The PASCAL Visual Object Classes Challenge

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.

/pdf/taintdroid-an-information-flow-tracking-system-for-realtime-5d8smsr0iy.pdf

TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones

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.

/pdf/taintdroid-an-information-flow-tracking-system-for-realtime-3fnz9lwdso.pdf

TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones

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.

/pdf/dissecting-android-malware-characterization-and-evolution-2euhdmm33o.pdf

Dissecting Android Malware: Characterization and Evolution

コンピュータ・サイエンス : ACM computing surveys

Anti-virus vendors are confronted with a multitude of potentially malicious samples today. Receiving thousands of new samples every day is not uncommon. The signatures that detect confirmed malicious threats are mainly still created manually, so it is important to discriminate between samples that pose a new unknown threat and those that are mere variants of known malware.This survey article provides an overview of techniques based on dynamic analysis that are used to analyze potentially malicious samples. It also covers analysis programs that leverage these It also covers analysis programs that employ these techniques to assist human analysts in assessing, in a timely and appropriate manner, whether a given sample deserves closer manual inspection due to its unknown malicious behavior.

/pdf/a-survey-on-automated-dynamic-malware-analysis-techniques-2b10kp3xz0.pdf

A survey on automated dynamic malware-analysis techniques and tools

Malicious programs spy on users' behavior and compromise their privacy. Even software from reputable vendors, such as Google Desktop and Sony DRM media player, may perform undesirable actions. Unfortunately, existing techniques for detecting malware and analyzing unknown code samples are insufficient and have significant shortcomings. We observe that malicious information access and processing behavior is the fundamental trait of numerous malware categories breaching users' privacy (including keyloggers, password thieves, network sniffers, stealth backdoors, spyware and rootkits), which separates these malicious applications from benign software. We propose a system, Panorama, to detect and analyze malware by capturing this fundamental trait. In our extensive experiments, Panorama successfully detected all the malware samples and had very few false positives. Furthermore, by using Google Desktop as a case study, we show that our system can accurately capture its information access and processing behavior, and we can confirm that it does send back sensitive information to remote servers in certain settings. We believe that a system such as Panorama will offer indispensable assistance to code analysts and malware researchers by enabling them to quickly comprehend the behavior and innerworkings of an unknown sample.

/pdf/panorama-capturing-system-wide-information-flow-for-malware-2wme8tbeoz.pdf

Panorama: capturing system-wide information flow for malware detection and analysis

With the introduction of Apple’s iOS and Google’s Android operating systems, the sales of smartphones have exploded. These smartphones have become powerful devices that are basically miniature versions of personal computers. However, the growing popularity and sophistication of smartphones have also increased concerns about the privacy of users who operate these devices. These concerns have been exacerbated by the fact that it has become increasingly easy for users to install and execute third-party applications. To protect its users from malicious applications, Apple has introduced a vetting process. This vetting process should ensure that all applications conform to Apple’s (privacy) rules before they can be offered via the App Store. Unfortunately, this vetting process is not welldocumented, and there have been cases where malicious applications had to be removed from the App Store after

PiOS : Detecting privacy leaks in iOS applications

Developers use cryptographic APIs in Android with the intent of securing data such as passwords and personal information on mobile devices. In this paper, we ask whether developers use the cryptographic APIs in a fashion that provides typical cryptographic notions of security, e.g., IND-CPA security. We develop program analysis techniques to automatically check programs on the Google Play marketplace, and find that 10.327 out of 11,748 applications that use cryptographic APIs -- 88% overall -- make at least one mistake. These numbers show that applications do not use cryptographic APIs in a fashion that maximizes overall security. We then suggest specific remediations based on our analysis towards improving overall cryptographic security in Android applications.

/pdf/an-empirical-study-of-cryptographic-misuse-in-android-1ec77rrn5d.pdf

An empirical study of cryptographic misuse in android applications

Commercial-off-the-shelf (COTS) network-enabled embedded devices are usually controlled by vendor firmware to perform integral functions in our daily lives. For example, wireless home routers are often the first and only line of defense that separates a home user’s personal computing and information devices from the Internet. Such a vital and privileged position in the user’s network requires that these devices operate securely. Unfortunately, recent research and anecdotal evidence suggest that such security assumptions are not at all upheld by the devices deployed around the world. A first step to assess the security of such embedded device firmware is the accurate identification of vulnerabilities. However, the market offers a large variety of these embedded devices, which severely impacts the scalability of existing approaches in this area. In this paper, we present FIRMADYNE, the first automated dynamic analysis system that specifically targets Linuxbased firmware on network-connected COTS devices in a scalable manner. We identify a series of challenges inherent to the dynamic analysis of COTS firmware, and discuss how our design decisions address them. At its core, FIRMADYNE relies on software-based full system emulation with an instrumented kernel to achieve the scalability necessary to analyze thousands of firmware binaries automatically. We evaluate FIRMADYNE on a real-world dataset of 23,035 firmware images across 42 device vendors gathered by our system. Using a sample of 74 exploits on the 9,486 firmware images that our system can successfully extract, we discover that 887 firmware images spanning at least 89 distinct products are vulnerable to one or more of the sampled exploit(s). This includes 14 previouslyunknown vulnerabilities that were discovered with the aid of our framework, which affect 69 firmware images spanning at least 12 distinct products. Furthermore, our results show that 11 of our tested attacks affect firmware images from more than one vendor, suggesting that code-sharing and common upstream manufacturers (OEMs) are quite prevalent. Note: This version has been corrected to eliminate false positives from SNMP exploit data discovered post-publication.

Manuel Egele

Papers

A survey on automated dynamic malware-analysis techniques and tools

Panorama: capturing system-wide information flow for malware detection and analysis

PiOS : Detecting privacy leaks in iOS applications

An empirical study of cryptographic misuse in android applications

Towards Automated Dynamic Analysis for Linux-based Embedded Firmware