932 resultados para sensori, embedded, raspberry, bluetooth, android


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Defence organisations perform information security evaluations to confirm that electronic communications devices are safe to use in security-critical situations. Such evaluations include tracing all possible dataflow paths through the device, but this process is tedious and error-prone, so automated reachability analysis tools are needed to make security evaluations faster and more accurate. Previous research has produced a tool, SIFA, for dataflow analysis of basic digital circuitry, but it cannot analyse dataflow through microprocessors embedded within the circuit since this depends on the software they run. We have developed a static analysis tool that produces SIFA compatible dataflow graphs from embedded microcontroller programs written in C. In this paper we present a case study which shows how this new capability supports combined hardware and software dataflow analyses of a security critical communications device.

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Data flow analysis techniques can be used to help assess threats to data confidentiality and integrity in security critical program code. However, a fundamental weakness of static analysis techniques is that they overestimate the ways in which data may propagate at run time. Discounting large numbers of these false-positive data flow paths wastes an information security evaluator's time and effort. Here we show how to automatically eliminate some false-positive data flow paths by precisely modelling how classified data is blocked by certain expressions in embedded C code. We present a library of detailed data flow models of individual expression elements and an algorithm for introducing these components into conventional data flow graphs. The resulting models can be used to accurately trace byte-level or even bit-level data flow through expressions that are normally treated as atomic. This allows us to identify expressions that safely downgrade their classified inputs and thereby eliminate false-positive data flow paths from the security evaluation process. To validate the approach we have implemented and tested it in an existing data flow analysis toolkit.

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A novel reduced-size microstrip rectangular patch antenna for Bluetooth operation is presented in this paper. The proposed antenna operates in the 2400 to 2484 MHz ISM Band. Although an air substrate is introduced, antenna occupies a small volume of 33.3×6.6×0.8 mm3. The gain and the impedance bandwidth of the antenna are predicted using a commercial Finite Element Method software package. The predicted results show good agreement with measured data.

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A double-layer rectangular patch microstrip antenna suitable for Bluetooth applications is investigated. The patch is etched on a separate substrate which is suspended above the ground plane and supported by an MCX connector. The air gap between the patch and the ground plane increases the impedance bandwidth and can be used to tune the resonant frequency. This paper presents experimental results on the effects of various parameters on the antenna characteristics and provides guidelines for the design of such an antenna.

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This chapter explores the changing intellectual landscapes and market-led research within academic institutions as the rise of 'embedded criminology' and argues for knowledges of resistance.

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This paper presents a new approach for network upgrading to improve the penetration level of Small Scale Generators in residential feeders. In this paper, it is proposed that a common DC link can be added to LV network to alleviate the negative impact of increased export power on AC lines, allowing customers to inject their surplus power with no restrictions to the common DC link. In addition, it is shown that the proposed approach can be a pathway from current AC network to future DC network.

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his study presents an improved method of dealing with embedded tax liabilities in portfolio choice. We argue that using a risk-free discount rate is appropriate for calculating the present value of future tax liabilities. Supportive of recent research, our results found a taxation-induced preference of holding equities over bonds, and a location preference of holding equities in the taxable account and bonds in retirement accounts. These important findings contrast with traditional investment advice which suggests a greater capacity for risk in retirement accounts.

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Our daily lives become more and more dependent upon smartphones due to their increased capabilities. Smartphones are used in various ways from payment systems to assisting the lives of elderly or disabled people. Security threats for these devices become increasingly dangerous since there is still a lack of proper security tools for protection. Android emerges as an open smartphone platform which allows modification even on operating system level. Therefore, third-party developers have the opportunity to develop kernel-based low-level security tools which is not normal for smartphone platforms. Android quickly gained its popularity among smartphone developers and even beyond since it bases on Java on top of "open" Linux in comparison to former proprietary platforms which have very restrictive SDKs and corresponding APIs. Symbian OS for example, holding the greatest market share among all smartphone OSs, was closing critical APIs to common developers and introduced application certification. This was done since this OS was the main target for smartphone malwares in the past. In fact, more than 290 malwares designed for Symbian OS appeared from July 2004 to July 2008. Android, in turn, promises to be completely open source. Together with the Linux-based smartphone OS OpenMoko, open smartphone platforms may attract malware writers for creating malicious applications endangering the critical smartphone applications and owners� privacy. In this work, we present our current results in analyzing the security of Android smartphones with a focus on its Linux side. Our results are not limited to Android, they are also applicable to Linux-based smartphones such as OpenMoko Neo FreeRunner. Our contribution in this work is three-fold. First, we analyze android framework and the Linux-kernel to check security functionalities. We survey wellaccepted security mechanisms and tools which can increase device security. We provide descriptions on how to adopt these security tools on Android kernel, and provide their overhead analysis in terms of resource usage. As open smartphones are released and may increase their market share similar to Symbian, they may attract attention of malware writers. Therefore, our second contribution focuses on malware detection techniques at the kernel level. We test applicability of existing signature and intrusion detection methods in Android environment. We focus on monitoring events on the kernel; that is, identifying critical kernel, log file, file system and network activity events, and devising efficient mechanisms to monitor them in a resource limited environment. Our third contribution involves initial results of our malware detection mechanism basing on static function call analysis. We identified approximately 105 Executable and Linking Format (ELF) executables installed to the Linux side of Android. We perform a statistical analysis on the function calls used by these applications. The results of the analysis can be compared to newly installed applications for detecting significant differences. Additionally, certain function calls indicate malicious activity. Therefore, we present a simple decision tree for deciding the suspiciousness of the corresponding application. Our results present a first step towards detecting malicious applications on Android-based devices.

