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Author

Anmol Sheth

Other affiliations: University of Colorado Boulder
Bio: Anmol Sheth is an academic researcher from Intel. The author has contributed to research in topics: Wireless network & Wireless. The author has an hindex of 30, co-authored 45 publications receiving 10186 citations. Previous affiliations of Anmol Sheth include University of Colorado Boulder.

Papers
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Journal ArticleDOI
TL;DR: TaintDroid as mentioned in this paper is an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data by leveraging Android's virtualized execution environment.
Abstract: Today’s smartphone operating systems frequently fail to provide users with visibility into how third-party applications collect and share their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid enables realtime analysis by leveraging Android’s virtualized execution environment. TaintDroid incurs only 32p performance overhead on a CPU-bound microbenchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, in our 2010 study we found 20 applications potentially misused users’ private information; so did a similar fraction of the tested applications in our 2012 study. Monitoring the flow of privacy-sensitive data with TaintDroid provides valuable input for smartphone users and security service firms seeking to identify misbehaving applications.

2,983 citations

Proceedings ArticleDOI
04 Oct 2010
TL;DR: Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, this work found 68 instances of misappropriation of users' location and device identification information across 20 applications.
Abstract: Today's smartphone operating systems frequently fail to provide users with adequate control over and visibility into how third-party applications use their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid provides realtime analysis by leveraging Android's virtualized execution environment. TaintDroid incurs only 14% performance overhead on a CPU-bound micro-benchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, we found 68 instances of potential misuse of users' private information across 20 applications. Monitoring sensitive data with TaintDroid provides informed use of third-party applications for phone users and valuable input for smartphone security service firms seeking to identify misbehaving applications.

2,379 citations

Journal ArticleDOI
22 Jan 2011
TL;DR: The measurement setup comprises the customized versions of Intel's close-source firmware and open-source iwlwifi wireless driver, userspace tools to enable these measurements, access point functionality for controlling both ends of the link, and Matlab scripts for data analysis.
Abstract: We are pleased to announce the release of a tool that records detailed measurements of the wireless channel along with received 802.11 packet traces. It runs on a commodity 802.11n NIC, and records Channel State Information (CSI) based on the 802.11 standard. Unlike Receive Signal Strength Indicator (RSSI) values, which merely capture the total power received at the listener, the CSI contains information about the channel between sender and receiver at the level of individual data subcarriers, for each pair of transmit and receive antennas.Our toolkit uses the Intel WiFi Link 5300 wireless NIC with 3 antennas. It works on up-to-date Linux operating systems: in our testbed we use Ubuntu 10.04 LTS with the 2.6.36 kernel. The measurement setup comprises our customized versions of Intel's close-source firmware and open-source iwlwifi wireless driver, userspace tools to enable these measurements, access point functionality for controlling both ends of the link, and Matlab (or Octave) scripts for data analysis. We are releasing the binary of the modified firmware, and the source code to all the other components.

1,354 citations

Proceedings ArticleDOI
30 Aug 2010
TL;DR: It is shown that, for the first time, wireless packet delivery can be accurately predicted for commodity 802.11 NICs from only the channel measurements that they provide, and the rate prediction is as good as the best rate adaptation algorithms for 802.
Abstract: RSSI is known to be a fickle indicator of whether a wireless link will work, for many reasons. This greatly complicates operation because it requires testing and adaptation to find the best rate, transmit power or other parameter that is tuned to boost performance. We show that, for the first time, wireless packet delivery can be accurately predicted for commodity 802.11 NICs from only the channel measurements that they provide. Our model uses 802.11n Channel State Information measurements as input to an OFDM receiver model we develop by using the concept of effective SNR. It is simple, easy to deploy, broadly useful, and accurate. It makes packet delivery predictions for 802.11a/g SISO rates and 802.11n MIMO rates, plus choices of transmit power and antennas. We report testbed experiments that show narrow transition regions (

697 citations

Journal ArticleDOI
TL;DR: The MANTIS MultimodAl system for NeTworks of In-situ wireless Sensors provides a new multithreaded cross-platform embedded operating system for wireless sensor networks that enables micro sensor nodes to natively interleave complex tasks with time-sensitive tasks, thereby mitigating the bounded buffer producer-consumer problem.
Abstract: The MANTIS MultimodAl system for NeTworks of In-situ wireless Sensors provides a new multithreaded cross-platform embedded operating system for wireless sensor networks. As sensor networks accommodate increasingly complex tasks such as compression/aggregation and signal processing, preemptive multithreading in the MANTIS sensor OS (MOS) enables micro sensor nodes to natively interleave complex tasks with time-sensitive tasks, thereby mitigating the bounded buffer producer-consumer problem. To achieve memory efficiency, MOS is implemented in a lightweight RAM footprint that fits in less than 500 bytes of memory, including kernel, scheduler, and network stack. To achieve energy efficiency, the MOS power-efficient scheduler sleeps the microcontroller after all active threads have called the MOS sleep() function, reducing current consumption to the µA range. A key MOS design feature is flexibility in the form of cross-platform support and testing across PCs, PDAs, and different micro sensor platforms. Another key MOS design feature is support for remote management of in-situ sensors via dynamic reprogramming and remote login.

