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With the mushrooming of wireless access infrastructures, the amount of data generated, transferred and consumed by the users of such networks has taken enormous proportions. This fact further complicates the task of network intrusion detection, especially when advanced machine learning (ML) operations are involved in the process. In wireless environments, the monitored data are naturally distributed among the numerous sensor nodes of the system. Therefore, the analysis of data must either happen in a central location after first collecting it from the sensors or locally through collaboration by viewing the problem through a distributed ML perspective. In both cases, concerns are risen regarding the requirements of this demanding task in matters of required network resources and achieved security/privacy. This paper proposes TermID, a distributed network intrusion detection system that is well suited for wireless networks. The system is based on classification rule induction and swarm intelligence principles to achieve efficient model training for intrusion detection purposes, without exchanging sensitive data. An additional achievement is that the produced model is easily readable by humans. While these are the main design principles of our approach, the accuracy of the produced model is not compromised by the distribution of the tasks and remains at competitive levels. Both the aforementioned claims are verified by the results of detailed experiments withheld with the use of a publicly available security-focused wireless dataset. [ABSTRACT FROM AUTHOR]

In this paper, we describe the design and implementation of an audio wiki application accessible via both the Public Switched Telephone Network and the Internet. The application exploits mature World Wide Web Consortium standards, such as VoiceXML, Speech Synthesis Markup Language, and Speech Recognition Grammar Specification toward achieving our goals. The purpose of such an application is to assist visually impaired, technologically uneducated, and underprivileged people in accessing information originally intended to be accessed visually via a personal computer (PC). Users may access wiki content via fixed or mobile phones, or via a PC using a Web Browser or a Voice over IP service. This feature promotes pervasiveness to collaboratively created content to an extremely large population, i.e., those who simply own a telephone line. [ABSTRACT FROM AUTHOR]