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Open-source licenses are infrastructure that collaborative communities inhabit. These licenses don't just define the legal terms under which members (and outsiders) can use and build on the contributions of others. They also reflect a community's consensus on the reciprocal obligations that define it as a community. A license is a statement of values, in legally executable form, adapted for daily use. As such, a license must be designed, much as the software and hardware that open-source developers create. Sometimes an existing license is fit to purpose and can be adopted without extensive discussion. However, often the technical and social needs of a community do not precisely map onto existing licenses, or the community itself is divided about the norms a license should enforce. In these cases of breakdown, the community itself must debate and design its license, using the same social processes it uses to debate and design the other infrastructure it relies on and the final goods it creates. In this Article, we analyze four case studies of controversy over license design in open-source software and hardware ecosystems. We draw on Stewart Brand's How Buildings Learn, a study of how physical buildings change over time as they are adapted and repurposed to deal with new circumstances by successive generations of users. Similarly, we describe how open-source licenses are adapted and repurposed by different communities confronting challenges. Debates over license drafting and interpretation are a key mechanism of achieving the necessary consensus for successful collaboration. The resulting licenses are the visible traces of the constant political work that sustains open-source collaboration. Successful licenses, like successful buildings, require ongoing maintenance, and the record of license changes over the years is a history of the communities that have inhabited them. [ABSTRACT FROM AUTHOR]

Pirated software is an attractive choice for cybercriminals seeking to spread malicious software, known as malware. This paper attempts to quantify the occurrence of malware concealed within pirated software. We collected samples of pirated software from various sources from Southeast Asian countries, including hard disk drives, optical discs purchased in eight different countries, and online platforms using peer-to-peer services. Our dataset comprises a total of 750 pirated software samples. To analyze these samples, we employed seven distinct antivirus (AV) engines. The malware identified by the AV engines was classified into four categories: adware, Trojans, viruses, and a miscellaneous category termed others. Our findings reveal that adware and Trojans are the most prevalent types of malware, with average infection rates of 34% and 35%, respectively, among our pirated software samples. Notably, our evaluation of AV detection performance highlights variations in sensitivity, ranging from a high of 132% to a low of 30% across all AV engines. Furthermore, upon installing pirated software, the most adversely affected operating system settings are the firewall and user account control configurations. Given the potential for malware to steal information or create malicious backdoors, its high prevalence within pirated software poses a substantial security risk to end users.

Amid the withdrawal of foreign software developers from the Russian market, policymakers continue to discuss the option of legalising software parallel import. The purpose of the article is to assess the expected effects of the software parallel import introduction. The methodological basis of the study is industrial organization theory and new institutional economics theory. Among the research methods are theoretical and mathematical modelling and comparative analysis. The empirical evidence includes publicly available data on the pirated content consumption retrieved from the web source I Know (iknowwhatyoudownload.com) and financial statements of Russian software developers (SPARK-Interfax database). The article presents a theoretical model describing the consumer’s choice between original and pirated versions of programs and analyses the situation, where the most popular software product gets inaccessible. Our analysis shows that when costs of piracy are low, users of ‘out-of-the-box’ programs will not switch to another software and prefer the pirated version of the previously used program. The results of the model are compared with data on the pirated software consumption in Russia. We have found that consumers prefer foreign software to Russian counterparts, which means that if parallel import is legalised, Russian software developers will fail to occupy the niche of home-use software, which remains an important reserve for the industry’s further development. Free access to foreign software distorts consumer incentives. The institution of parallel imports should be modified so that the consumer keeps paying the price of the original’s, the state guarantees such software is secure, and intellectual property violations are strictly controlled.

