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Malware detection is an important task for the ecosystem of mobile applications (APPs), especially for the Android ecosystem, and is vital to guarantee the user experience of Android APPs. There have been some exiting methods trying to solve the problem of malware detection, but the methods suffer from several defects, such as high time complexity and mediocre accuracy, which seriously decrease the practicability of existing methods. To solve these problems, in this study, we propose a novel Android malware detection framework, where we contribute an efficient Application Programming Interface (API) call sequences extraction algorithm and an investigation of different types of classifiers. In API call sequences extraction, we propose an algorithm for transforming the function call graph from a multigraph into a directed simple graph, which successfully avoids the unnecessary repetitive path searching. We also propose a pruning search, which further reduces the number of paths to be searched. Our algorithm greatly reduces the time complexity. We generate the transition matrix as classification features and investigate three types of machine learning classifiers to complete the malware detection task. The experiments are performed on real‐world Android Packages (APKs), and the results demonstrate that our method significantly reduces the running time and produces high detection accuracy. [ABSTRACT FROM AUTHOR]

In the development of applications for mobile multimedia computing, it has been a core problem to improve the efficiency of applications development. The use of Web APIs has played an important role in applications development and mashups creation, but it takes a lot of time to find a suitable Web API. In this paper, we propose to employ APIs recommendation to solve this problem. We use the matrix factorization (MF) model to predict the relationship between user and Web APIs. The relationship between user and Web APIs is represented by latent features. But different from MF model, we find that the relationship between Web APIs have an impact on prediction performance. This paper uses the embedding technique and mines the latent relationships between APIs and we develop a novel prediction model that leverages the latent relationships between APIs, which is named as LRM (LRM is short for latent relationships mining). The latent relations are input into the LRM model to produce the relation prediction between APIs and developers. The experimental results show that our method achieves high performance and behaves the best compared to existing baselines. [ABSTRACT FROM AUTHOR]

In recent years, with the development of software development, a large number of developers develop software by invoking API. With the increasing number of APIs, how to accurately recommend the APIs to developers has become a urgently necessary task. In this paper, we discover that there is a relationship between the user and the API, and use such relationships and collaborative learning techniques to finish APIs recommendation. We propose a holistic framework that contains three models. In the models, we design a joint matrix factorization technique and try to discover the preference among APIs invocation process. In natural language processing, word embedding is widely used. In our models, we use doc2vec to turn the representation of users and APIs into vector representation and calculate the similarity separately to generate the relationships. Besides the two modes in users side and APIs side, we also propose an ensemble model fully leveraging the preference mined from both users side and APIs side. We conduct the experiments on a real-world dataset and the experimental results show that our models perform better than all compared methods. [ABSTRACT FROM AUTHOR]