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

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.

/pdf/drebin-effective-and-explainable-detection-of-android-4kuv3sjqel.pdf

DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket.

Android's permission system is intended to inform users about the risks of installing applications. When a user installs an application, he or she has the opportunity to review the application's permission requests and cancel the installation if the permissions are excessive or objectionable. We examine whether the Android permission system is effective at warning users. In particular, we evaluate whether Android users pay attention to, understand, and act on permission information during installation. We performed two usability studies: an Internet survey of 308 Android users, and a laboratory study wherein we interviewed and observed 25 Android users. Study participants displayed low attention and comprehension rates: both the Internet survey and laboratory study found that 17% of participants paid attention to permissions during installation, and only 3% of Internet survey respondents could correctly answer all three permission comprehension questions. This indicates that current Android permission warnings do not help most users make correct security decisions. However, a notable minority of users demonstrated both awareness of permission warnings and reasonable rates of comprehension. We present recommendations for improving user attention and comprehension, as well as identify open challenges.

https://cups.cs.cmu.edu/soups/2012/proceedings/a3_Felt.pdf

Android permissions: user attention, comprehension, and behavior

In this paper, we present a systematic study for the detection of malicious applications (or apps) on popular Android Markets. To this end, we first propose a permissionbased behavioral footprinting scheme to detect new samples of known Android malware families. Then we apply a heuristics-based filtering scheme to identify certain inherent behaviors of unknown malicious families. We implemented both schemes in a system called DroidRanger. The experiments with 204, 040 apps collected from five different Android Markets in May-June 2011 reveal 211 malicious ones: 32 from the official Android Market (0.02% infection rate) and 179 from alternative marketplaces (infection rates ranging from 0.20% to 0.47%). Among those malicious apps, our system also uncovered two zero-day malware (in 40 apps): one from the official Android Market and the other from alternative marketplaces. The results show that current marketplaces are functional and relatively healthy. However, there is also a clear need for a rigorous policing process, especially for non-regulated alternative marketplaces.

/pdf/hey-you-get-off-of-my-market-detecting-malicious-apps-in-2hdanyr5j8.pdf

Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets

Recent years have witnessed a meteoric increase in the adoption of smartphones. To manage information and features on such phones, Android provides a permission-based security model that requires each application to explicitly request permissions before it can be installed to run. In this paper, we analyze eight popular Android smartphones and discover that the stock phone images do not properly enforce the permission model. Several privileged permissions are unsafely exposed to other applications which do not need to request them for the actual use. To identify these leaked permissions or capabilities, we have developed a tool called Woodpecker. Our results with eight phone images show that among 13 privileged permissions examined so far, 11 were leaked, with individual phones leaking up to eight permissions. By exploiting them, an untrusted application can manage to wipe out the user data, send out SMS messages, or record user conversation on the affected phones – all without asking for any permission.

/pdf/systematic-detection-of-capability-leaks-in-stock-android-iyx4h21gug.pdf

Systematic Detection of Capability Leaks in Stock Android Smartphones.

Virtualization is being widely adopted in today’s computing systems. Its unique security advantages in isolating and introspecting commodity OSes as virtual machines (VMs) have enabled a wide spectrum of applications. However, a common, fundamental assumption is the presence of a trustworthy hypervisor. Unfortunately, the large code base of commodity hypervisors and recent successful hypervisor attacks (e.g., VM escape) seriously question the validity of this assumption. In this paper, we present HyperSafe, a lightweight approach that endows existing Type-I bare-metal hypervisors with a unique self-protection capability to provide lifetime control flow integrity. Specifically, we propose two key techniques. The first one, non-bypassable memory lockdown, reliably protects the hypervisor’s code and static data from being compromised even in the presence of exploitable memory corruption bugs (e.g., buffer overflows), therefore successfully providing hypervisor code integrity. The second one, restricted pointer indexing, introduces one layer of indirection to convert the control data into pointer indexes. These pointer indexes are restricted such that the corresponding call/return targets strictly follow the hypervisor control flow graph, hence expanding protection to control-flow integrity. We have built a prototype and used it to protect two open-source Type-I hypervisors: BitVisor and Xen. The experimental results with synthetic hypervisor exploits and benchmarking programs show HyperSafe can reliably enable the hypervisor self-protection and provide the integrity guarantee with a small performance overhead.

