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

Mobile malware is rapidly becoming a serious threat. In this paper, we survey the current state of mobile malware in the wild. We analyze the incentives behind 46 pieces of iOS, Android, and Symbian malware that spread in the wild from 2009 to 2011. We also use this data set to evaluate the effectiveness of techniques for preventing and identifying mobile malware. After observing that 4 pieces of malware use root exploits to mount sophisticated attacks on Android phones, we also examine the incentives that cause non-malicious smartphone tinkerers to publish root exploits and survey the availability of root exploits.

/pdf/a-survey-of-mobile-malware-in-the-wild-18gj2rthl8.pdf

A survey of mobile malware in the wild

Programming question and answer (QA questions in some topics lead to discussions in other topics; and the topics gaining the most popularity over time are web development (especially jQuery), mobile applications (especially Android), Git, and MySQL.

/pdf/what-are-developers-talking-about-an-analysis-of-topics-and-1uk58lpy70.pdf

What are developers talking about? An analysis of topics and trends in Stack Overflow

Nowadays, mobile devices are an important part of our everyday lives since they enable us to access a large variety of ubiquitous services. In recent years, the availability of these ubiquitous and mobile services has significantly increased due to the different form of connectivity provided by mobile devices, such as GSM, GPRS, Bluetooth and Wi-Fi. In the same trend, the number and typologies of vulnerabilities exploiting these services and communication channels have increased as well. Therefore, smartphones may now represent an ideal target for malware writers. As the number of vulnerabilities and, hence, of attacks increase, there has been a corresponding rise of security solutions proposed by researchers. Due to the fact that this research field is immature and still unexplored in depth, with this paper we aim to provide a structured and comprehensive overview of the research on security solutions for mobile devices. This paper surveys the state of the art on threats, vulnerabilities and security solutions over the period 2004-2011, by focusing on high-level attacks, such those to user applications. We group existing approaches aimed at protecting mobile devices against these classes of attacks into different categories, based upon the detection principles, architectures, collected data and operating systems, especially focusing on IDS-based models and tools. With this categorization we aim to provide an easy and concise view of the underlying model adopted by each approach.

/pdf/a-survey-on-security-for-mobile-devices-29lf2mr79f.pdf

A Survey on Security for Mobile Devices

We introduce hand movement, orientation, and grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data were collected under two conditions: 1) sitting and 2) walking. We achieved authentication equal error rates (EERs) as low as 7.16% (walking) and 10.05% (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved the EERs of 15.1% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7% and 34.2%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at a 16-Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones).

/pdf/hmog-new-behavioral-biometric-features-for-continuous-wxqxi7t4mv.pdf

HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users

We are currently moving from the Internet society to a mobile society where more and more access to information is done by previously dumb phones. For example, the number of mobile phones using a full blown OS has risen to nearly200% from Q3/2009 to Q3/2010. As a result, mobile security is no longer immanent, but imperative. This survey paper provides a concise overview of mobile network security, attack vectors using the back end system and the web browser, but also the hardware layer and the user as attack enabler. We show differences and similarities between "normal" security and mobile security, and draw conclusions for further research opportunities in this area.

http://www.ieee-security.org/TC/SP2011/PAPERS/2011/paper007.pdf

Mobile Security Catching Up? Revealing the Nuts and Bolts of the Security of Mobile Devices

Graphical passwords were proposed as an alternative to overcome the inherent limitations of text-based passwords, inspired by research that shows that the graphical memory of humans is particularly well developed. A graphical password scheme that has been widely adopted is the Android Unlock Pattern, a special case of the Pass-Go scheme with grid size restricted to 3x3 points and restricted stroke count.In this paper, we study the security of Android unlock patterns. By performing a large-scale user study, we measure actual user choices of patterns instead of theoretical considerations on password spaces. From this data we construct a model based on Markov chains that enables us to quantify the strength of Android unlock patterns. We found empirically that there is a high bias in the pattern selection process, e.g., the upper left corner and three-point long straight lines are very typical selection strategies. Consequently, the entropy of patterns is rather low, and our results indicate that the security offered by the scheme is less than the security of only three digit randomly-assigned PINs for guessing 20% of all passwords (i.e., we estimate a partial guessing entropy G_0.2 of 9.10 bit).Based on these insights, we systematically improve the scheme by finding a small, but still effective change in the pattern layout that makes graphical user logins substantially more secure. By means of another user study, we show that some changes improve the security by more than doubling the space of actually used passwords (i.e., increasing the partial guessing entropy G_0.2 to 10.81 bit).

https://www.ei.ruhr-uni-bochum.de/media/emma/veroeffentlichungen/2013/09/26/patternLogin-CCS13.pdf

Quantifying the security of graphical passwords: the case of android unlock patterns

Smartphones have become the standard personal device to store private or sensitive information. Widely used as every day gadget, however, they are susceptible to get lost or stolen. To protect information on a smartphone from being physically accessed by attackers, a lot of authentication methods have been proposed in recent years. Each one of them suffers from certain drawbacks, either they are easy to circumvent, vulnerable against shoulder surfing attacks, or cumbersome to use. In this paper, we present an alternative approach for user authentication that is based on the smartphone’s sensors. By making use of the user’s biometrical behavior while entering text into the smartphone, we transparently authenticate the user in an ongoing-fashion. In a field study, we asked more than 300 participants to enter some short sentences into a smartphone while all available sensor events were recorded to determine a typing motion fingerprint of the user. After the proper feature extraction, a machine learning classifier based on Support Vector Machines (SVM) is used to identify the authorized user. The results of our study are twofold: While our approach is able to continuously authenticate some users with high precision, there also exist participants for which no accurate motion fingerprint can be learned. We analyze these difference in detail and provide guidelines for similar problems.

/pdf/continuous-authentication-on-mobile-devices-by-analysis-of-4eexi3aigz.pdf

Continuous authentication on mobile devices by analysis of typing motion behavior

Nowadays, Smartphones are widely used and they have a growing market share of already more than 55 % according to recent studies. They often contain sensitive or private data that can easily be accessed by an attacker if the device is unlocked. Since smartphones are mobile and used as everyday gadgets, they are susceptible to get lost or stolen. To prevent the data from being accessed by an attacker, access control mechanisms like user authentication are needed. However, commonly used authentication mechanisms like PINs, passwords, and patterns suffer from the same weakness: They are vulnerable against different kinds of attacks, most notably shoulder surfing. In order to prevent shoulder surfing, a secure channel between the smartphone and the user must be established that cannot be eavesdropped by an adversary.

http://fc15.ifca.ai/preproceedings/paper_82.pdf

Sebastian Uellenbeck

Papers

Mobile Security Catching Up? Revealing the Nuts and Bolts of the Security of Mobile Devices

Quantifying the security of graphical passwords: the case of android unlock patterns

Continuous authentication on mobile devices by analysis of typing motion behavior

Tactile One-Time Pad: Leakage-Resilient Authentication for Smartphones

Tactile one-time pad: smartphone authentication resilient against shoulder surfing