Historically, the harm caused by insiders has proven to be one of the greatest concerns for any organization. As such, it has received considerable attention from both the industrial and research communities. Existing works mainly focused on modeling the employees’ normal biometric behavior (e.g., human to device interaction pattern) to detect anomalous behavior which corresponds to the insider activity. However, it is unattainable to stop the insider at the final moment when the malicious act is being carried out. In this paper, we propose a novel framework which performs employee profiling based on aspect-based sentiments and social network information and examine its applicability for early detection of potential insider threats. On the contrary to the traditional sentiment analysis , aspect-based sentiment analysis provides more fine-grained information on the employee. Our framework employs a combination of deep learning techniques such as Gated Recurrent Unit (GRU) and skipgram to build temporal sentiment profiles for the employees. It then performs anomaly detection on the profiles and ranks the employees based on their respective anomaly score. Due to the absence of relevant benchmark dataset, we augmented the publicly available real-world Enron email corpus with an insider threat scenario to evaluate our framework. The evaluation results demonstrate that the augmentation is indeed reflected in the augmented employee’s anomaly ranking (i.e., from normal to abnormal) and her close associates are indeed placed closely to her when the profiles are visualized in the 2D space. The profiles obtained from our framework can also be used to complement any existing expert and intelligent systems with additional capabilities in handling textual information such as, integration with profiles obtained from biometric behavior to form a more comprehensive threat detection system.

Building behavior profiles of Android applications (apps) with holistic, rich and multi-view information (e.g., incorporating several semantic views of an app such as API sequences, system calls, etc.) would help catering downstream analytics tasks such as app categorization, recommendation and malware analysis significantly better. Towards this goal, we design a semisupervised Representation Learning (RL) framework named apk2vec to automatically generate a compact representation (aka profile/embedding) for a given app. More specifically, apk2vec has the three following unique characteristics which make it an excellent choice for large-scale app profiling: (1) it encompasses information from multiple semantic views such as API sequences, permissions, etc., (2) being a semi-supervised embedding technique, it can make use of labels associated with apps (e.g., malware family or app category labels) to build high quality app profiles, and (3) it combines RL and feature hashing which allows it to efficiently build profiles of apps that stream over time (i.e., online learning). The resulting semi-supervised multi-view hash embeddings of apps could then be used for a wide variety of downstream tasks such as the ones mentioned above. Our extensive evaluations with more than 42,000 apps demonstrate that apk2vec's app profiles could significantly outperform state-of-the-art techniques in four app analytics tasks namely, malware detection, familial clustering, app clone detection and app recommendation.

Summary The increasingly prevalent use of mobile devices has raised the popularity of mobile applications. Therefore, automated testing of mobile applications has become an extremely important task. However, it is still a challenge to automatically generate tests with high coverage for mobile applications due to their specific nontrivial structure and the highly interactive nature of graphical user interfaces (GUIs). In this paper, we propose a novel automated GUI testing technique for mobile applications, namely, Mobolic. In this approach, tests with high coverage are automatically generated and executed by combining the online testing technique and customated input generation. Employing the online testing technique, Mobolic systematically explores the app GUI without falling in a loop. It generates relevant events “on the fly” that are followed by an immediate execution. In addition, involving the customated input generation, Mobolic automatically generates relevant user inputs such as user‐predefined, concrete, or random ones. We implemented Mobolic and evaluated its performance on 10 real‐world open‐source Android applications. Our experimental results show the effectiveness and efficiency of Mobolic in terms of achieved code coverage and overall exercising time.

Automated functional user interface (UI) testing of mobile apps is a challenging task due to the highly interactive nature of the app UIs, and thus it commonly fails to provide high code coverage. In this paper, we present DroidDEV, an automated UI exerciser which aims to achieve high code coverage for real-world Android apps. DroidDEV dynamically builds a finite UI flow graph, generates and injects UI-context-aware inputs to exercise all the widgets on all the UI screens through a best-first search path finding algorithm. We have evaluated DroidDEV on 20 real-world open-source Android apps and compared it with manual UI testing and two other prevalent automated approaches including MobiGUITAR and Monkey. On these subjects, DroidDEV reports code coverage similar to manual UI testing, and it outperforms MobiGUITAR and Monkey in terms of code coverage and exercising time.

Android applications typically contain multiple third-party libraries and recent studies have shown that the presence of third-party libraries may introduce privacy risks and security threats. Furthermore, researchers have reported the importance of considering the third-party libraries for their program analysis tasks. A reason being that the presence of third-party libraries may dilute the features and affect the accuracy of their results. Existing literature typically employs a whitelist to exclude the third-party libraries from their analysis in order to achieve accurate results. However, these whitelists are generally incomplete and weak against the renaming obfuscation technique that is commonly employed in Android applications. In this paper, we propose LibSift, a tool to automatically detect third-party libraries in Android applications. LibSift detects third-party libraries based on package dependencies that are resilient to most common obfuscations. The evaluation results not only indicate that LibSift can detect third-party libraries accurately and effectively, but also show that LibSift can detect even the less popular libraries that are not detected by two of the state-of-the-art approaches.

The blooming mobile smart phone device industry has attracted a large number of application developers. However, due to the availability of reverse engineering tools for Android applications, it also caught the attention of plagiarists and malware writers. In recent years, application cloning has become a serious threat to the Android market. In previous work, mobile application clone detection mainly focuses on code-based analysis. Such an approach lacks resilient to advanced obfuscation techniques. Their efficiency is also questionable, as billions of opcodes need to be processed for cross-market clone detection. In this paper, we propose a novel technique of detecting Android application clones based on the analysis of user interface (UI) information collected at runtime. By leveraging on the multiple entry points feature of Android applications, the UI information can be collected easily without the need to generate relevant inputs and execute the entire application. Another advantage of our technique is obfuscation resilient since semantics preserving obfuscation technique do not affect runtime behaviors. We evaluated our approach on a set of real-world dataset and it has a low false positive rate and false negative rate. Furthermore, the results also show that our approach is effective in detecting different types of repackaging attacks.