We study how private intellectual property rights protection affects equilibrium prices in a duopoly competition between firms that offer a product variety of distinct qualities (vertical product differentiation) in a setup that is closely related to that put forward by Jain where firms offer the same qualities in equilibrium (horizontal product differentiation). Consumers may make a choice to buy a legal version, use an illegal copy (if they want to and can), or not use a product at all. Using an illegal version violates intellectual property rights protection and is thus punishable when discovered. Thus, both private and public (copyright) intellectual property rights protection are available on scene. [ABSTRACT FROM AUTHOR]

In this paper, we attempt to investigate the effect of usage of pirated software on productivity by utilizing three distinct indicators of productivity for developing countries and an unbalanced panel data set containing the years between 2003 and 2017. Our hypothesis claims that increase in the use of pirated software enhance the productivity level in developing countries. Firstly, we implemented univariate analyses and the results of univariate analyses implied that the usage of pirated software and productivity are positively and significantly related in all of three models. Secondly, three control variables (i.e., education, investment, and openness) were employed in our three productivity models in order to check if the findings of univariate analyses remain valid and robust. After inclusion of the three control variables, the statistically significant positive association between the use of pirated software and productivity were observed in all models. Regarding to control variables, they take expected signs whenever they are statistically significant. Based on the estimated coefficients of PROD1 models, a 1% positive change in usage of pirated software leads to a rise in a growth in productivity by 0.1023% and 0.0623% in univariate and multivariate models, respectively while a 1% positive change in usage of pirated software leads to a rise in a growth in productivity by 0.1072% and 0.0684% in univariate and multivariate models of PROD2, respectively. Besides a 1% positive change in usage of pirated software induce to an increase in productivity level by 0.0881 and by 0.0601, respectively in PROD3 models. Secondly, three control variables, namely education, investment, and openness, were included in our three productivity models to test if the finding of univariate analyses keeps its validity. EDUC, INVEST, and OPEN variables are taken the anticipated positive influences and they are statistically significant at least at the level of 5%. More specifically, in estimation results for PROD1 model, one percent increase in EDUC, INVEST, and OPEN variables cause to a jump in productivity by 0.2309%, 0.1198%, and 0.7359%, respectively. Openness of an economy has the most explanatory power among all independent variables. In addition to PROD1 model, in estimation results for PROD2 model, one percent increase in EDUC, INVEST, and OPEN variables cause to a jump in productivity by 0.2482%, 0.0893%, and 0.7211%, respectively. Besides a 1% positive change in EDUC, INVEST, and OPEN variables induce to an increase in productivity level by 0.3345%, 0.0341%, and 0.2238%, respectively in PROD3 models. [ABSTRACT FROM AUTHOR]

Recent developments in computer networks and Internet of Things (IoT) have enabled easy access to data. But the government and business sectors face several difficulties in resolving cybersecurity network issues, like novel attacks, hackers, internet criminals, and so on. Presently, malware attacks and software piracy pose serious risks in compromising the security of IoT. They can steal confidential data which results in financial and reputational losses. The advent of machine learning (ML) and deep learning (DL) models has been employed to accomplish security in the IoT cloud environment. This article presents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection (EAGTODL-CTD) in IoT Cloud Networks. The presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud environment. The proposed EAGTODL-CTD model mainly focuses on the conversion of input binary files to color images, where the malware can be detected using an image classification problem. The EAGTODL-CTD model pre-processes the input data to transform to a compatible format. For threat detection and classification, cascaded gated recurrent unit (CGRU) model is exploited to determine class labels. Finally, EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters, showing the novelty of our work. The performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and benign. The experimental values reported the supremacy of the EAGTODL- CTD model with increased accuracy of 99.47%. [ABSTRACT FROM AUTHOR]

While the intention and participation in media and software piracy have been widely investigated, little is known concerning the determinants associated with electronic book piracy. With the adoption of e-book reader devices, Internet piracy remains a global concern that impacts multiple stakeholders, including authors, publishers, and libraries. The current study aims to examine the factors associated with electronic book piracy among college students. Results indicate that peer association and perceived risk are significantly linked to one's participation in e-book piracy. However, techniques of neutralization are not statistically significant. Libraries and publishers must educate students and raise awareness about the potential outcomes and risks related to e-book piracy. [ABSTRACT FROM AUTHOR]

