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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]

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]

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%.

Internet of Things (IoT) -based systems need to be up to date on cybersecurity threats. The security of IoT networks is challenged by software piracy and malware attacks, and much important information can be stolen and used for cybercrimes. This paper attempts to improve IoT cybersecurity by proposing a combined model based on deep learning to detect malware and software piracy across the IoT network. The malware’s model is based on Deep Convolutional Neural Networks (DCNNs). Apart from this, TensorFlow Deep Neural Networks (TFDNNs) are introduced to detect software piracy threats according to source code plagiarism. The investigation is conducted on the Google Code Jam (GCJ) dataset. The conducted experiments prove that the classification performance achieves high accuracy of about 98%.

The IoT (Internet of Things) connect systems, applications, data storage, and services that may be a new gateway for cyber-attacks as they continuously offer services in the organization. Currently, software piracy and malware attacks are high risks to compromise the security of IoT. These threats may steal important information that causes economic and reputational damages. In this paper, we have proposed a combined deep learning approach to detect the pirated software and malware-infected files across the IoT network. The TensorFlow deep neural network is proposed to identify pirated software using source code plagiarism. The tokenization and weighting feature methods are used to filter the noisy data and further, to zoom the importance of each token in terms of source code plagiarism. Then, the deep learning approach is used to detect source code plagiarism. The dataset is collected from Google Code Jam (GCJ) to investigate software piracy. Apart from this, the deep convolutional neural network is used to detect malicious infections in IoT network through color image visualization. The malware samples are obtained from Maling dataset for experimentation. The experimental results indicate that the classification performance of the proposed solution to measure the cybersecurity threats in IoT are better than the state of the art methods.

Abstract. Many studies have investigated why users engage in digital piracy. The theoretical explanations are particularistic, and the empirical findings are fragmented, sometimes divergent, and reveal unexplained variations of effects. [...]

Software piracy has numerous negative effects. The concern of software piracy is not limited to software developers but also educational institutions. The purpose of the current study was to investigate the factors contributing to the behavioral intention of Generation Z students' to comply with educational software anti-piracy laws by utilizing the Protection Motivation Theory (PMT). The results indicated that self-efficacy and response efficacy had significant relationships with the behavioral intention of Generation Z's intention to comply with educational software anti-piracy laws. Several insights from practical and managerial perspective were also discussed. In the end, software developer and academic institutions could use this model to promote anti-piracy behavior in the future. [ABSTRACT FROM AUTHOR]

This paper studies the determinants of software piracy losses along five major macro­economic dimensions: Technological, Educational, Institutional, Access to Information and Labor force. The study was conducted based on a large dataset available from 1995 to 2010 and comprising 81 countries. As for the Technological dimension, more patents by residents increases piracy losses while the effect of R&D is the opposite (decreases piracy losses). In terms of the Educational dimension, the results show that more spending on education increase the piracy losses but, at the same time, more schooling years have the contrary effect. Concerning the Institutional dimension, nations with less corruption have lower piracy levels. Regarding the Access to Information, it seems that access to Internet diminishes the losses while the share of Internet broadband subscriptions has no effect. The results also show that, regarding the Labor dimension, employment in services has a deterrent effect while labor force with higher education and youth unemployment increases piracy losses. First published online 20 October 2015

Software piracy is a modern day war between malicious software pirates and software developers. Annually, developers lose billions of dollars in revenue to piracy, making it an industry bane that must be controlled by all means. The drive for improved software protection systems has increased the complexity of both proposed and implemented schemes. Some of the methods are cost intensive in terms of development, management and hardware requirement (smart card tokens), while this may be justified for costly software applications, for low cost and basic applications by small scale developers, implementing advanced protection schemes is often economically infeasible and largely and overkill. This study presents a subtle software protection model using serial keys. The model implements a form of obfuscation by using hidden codes, encrypted functions and uses a distraction technique by diverting the attention of potential hackers to the serial key while trickily using coded strings for the actual user authentication. [ABSTRACT FROM AUTHOR]

