text: The astonishing spread of Android OS, not only in smartphones and tablets but also in IoT devices, makes this operating system a very tempting target for malware threats. Indeed, the latter are expanding at a similar rate. In this respect, malware fingerprints, whether based on cryptographic or fuzzy-hashing, are the first defense line against such attacks. Fuzzy-hashing fingerprints are suitable for capturing malware static features. Moreover, they are more resilient to small changes in the actual static content of malware files. On the other hand, dynamic analysis is another technique for malware detection that uses emulation environments to extract behavioral features of Android malware. However, to the best of our knowledge, there is no such fingerprinting technique that leverages dynamic analysis and would act as the first defense against Android malware attacks. In this paper, we address the following question: could we generate effective fingerprints for Android malware through dynamic analysis? To this end, we propose DySign, a novel technique for fingerprinting Android malware's dynamic behaviors. This is achieved through the generation of a digest from the dynamic analysis of a malware sample on existing known malware. It is important to mention that: (i) DySign fingerprints are approximated of the observed behaviors during dynamic analysis so as to achieve resiliency to small changes in the behaviors of future malware variants; (ii) Fingerprint computation is agnostic to the analyzed malware sample or family. DySign leverages state-of-the-art Natural Language Processing (NLP) techniques to generate the aforementioned fingerprints, which are then leveraged to build an enhanced Android malware detection system with family attribution.
  @cite_42: 
  @cite_32: We present Apposcopy, a new semantics-based approach for identifying a prevalent class of Android malware that steals private user information. Apposcopy incorporates (i) a high-level language for specifying signatures that describe semantic characteristics of malware families and (ii) a static analysis for deciding if a given application matches a malware signature. The signature matching algorithm of Apposcopy uses a combination of static taint analysis and a new form of program representation called Inter-Component Call Graph to efficiently detect Android applications that have certain control- and data-flow properties. We have evaluated Apposcopy on a corpus of real-world Android applications and show that it can effectively and reliably pinpoint malicious applications that belong to certain malware families.
  @cite_3: 
  @cite_24: Smartphones have become pervasive due to the availability of office applications, Internet, games, vehicle guidance using location-based services apart from conventional services such as voice calls, SMSes, and multimedia services. Android devices have gained huge market share due to the open architecture of Android and the popularity of its application programming interface (APIs) in the developer community. Increased popularity of the Android devices and associated monetary benefits attracted the malware developers, resulting in big rise of the Android malware apps between 2010 and 2014. Academic researchers and commercial antimalware companies have realized that the conventional signature-based and static analysis methods are vulnerable. In particular, the prevalent stealth techniques, such as encryption, code transformation, and environment-aware approaches, are capable of generating variants of known malware. This has led to the use of behavior-, anomaly-, and dynamic-analysis-based methods. Since a single approach may be ineffective against the advanced techniques, multiple complementary approaches can be used in tandem for effective malware detection. The existing reviews extensively cover the smartphone OS security. However, we believe that the security of Android, with particular focus on malware growth, study of antianalysis techniques, and existing detection methodologies, needs an extensive coverage. In this survey, we discuss the Android security enforcement mechanisms, threats to the existing security enforcements and related issues, malware growth timeline between 2010 and 2014, and stealth techniques employed by the malware authors, in addition to the existing detection methods. This review gives an insight into the strengths and shortcomings of the known research methodologies and provides a platform, to the researchers and practitioners, toward proposing the next-generation Android security, analysis, and malware detection techniques.
  @cite_44: Android Smartphone popularity has increased malware threats forcing security researchers and AntiVirus (AV) industry to carve out smart methods to defend Smartphone against malicious apps. Robust signature based solutions to mitigate threats become necessary to protect the Smartphone and confidential user data. In this paper we present AndroSimilar, a robust approach which generates signature by extracting statistically improbable features, to detect malicious Android apps. Proposed method is effective against code obfuscation and repackaging, widely used techniques to evade AV signature and to propagate unseen variants of known malware. AndroSimilar is a syntactic foot-printing mechanism that finds regions of statistical similarity with known malware to detect those unknown, zero day samples. Syntactic file similarity of whole file is considered instead of just opcodes for faster detection compared to known fuzzy hashing approaches. Results demonstrate robust detection of variants of known malware families. Proposed approach can be refined to deploy as Smartphone AV.
  @cite_45: Android's market experienced exponential popularity during the last few years. This blazing growth has, unfortunately, opened the door to thousands of malicious applications targeting Android devices everyday. Moreover, with the increasing sophistication of today's malware, the use of traditional hashing techniques for Android malware fingerprinting becomes defenseless against polymorphic malicious applications. Inspired by fuzzy hashing techniques, we propose, in this paper, a novel and comprehensive fingerprinting approach for Android packaging APK. The proposed fingerprint captures, not only the binary features of the APK file, but also the underlying structure of the app. Furthermore, we leverage this fingerprinting technique to build ROAR, an automatic system for Android malware detection and family attribution. Our experiments show that the proposed fingerprint and the ROAR system achieve a precision of 95 .
  @cite_15: The phenomenal growth of the Android platform in the past few years has made it a lucrative target of malicious application (app) developers. There are numerous instances of malware apps that send premium rate SMS messages, track users' private data, or apps that, even if not characterized as malware, conduct questionable actions affecting the user's privacy or costing them money. In this paper, we investigate the feasibility of using both the permissions an app requests, the category of the app, and what permissions are requested by other apps in the same category to better inform users whether the risks of installing an app is commensurate with its expected benefit. Existing approaches consider only the risks of the permissions requested by an app and ignore both the benefits and what permissions are requested by other apps, thus having a limited effect. We propose several risk signals that and evaluate them using two datasets, one consists of 158,062 Android apps from the Android Market, and another consists of 121 malicious apps. We demonstrate the effectiveness of our proposal through extensive data analysis.
  @cite_10: 
  @cite_11: 
summary: Static analysis techniques perform code disassembling and decompilation without actually running it. This approach is undermined by the use of various code transformation techniques @cite_24 . We may divide static analysis based techniques into the following categories: i) This analysis deals with extracted syntactic pattern features @cite_32 @cite_10 @cite_42 , and create a unique signature matching for a particular malware. However, such signature cannot handle new variants of existing known malware. Moreover, the signature database should be updated to handle new variants. AndroSimilar @cite_44 has been proposed to detect zero-day variants of the known malware. It is an automated statistical feature signature-based method. However, it is sensitive due to code transformation methods. ii) The Manifest file contains important meta-data about the components (i.e., activities, services, receivers, etc.) and required permissions. There are some methods that have been proposed to extract such information and subject it to analysis @cite_45 @cite_3 . iii) Discovering the dangerous permission request is not adequate to proclaim a malware app, but nevertheless, permissions mapping requested and used permissions are an important risk identification technique @cite_15 @cite_11 .