Applications for the Internet of Things (IoT) have been employed in a broad range of industries, including smart homes, healthcare, smart energy, and Industrial 4.0. IoT offers many advantages, such as ease and efficiency, but it also adds a lot of new risks. The problem is often made worse by the potential number of linked IoT devices and the ad hoc nature of such networks. IoT management now faces substantial difficulties related to safety and confidentiality. Current research has shown that deep learning algorithms are quite effective and have numerous benefits over previous approaches for doing security assessments of IoT devices. An integrated deep learning approach is created in this work to identify files containing malware and pirated software through the Internet of Things network The Hybrid Dual-Channel Convolution Neural Network (DCCNN) with Spider Monkey Optimization (SMO) is called DCCNN-SMO advocated leveraging stolen reference code to detect software piracy. For the purpose of examining software piracy, the dataset was gathered via Google Code Jam (GCJ). To research the suggested strategy, collected 100 input data for internet users from GCJ. In addition, the DCCNN-SMO is utilized to visualize colored images to identify harmful intrusions in IoT networks. From the Leopard Mobile database, malware samples were taken for testing. The actual results demonstrate that the recommended method for calculating the cyber security risks in IoT performs categorization more over current approaches. The proposed DCCNN-SMO + SVM approach provides better resultant value of 98.55% whereas the other existing approaches like GIST + SVM is 86.1%, CLGM + SVM is 92.06% and DNN + SVM is 97.46%, LBP + SVM is 78.05%.

Piracy is a significant source of concern facing software developers, music labels, and movie production companies, to name a few. Digital goods producers and government entities argue that there are victims of piracy, whereas pirates may perceive their actions to be victimless. Regarding implications of our research, we extend the theory of planned behavior (TPB) by theorizing that attitudes, subjective norms, and perceived behavioral control could influence perceptions of moral obligations as a consequence of the desire to rationalize unethical behavior. Unlike prior literature, we manipulate the rationalization of moral obligations due to the victimless view toward piracy and show how moral obligations become important determinants of piracy behavior. Accordingly, our demonstrated malleability of morals may be an important path through which individuals are able to continue past behaviors. We also conduct a second study to identify the effect of implementing an educational message from a fictitious software company to exogenously nudge the pirate and influence the impact of perceived moral obligations on intentions to pirate. Our results show that the introduction of an exogenous educational message is an effective piracy mitigation strategy. [ABSTRACT FROM AUTHOR]

Why do mainstream consumers who would not typically engage in illegal behaviour routinely resort to online piracy of copyrighted software? This paper provides answers to this research query by applying routine activity theory and the theory of reasoned action. The paper’s study analyses consumers’ role as possible offenders that can have the opportunity to engage in online software piracy as part of their routine online activities. Although it is problematic to measure the exact magnitude of the negative impact on the US economy, as stated by the Government Accountability Office it is sizeable. After analysing the conceptual model using a US national consumer sample of over 700 consumers, results show the influence of proximity to motivated offenders, target suitability, and capable guardianship on consumers’ attitudes and perceived subjective norms towards online software piracy, as well as their intentions to engage in this illegal behaviour on the Internet. By integrating routine activity theory, a criminology theory with the theory of reasoned action from psychology and analysing a widespread online software piracy phenomenon, several academic and practical contributions are made. [ABSTRACT FROM AUTHOR]

Measures of ethical behaviours in research may be subject to social desirability bias (SDB). Most behavioural aspects of software piracy research are a subset of ethical research. Few studies of software piracy have explicitly addressed the issue of SDB. In this article, the extent of SDB that may be present in software piracy is empirically assessed. This was done by comparing SDB in software piracy under three conditions using the 40-item Balanced Inventory of Desirable Responding (BIDR) scale. First, software piracy behaviour was examined in an experimental setting with actual money at stake. This allowed subject behaviour to be observed rather than inferred from self-report responses. Second, a survey was conducted to test the extent of software piracy based on subject self-report responses. Third, the extent of software piracy was also assessed based on peer-report responses (i.e.: the subject reported whether their peers would pirate). A comparison of the responses in the three conditions allows us to draw inferences about SDB in software piracy research. Self-report surveys exhibited high levels of SDB. The behaviour in the experimental condition, and responses in the peer-report survey method, did not exhibit SDB. [ABSTRACT FROM AUTHOR]