/pdf/hypersafe-a-lightweight-approach-to-provide-lifetime-wxifj56pr1.pdf

HyperSafe: A Lightweight Approach to Provide Lifetime Hypervisor Control-Flow Integrity

This paper presents HyperSentry, a novel framework to enable integrity measurement of a running hypervisor (or any other highest privileged software layer on a system). Unlike existing solutions for protecting privileged software, HyperSentry does not introduce a higher privileged software layer below the integrity measurement target, which could start another race with malicious attackers in obtaining the highest privilege in the system. Instead, HyperSentry introduces a software component that is properly isolated from the hypervisor to enable stealthy and in-context measurement of the runtime integrity of the hypervisor. While stealthiness is necessary to ensure that a compromised hypervisor does not have a chance to hide the attack traces upon detecting an up-coming measurement, in-context measurement is necessary to retrieve all the needed inputs for a successful integrity measurement.HyperSentry uses an out-of-band channel (e.g., Intelligent Platform Management Interface (IPMI), which is commonly available on server platforms) to trigger the stealthy measurement, and adopts the System Management Mode (SMM) to protect its base code and critical data. A key contribution of HyperSentry is the set of novel techniques that overcome SMM's limitation, providing an integrity measurement agent with (1) the same contextual information available to the hypervisor, (2) completely protected execution, and (3) attestation to its output. To evaluate HyperSentry, we implement a prototype of the framework along with an integrity measurement agent for the Xen hypervisor. Our experimental evaluation shows that HyperSentry is a low-overhead practical solution for real world systems.

/pdf/hypersentry-enabling-stealthy-in-context-measurement-of-1tt4nw7qgd.pdf

HyperSentry: enabling stealthy in-context measurement of hypervisor integrity

Kernel rootkits have posed serious security threats due to their stealthy manner. To hide their presence and activities, many rootkits hijack control flows by modifying control data or hooks in the kernel space. A critical step towards eliminating rootkits is to protect such hooks from being hijacked. However, it remains a challenge because there exist a large number of widely-scattered kernel hooks and many of them could be dynamically allocated from kernel heap and co-located together with other kernel data. In addition, there is a lack of flexible commodity hardware support, leading to the socalled protection granularity gap -- kernel hook protection requires byte-level granularity but commodity hardware only provides page level protection.To address the above challenges, in this paper, we present HookSafe, a hypervisor-based lightweight system that can protect thousands of kernel hooks in a guest OS from being hijacked. One key observation behind our approach is that a kernel hook, once initialized, may be frequently "read"-accessed, but rarely write"-accessed. As such, we can relocate those kernel hooks to a dedicated page-aligned memory space and then regulate accesses to them with hardware-based page-level protection. We have developed a prototype of HookSafe and used it to protect more than 5,900 kernel hooks in a Linux guest. Our experiments with nine real-world rootkits show that HookSafe can effectively defeat their attempts to hijack kernel hooks. We also show that HookSafe achieves such a large-scale protection with a small overhead (e.g., around 6% slowdown in performance benchmarks).

/pdf/countering-kernel-rootkits-with-lightweight-hook-protection-1cv5q0xge5.pdf

Zhi Wang

Papers

Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets

Systematic Detection of Capability Leaks in Stock Android Smartphones.

HyperSafe: A Lightweight Approach to Provide Lifetime Hypervisor Control-Flow Integrity

HyperSentry: enabling stealthy in-context measurement of hypervisor integrity

Countering kernel rootkits with lightweight hook protection