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In recent years, the number of mobile devices is increasing rapidly. Meanwhile, the amount of malicious software is rising almost exponentially, alongside the diversity and complexity of malware. The flexibility of Software-Defined Networking (SDN) provides an opportunity to develop a malware detection model in more efficient and flexible manner. In this paper, we propose a network behavior-based...
As accurate malware detection on mobile devices requires fast process of a large number of application traces, cloud-based malware detection can utilize the data sharing and powerful computational resources of security servers to improve the detection performance. In this paper, we investigate the cloud-based malware detection game, in which mobile devices offload their application traces to security...
While mobile applications make our lives more convenient, security concerns may arise when the mobile applications contain malicious code that would harm the mobile devices and their users financially and physically. In this article, we propose a malware detection framework to protect the mobile devices with the help of the cloud, where the cloud is equipped with the facilities for automatic analysis...
Effective detection of malware is of paramount importance for securing the next generation of smart devices. Static detection, the preferred technique used so far, is not sufficiently powerful to defeat state-of-the-art malware, and will be even less effective in the near future. Dynamic malware detection guarantees better protection since it operates at run-time and can identify also unknown malware,...
Resource-constrained mobile devices pose a challenge to the design of security mechanisms. Existing host-based malware detection solutions are often resource-intensive. We present a decentralized and resource-aware malware detection architecture for mobile devices. Our approach leverages two key ideas: social collaboration and the concept of a hot set. The hot set concept states that not all malware...
This paper presents a distributed Support Vector Machine (SVM) algorithm in order to detect malicious software (malware) on a network of mobile devices. The light-weight system monitors mobile user activity in a distributed and privacy-preserving way using a statistical classification model which is evolved by training with examples of both normal usage patterns and unusual behavior. The system is...
Dramatic increase in smartphone sales and third-party applications that users can download has significantly increased the possibility of rootkits and malware targeted for smartphones. This paper discusses the current state of research in detection and mitigation of propagation of malicious code, such as viruses, malware and even rootkits in smartphones. A new strategy is introduced that offers a...
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