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Smartphones are getting increasingly popular and several malwares appeared targeting these devices. General countermeasures to smartphone malwares are currently limited to signature-based antivirus scanners which efficiently detect known malwares, but they have serious shortcomings with new and unknown malwares creating a window of opportunity for attackers. As smartphones become host for sensitive data and applications, extended malware detection mechanisms are necessary complying with the corresponding resource constraints. The contribution of this paper is twofold. First, we perform static analysis on the executables to extract their function calls in Android environment using the command readelf. Function call lists are compared with malware executables for classifying them with PART, Prism and Nearest Neighbor Algorithms. Second, we present a collaborative malware detection approach to extend these results. Corresponding simulation results are presented.

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Smartphones started being targets for malware in June 2004 while malware count increased steadily until the introduction of a mandatory application signing mechanism for Symbian OS in 2006. From this point on, only few news could be read on this topic. Even despite of new emerging smartphone platforms, e.g. android and iPhone, malware writers seemed to lose interest in writing malware for smartphones giving users an unappropriate feeling of safety. In this paper, we revisit smartphone malware evolution for completing the appearance list until end of 2008. For contributing to smartphone malware research, we continue this list by adding descriptions on possible techniques for creating the first malware(s) for Android platform. Our approach involves usage of undocumented Android functions enabling us to execute native Linux application even on retail Android devices. This can be exploited to create malicious Linux applications and daemons using various methods to attack a device. In this manner, we also show that it is possible to bypass the Android permission system by using native Linux applications.

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Smartphones are steadily gaining popularity, creating new application areas as their capabilities increase in terms of computational power, sensors and communication. Emerging new features of mobile devices give opportunity to new threats. Android is one of the newer operating systems targeting smartphones. While being based on a Linux kernel, Android has unique properties and specific limitations due to its mobile nature. This makes it harder to detect and react upon malware attacks if using conventional techniques. In this paper, we propose an Android Application Sandbox (AASandbox) which is able to perform both static and dynamic analysis on Android programs to automatically detect suspicious applications. Static analysis scans the software for malicious patterns without installing it. Dynamic analysis executes the application in a fully isolated environment, i.e. sandbox, which intervenes and logs low-level interactions with the system for further analysis. Both the sandbox and the detection algorithms can be deployed in the cloud, providing a fast and distributed detection of suspicious software in a mobile software store akin to Google's Android Market. Additionally, AASandbox might be used to improve the efficiency of classical anti-virus applications available for the Android operating system.

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In the last decade, smartphones have gained widespread usage. Since the advent of online application stores, hundreds of thousands of applications have become instantly available to millions of smart-phone users. Within the Android ecosystem, application security is governed by digital signatures and a list of coarse-grained permissions. However, this mechanism is not fine-grained enough to provide the user with a sufficient means of control of the applications' activities. Abuse of highly sensible private information such as phone numbers without users' notice is the result. We show that there is a high frequency of privacy leaks even among widely popular applications. Together with the fact that the majority of the users are not proficient in computer security, this presents a challenge to the engineers developing security solutions for the platform. Our contribution is twofold: first, we propose a service which is able to assess Android Market applications via static analysis and provide detailed, but readable reports to the user. Second, we describe a means to mitigate security and privacy threats by automated reverse-engineering and refactoring binary application packages according to the users' security preferences.

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Private data stored on smartphones is a precious target for malware attacks. A constantly changing environment, e.g. switching network connections, can cause unpredictable threats, and require an adaptive approach to access control. Context-based access control is using dynamic environmental information, including it into access decisions. We propose an "ecosystem-in-an-ecosystem" which acts as a secure container for trusted software aiming at enterprise scenarios where users are allowed to use private devices. We have implemented a proof-of-concept prototype for an access control framework that processes changes to low-level sensors and semantically enriches them, adapting access control policies to the current context. This allows the user or the administrator to maintain fine-grained control over resource usage by compliant applications. Hence, resources local to the trusted container remain under control of the enterprise policy. Our results show that context-based access control can be done on smartphones without major performance impact.

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Smartphones get increasingly popular where more and more smartphone platforms emerge. Special attention was gained by the open source platform Android which was presented by the Open Handset Alliance (OHA) hosting members like Google, Motorola, and HTC. Android uses a Linux kernel and a stripped-down userland with a custom Java VM set on top. The resulting system joins the advantages of both environments, while third-parties are intended to develop only Java applications at the moment. In this work, we present the benefit of using native applications in Android. Android includes a fully functional Linux, and using it for heavy computational tasks when developing applications can bring in substantional performance increase. We present how to develop native applications and software components, as well as how to let Linux applications and components communicate with Java programs. Additionally, we present performance measurements of native and Java applications executing identical tasks. The results show that native C applications can be up to 30 times as fast as an identical algorithm running in Dalvik VM. Java applications can become a speed-up of up to 10 times if utilizing JNI.

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Google Android, Google's new product and its first attempt to enter the mobile market, might have an equal impact on mobile users like Apple's hyped product, the iPhone. In this Technical report we are going to present the Google Android platform, what Android is, describe why it might be considered as a worthy rival to Apple's iPhone. We will describe parts of its internals, take a look "under the hood" while explaining components of the underlying operating system. We will show how to develop applications for this platform, which difficulties a developer might have to face, and how developers can possibly use other programming languages to develop for Android than the propagated language Java.