ReCon: Revealing and Controlling PII Leaks in Mobile Network Traffic

It is well known that apps running on mobile devices extensively track and leak users' personally identifiable information (PII); however, these users have little visibility into PII leaked through the network traffic generated by their devices, and have poor control over how, when and where that traffic is sent and handled by third parties. In this paper, we present the design, implementation, and evaluation of ReCon: a cross-platform system that reveals PII leaks and gives users control over them without requiring any special privileges or custom OSes. ReCon leverages machine learning to reveal potential PII leaks by inspecting network traffic, and provides a visualization tool to empower users with the ability to control these leaks via blocking or substitution of PII. We evaluate ReCon's effectiveness with measurements from controlled experiments using leaks from the 100 most popular iOS, Android, and Windows Phone apps, and via an IRB-approved user study with 92 participants. We show that ReCon is accurate, efficient, and identifies a wider range of PII than previous approaches.Comment: Please use MobiSys version when referencing this work: http://dl.acm.org/citation.cfm?id=2906392. 18 pages, recon.meddle.mob

Machine Learning Techniques for Malware Detection with Challenges and Future Directions

In the recent times Cybersecurity is the hot research topic because of its sensitivity. Especially at the times of digital world where everything is now transformed into digital medium. All the critical transactions are being carried out online with internet applications. Malware is an important issue which has the capability of stealing the privacy and funds from an ordinary person who is doing sensitive transactions through his mobile device. Researchers in the current time are striving to develop efficient techniques to detect these kinds of attacks. Not only individuals are getting offended even the governments are getting effected by these kinds of attacks and losing big amount of funds. In this work various Artificial intelligent and machine learning techniques are discussed which were implements for the detection of malware. Traditional machine learning techniques like Decision tree, K-Nearest Neighbor and Support vector machine and further to advanced machine learning techniques like Artificial neural network and convolution neural network are discussed. Among the discussed techniques, the work got the highest accuracy is 99% followed by 98.422%, 97.3% and 96% where the authors have implemented package-level API calls as feature, followed by advanced classification technique. Also, dataset details are discussed and listed which were used for the experimentation of malware detection, among the many dataset DREBIN had the most significant number of samples with 123453 Benign samples and 5560 Malware samples. Finally, open challenges are listed, and the future directions are highlighted which would encourage a new researcher to adopt this field of research and solve these open challenges with the help of future direction details provided in this paper. The paper is concluded with the limitation and conclusion sectio

A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection

Taming Android Fragmentation through Lightweight Crowdsourced Testing

Android fragmentation refers to the overwhelming diversity of Android devices and OS versions. These lead to the impossibility of testing an app on every supported device, leaving a number of compatibility bugs scattered in the community and thereby resulting in poor user experiences. To mitigate this, our fellow researchers have designed various works to automatically detect such compatibility issues. However, the current state-of-the-art tools can only be used to detect specific kinds of compatibility issues (i.e., compatibility issues caused by API signature evolution), i.e., many other essential types of compatibility issues are still unrevealed. For example, customized OS versions on real devices and semantic changes of OS could lead to serious compatibility issues, which are non-trivial to be detected statically. To this end, we propose a novel, lightweight, crowdsourced testing approach, LAZYCOW, to fill this research gap and enable the possibility of taming Android fragmentation through crowdsourced efforts. Specifically, crowdsourced testing is an emerging alternative to conventional mobile testing mechanisms that allow developers to test their products on real devices to pinpoint platform-specific issues. Experimental results on thousands of test cases on real-world Android devices show that LAZYCOW is effective in automatically identifying and verifying API-induced compatibility issues. Also, after investigating the user experience through qualitative metrics, users' satisfaction provides strong evidence that LAZYCOW is useful and welcome in practice