Software piracy is globally a major issue of using software without proper permission as enforced by the software license and agreement. Currently, software industries and organizations face a huge loss of money due to software piracy. Most of the educational institutions/organizations have also been affected from software piracy. To facilitate educational institutions regarding software piracy, it would be ideal to have a detailed study of the awareness of piracy level in education institutions for future development and to help academia to get rid of problems occur from pirated software. The present research aims to study and determine the awareness of piracy level in educational institutions. This study will eventually help academia in determination of level of piracy and awareness of pirated software. [ABSTRACT FROM AUTHOR]

This paper studies the determinants of software piracy losses along five major macroeconomic dimensions: Technological, Educational, Institutional, Access to Information and Labor force. The study was conducted based on a large dataset available from 1995 to 2010 and comprising 81 countries. As for the Technological dimension, more patents by residents increases piracy losses while the effect of R&D is the opposite (decreases piracy losses). In terms of the Educational dimension, the results show that more spending on education increase the piracy losses but, at the same time, more schooling years have the contrary effect. Concerning the Institutional dimension, nations with less corruption have lower piracy levels. Regarding the Access to Information, it seems that access to Internet diminishes the losses while the share of Internet broadband subscriptions has no effect. The results also show that, regarding the Labor dimension, employment in services has a deterrent effect while labor force with higher education and youth unemployment increases piracy losses. [ABSTRACT FROM AUTHOR]

This paper develops an optimization model for pricing a monopolistic application software in the presence of piracy. The purpose is raising revenue produced by product’s sale with determining prices in a price skimming strategy and minimizing amount of piracy. The model is a multifunctional price skimming optimization with simplex method which accompanied by a deterministic method for calculating time intervals of each segment. A linear function is used to describe demand of each segment. In addition, a linear piracy function is proposed to make piracy a dynamic parameter. The model has the ability to apply penetration pricing and controlling market share. Rough estimates of Windows 7 sale’s parameters are used to apply in the model. Optimizing case of Windows 7 is resulted in 7.3 percent increase in revenue while value of net market share is virtually constant. Therefore, the developed model demonstrates its competence in optimizing revenue by determining prices with presence of piracy. Results of the research show that to tackle piracy, range of price skimming must be decreased in a way that highest price need to be intensely reduced while lowest one must be slightly reduced. The benefit of using this strategy, is incurring lowest revenue loss due to piracy. The Effects of an escalation in piracy on proposed optimization model include increase in number of sale, demand, selling portion, market share, and decrease in price, price difference between segments, and revenue.

Background: Higher education is increasingly making use of information and communication technology (ICT) to deliver educational services. Young adults at higher educational institutions are also making use of ICTs in their daily lives but are not taught how to do so ethically. Software piracy, plagiarism and cheating, while making use of ICTs, are the most common ethical dilemmas that will face digital natives. Objective: The purpose of this article was to investigate information ethics of young adults at a higher education institution in the Eastern Cape Province of South Africa. Method: This study made use of a positive, quantitative survey approach. A closed-ended questionnaire was distributed to a group of 312 first-year students, who had registered for a computer literacy class. A response rate of 69.2% was recorded, resulting in 216 students participating in the study. The results were analysed using descriptive and inferential (t-tests) statistics in SPSS V22. Results: The results indicated that plagiarism is a problem among first-year students, and elements of authorship should be included in the curriculum. Students understood what software piracy was but did not think it was wrong to copy software from the Internet. Finally, the students understood that cheating, while making use of technology, is wrong and should be avoided. Conclusion: The recommendation of the study then is that information ethics must be included in the undergraduate curriculum in order to prepare students to deal with these ethical problems.

Using the data collected from several universities in China and the United States, which have drastically different piracy rates, economic development stages, income levels, and national cultures, this study aims to identify which demographic factors affect software piracy significantly. Totally, the dataset includes 600 valid responses. Multivariate data analysis, supported by Multivariate Analysis of Variance and Turkey's test, reveals that among various factors, country difference and gender most significantly influence software piracy. In addition, this study offers recommendations on how to curb software piracy effectively. [ABSTRACT FROM AUTHOR]

CodeMist is an innovative security framework that leverages both passive and active approaches to prevent piracy of cloud-based rental software. [ABSTRACT FROM AUTHOR]