Automatically Locating Malicious Packages in Piggybacked Android Apps

in Abstract book of the 4th IEEE/ACM International Conference on Mobile Software Engineering and Systems (MobileSoft 2017) (2017, May)

To devise efficient approaches and tools for detecting malicious packages in the Android ecosystem, researchers are increasingly required to have a deep understanding of malware. There is thus a need to ... [more ▼]

To devise efficient approaches and tools for detecting malicious packages in the Android ecosystem, researchers are increasingly required to have a deep understanding of malware. There is thus a need to provide a framework for dissecting malware and locating malicious program fragments within app code in order to build a comprehensive dataset of malicious samples. Towards addressing this need, we propose in this work a tool-based approach called HookRanker, which provides ranked lists of potentially malicious packages based on the way malware behaviour code is triggered. With experiments on a ground truth set of piggybacked apps, we are able to automatically locate the malicious packages from piggybacked Android apps with an accuracy of 83.6% in verifying the top five reported items. [less ▲]

Understanding Android App Piggybacking

Poster (2017, May)

The Android packaging model offers adequate opportunities for attackers to inject malicious code into popular benign apps, attempting to develop new malicious apps that can then be easily spread to a ... [more ▼]

The Android packaging model offers adequate opportunities for attackers to inject malicious code into popular benign apps, attempting to develop new malicious apps that can then be easily spread to a large user base. Despite the fact that the literature has already presented a number of tools to detect piggybacked apps, there is still lacking a comprehensive investigation on the piggybacking processes. To fill this gap, in this work, we collect a large set of benign/piggybacked app pairs that can be taken as benchmark apps for further investigation. We manually look into these benchmark pairs for understanding the characteristics of piggybacking apps and eventually we report 20 interesting findings. We expect these findings to initiate new research directions such as practical and scalable piggybacked app detection, explainable malware detection, and malicious code location. [less ▲]

Weaving Rules into Models@run.time for Embedded Smart Systems

in Mouline, Ludovic; Hartmann, Thomas; Fouquet, François (Eds.) et al Programming '17 Companion to the first International Conference on the Art, Science and Engineering of Programming (2017, April)

Smart systems are characterised by their ability to analyse measured data in live and to react to changes according to expert rules. Therefore, such systems exploit appropriate data models together with ... [more ▼]

Smart systems are characterised by their ability to analyse measured data in live and to react to changes according to expert rules. Therefore, such systems exploit appropriate data models together with actions, triggered by domain-related conditions. The challenge at hand is that smart systems usually need to process thousands of updates to detect which rules need to be triggered, often even on restricted hardware like a Raspberry Pi. Despite various approaches have been investigated to efficiently check conditions on data models, they either assume to fit into main memory or rely on high latency persistence storage systems that severely damage the reactivity of smart systems. To tackle this challenge, we propose a novel composition process, which weaves executable rules into a data model with lazy loading abilities. We quantitatively show, on a smart building case study, that our approach can handle, at low latency, big sets of rules on top of large-scale data models on restricted hardware. [less ▲]

Understanding Android App Piggybacking: A Systematic Study of Malicious Code Grafting

in IEEE Transactions on Information Forensics and Security (2017)

The Android packaging model offers ample opportunities for malware writers to piggyback malicious code in popular apps, which can then be easily spread to a large user base. Although recent research has ... [more ▼]

The Android packaging model offers ample opportunities for malware writers to piggyback malicious code in popular apps, which can then be easily spread to a large user base. Although recent research has produced approaches and tools to identify piggybacked apps, the literature lacks a comprehensive investigation into such phenomenon. We fill this gap by 1) systematically building a large set of piggybacked and benign apps pairs, which we release to the community, 2) empirically studying the characteristics of malicious piggybacked apps in comparison with their benign counterparts, and 3) providing insights on piggybacking processes. Among several findings providing insights, analysis techniques should build upon to improve the overall detection and classification accuracy of piggybacked apps, we show that piggybacking operations not only concern app code but also extensively manipulates app resource files, largely contradicting common beliefs. We also find that piggybacking is done with little sophistication, in many cases automatically, and often via library code. [less ▲]

Assessing and Improving the Mutation Testing Practice of PIT

in 10th IEEE International Conference on Software Testing, Verification and Validation (2017)

Augmenting and Structuring User Queries to Support Efficient Free-Form Code Search

Report (2017)

Source code terms such as method names and variable types are often different from conceptual words mentioned in a search query. This vocabulary mismatch problem can make code search inefficient. In this ... [more ▼]

Source code terms such as method names and variable types are often different from conceptual words mentioned in a search query. This vocabulary mismatch problem can make code search inefficient. In this paper, we present Code voCABUlary (CoCaBu), an approach to resolving the vocabulary mismatch problem when dealing with free-form code search queries. Our approach leverages common developer questions and the associated expert answers to augment user queries with the relevant, but missing, structural code entities in order to improve the performance of matching relevant code examples within large code repositories. To instantiate this approach, we build GitSearch, a code search engine, on top of GitHub and StackOverflow Q\&A data. We evaluate GitSearch in several dimensions to demonstrate that (1) its code search results are correct with respect to user-accepted answers; (2) the results are qualitatively better than those of existing Internet-scale code search engines; (3) our engine is competitive against web search engines, such as Google, in helping users complete solve programming tasks; and (4) GitSearch provides code examples that are acceptable or interesting to the community as answers for StackOverflow questions. [less ▲]

