2,941 research outputs found

    Adware, Shareware, and Consumer Privacy

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    Programmers can distribute new software to online users either for a fee as shareware or bundle it with advertising banners and distribute it for free as adware. In this paper we study the programmers' choice between these two modes of distribution in the context of a model that take explicit account of the strategic interaction between programmers who develop software, firms that advertise their products through ad banners, and consumers who buy software and consumer products. Adware allows advertisers to send targeted information to specific consumers and may therefore improve their purchasing decisions. At the same time, adware also raises privacy concerns. We study the effect of programmers' choice between shareware and adware on consumers' welfare through its effect on the beneficial information that consumers receive about consumers products on the one hand and their loss of privacy on the other hand. We also examine the implications of improvements in the technology of ad banners and the desirability of bans on the use of adware

    Mal-Netminer: Malware Classification Approach based on Social Network Analysis of System Call Graph

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    As the security landscape evolves over time, where thousands of species of malicious codes are seen every day, antivirus vendors strive to detect and classify malware families for efficient and effective responses against malware campaigns. To enrich this effort, and by capitalizing on ideas from the social network analysis domain, we build a tool that can help classify malware families using features driven from the graph structure of their system calls. To achieve that, we first construct a system call graph that consists of system calls found in the execution of the individual malware families. To explore distinguishing features of various malware species, we study social network properties as applied to the call graph, including the degree distribution, degree centrality, average distance, clustering coefficient, network density, and component ratio. We utilize features driven from those properties to build a classifier for malware families. Our experimental results show that influence-based graph metrics such as the degree centrality are effective for classifying malware, whereas the general structural metrics of malware are less effective for classifying malware. Our experiments demonstrate that the proposed system performs well in detecting and classifying malware families within each malware class with accuracy greater than 96%.Comment: Mathematical Problems in Engineering, Vol 201

    Impact Analysis of Malware Based on Call Network API with Heuristic Detection Method

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    Malware is a program that has a negative influence on computer systems that don\u27t have user permissions. The purpose of making malware by hackers is to get profits in an illegal way. Therefore, we need a malware analysis. Malware analysis aims to determine the specifics of malware so that security can be built to protect computer devices. One method for analyzing malware is heuristic detection. Heuristic detection is an analytical method that allows finding new types of malware in a file or application. Many malwares are made to attack through the internet because of technological advancements. Based on these conditions, the malware analysis is carried out using the API call network with the heuristic detection method. This aims to identify the behavior of malware that attacks the network. The results of the analysis carried out are that most malware is spyware, which is lurking user activity and retrieving user data without the user\u27s knowledge. In addition, there is also malware that is adware, which displays advertisements through pop-up windows on computer devices that interfaces with user activity. So that with these results, it can also be identified actions that can be taken by the user to protect his computer device, such as by installing antivirus or antimalware, not downloading unauthorized applications and not accessing unsafe websites. &nbsp

    PREVENTING PERVASIVE THREATS TO NETWORK WITH POWER LAW

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    Research have studied numerous means of compute size adware and spyware and spyware and adware and spyware and adware and adware and spyware which studies will indicate that size bot nets can transform from millions to volume of thousands and you will find no leading concepts to create apparent these variation. Within our work we inspect how adware and spyware and spyware and adware and spyware and adware and adware and spyware propagate within systems from global perspective and rigorous two layer epidemic representation for adware and spyware and spyware and adware and spyware and adware and adware and spyware distribution from network to network.  Based on forecasted representation, our analysis indicate that distribution of provided adware and spyware and spyware and adware and spyware and adware and adware and spyware follows exponential distribution, the distribution of power law acquiring a short exponential tail, additionally to power law distribution at its initial, late additionally to final stages, correspondingly. The suggested type of two layer adware and spyware and spyware and adware and spyware and adware and adware and spyware propagation explains development of specified adware and spyware and spyware and adware and spyware and adware and adware and spyware at Internet level applying this two layer representation, we determine entire volume of compromised hosts additionally for distribution concerning systems

    Coddling Spies: Why the Law Doesn’t Adequately Address Computer Spyware

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    Consumers and businesses have attempted to use the common law of torts as well as federal statutes like the Computer Fraud and Abuse Act, the Stored Wire and Electronic Communications and Transactional Records Act, and the Wiretap Act to address the expanding problem of spyware. Spyware, which consists of software applications inserted into another\u27s computer to report a user\u27s activity to an outsider, is as innocuous as tracking purchases or as sinister as stealing trade secrets or an individual\u27s identity. Existing law does not address spyware adequately because authorization language, buried in click-through boilerplate, renders much of current law useless. Congress must act to make spyware companies disclose their intentions with conspicuous and clearly-stated warnings
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