structure of the management of the enterprise, the rationalization of the incentives and the success of its function. The situation in the light metallurgical halls has negatively impacted the business of the enterprise. To that it is necessary to develop the optimization of the go for the efficiency of the function of the enterprise in the smart minds. The head of the poll in the analysis of information flows in the business processes of AT "Nikopolskiy ferroplaviv plant" with the method of optimizing virobnichny processes. An enterprise is a dynamic structure, the work of which is based both on the basis of the relationship between the feeling of the middle and the internal relationship between the elements. Every enterprise cannot be realized without the transfer of information. Management of information streams is one of the main tasks, without a lot of ill-advised development, not only of the enterprise, but of the whole.

АНАЛИЗ ИНФОРМАЦИОННЫХ ПОТОКОВ МНОГОФУНКЦИОНАЛЬНОЙ ШАХТНОЙ СИСТЕМЫ БЕЗОПАСНОСТИ И РАЗРАБОТКА КОНЦЕПТУАЛЬНОЙ МОДЕЛИ БАЗЫ ДАННЫХ

Despite the persistent research interest in marine microplastic (MP) particles, the pollutant is still largely misunderstood. Significant data gaps hinder experts’ understanding of the sources, pathways, and fate of marine MPs, making it difficult to assess its environmental implications. Interdisciplinary work is required to fully understand the complexity of marine MPs (MMP) and to address the potential stress that this form of pollution may put on marine ecosystems. This study introduces an approach called information flow analysis (IFA), which intends to connect different fields of study through data flows, demonstrating the importance of these fields in the development of life cycle impact assessment (LCIA) of MMPs. The IFA approach was used to clarify where microplastic data is expected to come from with the goal of providing insight into how to obtain the data required for risk assessment (RA) and life cycle assessment (LCA). In order to observe and demonstrate these links, the next step was to develop a site‐specific coarse material flow analysis (MFA) based on previously estimated MP emissions from Norway and data from MP sampling in the inner Oslofjord. The coarse MFA demonstrated that MP sources in Norway were estimated to contribute between 85% and 96% rubbery MP particles and between 4% and 15% semisynthetic MP particles in 2018, while blue mussels sampled by the Norwegian Institute for Water Research (NIVA) contained 93% rubbery MP particles and 7% semisynthetic MP particles. This seems to demonstrate a connection between Norwegian MP sources (emissions) and MP levels observed in the environment.

Reasoning about information flow can help software engineering. Static information flow inference analysis is a technique which automatically infers information flows based on data or control dependence. It can be utilized for the purposes of general program understanding, detection of security attacks and security vulnerabilities, and type inference for security type systems. This paper proposes a new static information flow inference analysis, which unlike most other information flow analyses, handles both explicit and implicit information flows. The analysis does not require annotations and it is relatively precise and practical. We illustrate the usage of the static information flow analysis on three applications. The first application of information flow analysis is security violation detection. We perform experiments on a set of Java web applications and the experiments show that our analysis effectively detects security violations. The second application is type inference. Our experiments on the Java web applications successfully infer security types. The last application studies the effect of thread-shared variables on thread-local variables. Our experiments on a set of multi-thread programs show that most of the thread-local variables are affected by the thread-shared variables. We study the impact of implicit flow versus explicit flow in these applications. Implicit flow has significant impact on all these applications. In security violation detection, implicit flow detects more security violations than explicit flow. In type inference, implicit flow infers more untrusted type variables. In the study of the effect of thread-shared variables, implicit flow detects more affected variables than explicit flow.

Analyzing information flow is central in assessing the security of applications. However, static and dynamic analyses of information flow are easily challenged by non-available or obscure code. We present a lightweight mutation-based analysis that systematically mutates dynamic values returned by sensitive sources to assess whether the mutation changes the values passed to sensitive sinks. If so, we found a flow between source and sink. In contrast to existing techniques, mutation-based flow analysis does not attempt to identify the specific path of the flow and is thus resilient to obfuscation. In its evaluation, our MUTAFLOW prototype for Android programs showed that mutation-based flow analysis is a lightweight yet effective complement to existing tools. Compared to the popular FlowDroid static analysis tool, MutaFlow requires less than 10% of source code lines but has similar accuracy; on 20 tested real-world apps, it is able to detect 75 flows that FlowDroid misses.