Efficiently computing the likelihoods of cyclically interdependent risk scenarios

in Computers and Security (2017), 64

Quantitative risk assessment provides a holistic view of risk in an organisation, which is, however, often biased by the fact that risk shared by several assets is encoded multiple times in a risk ... [more ▼]

Quantitative risk assessment provides a holistic view of risk in an organisation, which is, however, often biased by the fact that risk shared by several assets is encoded multiple times in a risk analysis. An apparent solution to this issue is to take all dependencies between assets into consideration when building a risk model. However, existing approaches rarely support cyclic dependencies, although assets that mutually rely on each other are encountered in many organisations, notably in critical infrastructures. To the best of our knowledge, no author has provided a provably efficient algorithm (in terms of the execution time) for computing the risk in such an organisation, notwithstanding that some heuristics exist. This paper introduces the dependency-aware root cause (DARC) model, which is able to compute the risk resulting from a collection of root causes using a poly-time randomised algorithm, and concludes with a discussion on real-time risk monitoring, which DARC supports by design. © 2016 Elsevier Ltd [less ▲]

DSCo-NG: A Practical Language Modeling Approach for Time Series Classification

in The 15th International Symposium on Intelligent Data Analysis (2016, October)

The abundance of time series data in various domains and their high dimensionality characteristic are challenging for harvesting useful information from them. To tackle storage and processing challenges ... [more ▼]

The abundance of time series data in various domains and their high dimensionality characteristic are challenging for harvesting useful information from them. To tackle storage and processing challenges, compression-based techniques have been proposed. Our previous work, Domain Series Corpus (DSCo), compresses time series into symbolic strings and takes advantage of language modeling techniques to extract from the training set knowledge about different classes. However, this approach was flawed in practice due to its excessive memory usage and the need for a priori knowledge about the dataset. In this paper we propose DSCo-NG, which reduces DSCo’s complexity and offers an efficient (linear time complexity and low memory footprint), accurate (performance comparable to approaches working on uncompressed data) and generic (so that it can be applied to various domains) approach for time series classification. Our confidence is backed with extensive experimental evaluation against publicly accessible datasets, which also offers insights on when DSCo-NG can be a better choice than others. [less ▲]

Accessing Inaccessible Android APIs: An Empirical Study

in The 32nd International Conference on Software Maintenance and Evolution (ICSME) (2016, October)

As Android becomes a de-facto choice of development platform for mobile apps, developers extensively leverage its accompanying Software Development Kit to quickly build their apps. This SDK comes with a ... [more ▼]

As Android becomes a de-facto choice of development platform for mobile apps, developers extensively leverage its accompanying Software Development Kit to quickly build their apps. This SDK comes with a set of APIs which developers may find limited in comparison to what system apps can do or what framework developers are preparing to harness capabilities of new generation devices. Thus, developers may attempt to explore in advance the normally “inaccessible” APIs for building unique API-based functionality in their app. The Android programming model is unique in its kind. Inaccessible APIs, which however are used by developers, constitute yet another specificity of Android development, and is worth investigating to understand what they are, how they evolve over time, and who uses them. To that end, in this work, we empirically investigate 17 important releases of the Android framework source code base, and we find that inaccessible APIs are commonly implemented in the Android framework, which are further neither forward nor backward compatible. Moreover, a small set of inaccessible APIs can eventually become publicly accessible, while most of them are removed during the evolution, resulting in risks for such apps that have leveraged inaccessible APIs. Finally, we show that inaccessible APIs are indeed accessed by third-party apps, and the official Google Play store has tolerated the proliferation of apps leveraging inaccessible API methods. [less ▲]

UAV degradation identification for pilot notification using machine learning techniques

in Proceedings of 21st IEEE International Conference on Emerging Technologies and Factory Automation ETFA 2016 (2016, September 06)

Unmanned Aerial Vehicles are currently investigated as an important sub-domain of robotics, a fast growing and truly multidisciplinary research field. UAVs are increasingly deployed in real-world settings ... [more ▼]

Unmanned Aerial Vehicles are currently investigated as an important sub-domain of robotics, a fast growing and truly multidisciplinary research field. UAVs are increasingly deployed in real-world settings for missions in dangerous environments or in environments which are challenging to access. Combined with autonomous flying capabilities, many new possibilities, but also challenges, open up. To overcome the challenge of early identification of degradation, machine learning based on flight features is a promising direction. Existing approaches build classifiers that consider their features to be correlated. This prevents a fine-grained detection of degradation for the different hardware components. This work presents an approach where the data is considered uncorrelated and, using machine learning <br />techniques, allows the precise identification of UAV’s damages. [less ▲]