Digital Immunity Module: Preventing Unwanted Encryption using Source Coding

Unwanted data encryption, such as ransomware attacks, continues to be a significant cybersecurity threat. Ransomware is a preferred weapon of cybercriminals who target small to large organizations' computer systems and data centres. It is malicious software that infects a victim's computer system and encrypts all its valuable data files. The victim needs to pay a ransom, often in cryptocurrency, in return for a decryption key. Many solutions use methods, including the inspection of file signatures, runtime process behaviors, API calls, and network traffic, to detect ransomware code. However, unwanted data encryption is still a top threat. This paper presents the first immunity solution, called the digital immunity module (DIM). DIM focuses on protecting valuable business-related data files from unwanted encryption rather than detecting malicious codes or processes. We show that methods such as file entropy and fuzzy hashing can be effectively used to sense unwanted encryption on a protected file, triggering our novel source coding method to paralyze the malicious manipulation of data such as ransomware encryption. Specifically, maliciously encrypted data blocks consume exponentially larger space and longer writing time on the DIM-protected file system. As a result, DIM creates enough time for system/human intervention and forensics analysis. Unlike the existing solutions, DIM protects the data regardless of ransomware families and variants. Additionally, DIM can defend against simultaneously active multiple ransomware, including the most recent hard to detect and stop fileless ones. We tested our solution on 39 ransomware families, including the most recent ransomware attacks. DIM successfully defended our sample file dataset (1335 pdf, jpg, and tiff files) against those ransomware attacks with zero file loss.

Digital Immunity Module: Preventing Unwanted Encryption using Source Coding

Unwanted data encryption, such as ransomware attacks, continues to be a significant cybersecurity threat. Ransomware is a preferred weapon of cybercriminals who target small to large organizations' computer systems and data centres. It is malicious software that infects a victim's computer system and encrypts all its valuable data files. The victim needs to pay a ransom, often in cryptocurrency, in return for a decryption key. Many solutions use methods, including the inspection of file signatures, runtime process behaviors, API calls, and network traffic, to detect ransomware code. However, unwanted data encryption is still a top threat. This paper presents the first immunity solution, called the digital immunity module (DIM). DIM focuses on protecting valuable business-related data files from unwanted encryption rather than detecting malicious codes or processes. We show that methods such as file entropy and fuzzy hashing can be effectively used to sense unwanted encryption on a protected file, triggering our novel source coding method to paralyze the malicious manipulation of data such as ransomware encryption. Specifically, maliciously encrypted data blocks consume exponentially larger space and longer writing time on the DIM-protected file system. As a result, DIM creates enough time for system/human intervention and forensics analysis. Unlike the existing solutions, DIM protects the data regardless of ransomware families and variants. Additionally, DIM can defend against simultaneously active multiple ransomware, including the most recent hard to detect and stop fileless ones. We tested our solution on 39 ransomware families, including the most recent ransomware attacks. DIM successfully defended our sample file dataset (1335 pdf, jpg, and tiff files) against those ransomware attacks with zero file loss.

EFFECTS OF ACHIEVING SUPPLY CHAIN FLEXIBILITY

This research study focuses on the current topic of supply chain management which serves as a tool for manufacturing enterprises to cope with pressure put on them by continuously changing market conditions and the global economy itself. Paper presents the results of research conducted on the sample file of Slovak production enterprises. The main aim of this research study is to explore the extent of achieving agility, adaptability and alignment as secondary effects of supply chain flexibility in Slovak manufacturing enterprises. Representativeness of the sample file was confirmed by the application of Pearson´s chi-squared test (χ2 - test) due to the criterion of an enterprise’s size. The results of this research provide a clear image of business reality in terms of supply chain organization and therefore have implications for business practice which may serve managers in their decision-making process in supply chain management.

Forecasting Financial Performance for Quarries/ Předvídání Hospodářského Výsledku Lomů

The paper highlights the importance of successful financial performance for companies, and provides for a brief review of foreign expert opinions on the most important factors that influence the financial performance of enterprises. Linear and quadratic discriminate analyses and a logistic regression analysis were applied to a sample file of 233 annual data from 3 countries (Czech Republic, Slovak Republic, Ukraine) for a period of 2008-2012 concerning quarries extracting building materials. These methods provided for distributing the sample file quarries into two classes of profitable and loss-making enterprises. Their financial performance had been known, which enabled to assess the classification accuracy of individual method applications. The average classification accuracy was about 86% and there were no significant differences in the specific method applications. The linear discriminate analysis calculations are the simplest ones in comparison with two other applied methods. The linear discriminate analysis also made possible to identify the most influential discriminators that contributed to the classification into the specific groups. In case of our investigation, prices per production unit, direct variable costs, and ratio of fixed costs to total costs were the most important factors of influence. The factors, if analysed, can provide for prediction of financial performance of quarries in future.

Internal Migration of the Foreign-Born in Canada

This article uses the Public Use Sample file of the 1986 Canadian census to characterize and explain the interprovincial migration patterns of the foreign-born in Canada. Simple overall in- and outmigration rates are calculated for the foreign-born and compared to the interprovincial migration rates for Canadian-born migrants, specifically primary, return and onward migrants. A two-level nested logit model is then applied for foreign-born migrants age 20–64 to study the effects of personal factors and provincial attributes on their interprovincial migration patterns. The foreign-born have higher in- and outmigration rates than primary migrants, with Ontario having a strong ability to attract and retain the foreign-born. Despite these differences, the foreign-born respond to economic variables in a rational way and relatively little of the migration decision process can be explained by place-of-birth effects. Selectivity with respect to personal factors (i.e., education, age, sex, family type) is similar to the Canadian-born.