489 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: TaintDroid as mentioned in this paper is an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data by leveraging Android's virtualized execution environment.
Abstract: Today’s smartphone operating systems frequently fail to provide users with visibility into how third-party applications collect and share their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid enables realtime analysis by leveraging Android’s virtualized execution environment. TaintDroid incurs only 32p performance overhead on a CPU-bound microbenchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, in our 2010 study we found 20 applications potentially misused users’ private information; so did a similar fraction of the tested applications in our 2012 study. Monitoring the flow of privacy-sensitive data with TaintDroid provides valuable input for smartphone users and security service firms seeking to identify misbehaving applications.

2,983 citations

Proceedings ArticleDOI
16 Nov 2004
TL;DR: This work presents Contiki, a lightweight operating system with support for dynamic loading and replacement of individual programs and services, built around an event-driven kernel but provides optional preemptive multithreading that can be applied to individual processes.
Abstract: Wireless sensor networks are composed of large numbers of tiny networked devices that communicate untethered. For large scale networks, it is important to be able to download code into the network dynamically. We present Contiki, a lightweight operating system with support for dynamic loading and replacement of individual programs and services. Contiki is built around an event-driven kernel but provides optional preemptive multithreading that can be applied to individual processes. We show that dynamic loading and unloading is feasible in a resource constrained environment, while keeping the base system lightweight and compact.

2,566 citations

Proceedings ArticleDOI
04 Oct 2010
TL;DR: Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, this work found 68 instances of misappropriation of users' location and device identification information across 20 applications.
Abstract: Today's smartphone operating systems frequently fail to provide users with adequate control over and visibility into how third-party applications use their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid provides realtime analysis by leveraging Android's virtualized execution environment. TaintDroid incurs only 14% performance overhead on a CPU-bound micro-benchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, we found 68 instances of potential misuse of users' private information across 20 applications. Monitoring sensitive data with TaintDroid provides informed use of third-party applications for phone users and valuable input for smartphone security service firms seeking to identify misbehaving applications.

2,379 citations

Proceedings ArticleDOI
20 May 2012
TL;DR: Systematize or characterize existing Android malware from various aspects, including their installation methods, activation mechanisms as well as the nature of carried malicious payloads reveal that they are evolving rapidly to circumvent the detection from existing mobile anti-virus software.
Abstract: The popularity and adoption of smart phones has greatly stimulated the spread of mobile malware, especially on the popular platforms such as Android. In light of their rapid growth, there is a pressing need to develop effective solutions. However, our defense capability is largely constrained by the limited understanding of these emerging mobile malware and the lack of timely access to related samples. In this paper, we focus on the Android platform and aim to systematize or characterize existing Android malware. Particularly, with more than one year effort, we have managed to collect more than 1,200 malware samples that cover the majority of existing Android malware families, ranging from their debut in August 2010 to recent ones in October 2011. In addition, we systematically characterize them from various aspects, including their installation methods, activation mechanisms as well as the nature of carried malicious payloads. The characterization and a subsequent evolution-based study of representative families reveal that they are evolving rapidly to circumvent the detection from existing mobile anti-virus software. Based on the evaluation with four representative mobile security software, our experiments show that the best case detects 79.6% of them while the worst case detects only 20.2% in our dataset. These results clearly call for the need to better develop next-generation anti-mobile-malware solutions.

2,122 citations

Proceedings ArticleDOI
01 Jan 2014
TL;DR: DREBIN is proposed, a lightweight method for detection of Android malware that enables identifying malicious applications directly on the smartphone and outperforms several related approaches and detects 94% of the malware with few false alarms.
Abstract: Malicious applications pose a threat to the security of the Android platform. The growing amount and diversity of these applications render conventional defenses largely ineffective and thus Android smartphones often remain unprotected from novel malware. In this paper, we propose DREBIN, a lightweight method for detection of Android malware that enables identifying malicious applications directly on the smartphone. As the limited resources impede monitoring applications at run-time, DREBIN performs a broad static analysis, gathering as many features of an application as possible. These features are embedded in a joint vector space, such that typical patterns indicative for malware can be automatically identified and used for explaining the decisions of our method. In an evaluation with 123,453 applications and 5,560 malware samples DREBIN outperforms several related approaches and detects 94% of the malware with few false alarms, where the explanations provided for each detection reveal relevant properties of the detected malware. On five popular smartphones, the method requires 10 seconds for an analysis on average, rendering it suitable for checking downloaded applications directly on the device.

1,905 citations