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Mobile Offloading in the Wild: Findings and Lessons Learned Through a Real-life Experiment with a New Cloud-aware System
"... Abstract—Mobile-cloud offloading mechanisms delegate heavy mobile computation to the cloud. In real life use, the energy tradeoff of computing the task locally or sending the input data and the code of the task to the cloud is often negative, especially with popular communication intensive jobs like ..."
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Abstract—Mobile-cloud offloading mechanisms delegate heavy mobile computation to the cloud. In real life use, the energy tradeoff of computing the task locally or sending the input data and the code of the task to the cloud is often negative, especially with popular communication intensive jobs like social-networking, gaming, and emailing. We design and build a working implementation of CDroid, a system that tightly couples the device OS to its cloud counterpart. The cloud-side handles data traffic through the device efficiently and, at the same time, caches code and data optimally for possible future offloading. In our system, when offloading decision takes place, input and code are likely to be already on the cloud. CDroid makes mobile cloud offloading more practical enabling offloading of lightweight jobs and communication intensive apps. Our experiments with real users in everyday life show excellent
Architecture strategies for cyber-foraging: Preliminary results from a systematic literature review.
- In Proceedings of the 8th European Conference on Software Architecture (ECSA 2014),
, 2014
"... Abstract. Mobile devices have become for many the preferred way of interacting with the Internet, social media and the enterprise. However, mobile devices still do not have the computing power and battery life that will allow them to perform effectively over long periods of time or for executing ap ..."
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Abstract. Mobile devices have become for many the preferred way of interacting with the Internet, social media and the enterprise. However, mobile devices still do not have the computing power and battery life that will allow them to perform effectively over long periods of time or for executing applications that require extensive communication or computation, or low latency. Cyber-foraging is a technique to enable mobile devices to extend their computing power and storage by offloading computation or data to more powerful servers located in the cloud or in single-hop proximity. This paper presents the preliminary results of a systematic literature review (SLR) on architectures that support cyberforaging. The preliminary results show that this is an area with many opportunities for research that will enable cyber-foraging solutions to become widely adopted as a way to support the mobile applications of the present and the future.
Security Architecture for Federated Mobile Cloud Computing
"... Abstract Mobile cloud computing systems are getting increasingly popular because they can facilitate many new applications, such as opportunistic social computing by smartphone users who happen to be at a scene of importance (e.g., disaster rescue), while possibly uploading compute-heavy tasks to th ..."
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Abstract Mobile cloud computing systems are getting increasingly popular because they can facilitate many new applications, such as opportunistic social computing by smartphone users who happen to be at a scene of importance (e.g., disaster rescue), while possibly uploading compute-heavy tasks to the resource-rich clouds. Feder-ated mobile cloud computing further allows to coordinate and optimize the services to mobile users of different clouds. Accompanying the great deal of opportunities it brings up, federated mobile cloud computing imposes a diverse set of new chal-lenges, especially from a security perspective because the defender needs to cope with a large spectrum of attacks. Example security questions are: How should we better deal with the new dimension of threats that are caused by that smartphones run a huge population of untrusted third-party applications (apps)? How should we monitor the mobile clouds for security purposes? How should we deal with the tar-geted attackers that attempt to launch attacks against the various credentials used for authentication purposes (e.g., banking)? How should we enhance the privacy of users when a malware breaks into their smartphone (e.g., records of location infor-mation?) How should the federated mobile clouds share security information and possibly coordinate their defense activities? In this chapter, we explore the threat model against, and security requirements of, federated mobile clouds computing. We then propose and investigate a comprehensive security architecture, which can seamlessly integrate a set of novel security mechanisms that are tailored to satisfy the security needs of federated mobile cloud computing.
MARVIN: Efficient and Comprehensive Mobile App Classification Through Static and Dynamic Analysis
"... Abstract—Android dominates the smartphone operating sys-tem market and consequently has attracted the attention of malware authors and researchers alike. Despite the consider-able number of proposed malware analysis systems, compre-hensive and practical malware analysis solutions are scarce and ofte ..."
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Abstract—Android dominates the smartphone operating sys-tem market and consequently has attracted the attention of malware authors and researchers alike. Despite the consider-able number of proposed malware analysis systems, compre-hensive and practical malware analysis solutions are scarce and often short-lived. Systems relying on static analysis alone struggle with increasingly popular obfuscation and dynamic code loading techniques, while purely dynamic analysis systems are prone to analysis evasion. We present MARVIN, a system that combines static with dynamic analysis and which leverages machine learning tech-niques to assess the risk associated with unknown Android apps in the form of a malice score. MARVIN performs static and dynamic analysis, both off-device, to represent properties and behavioral aspects of an app through a rich and comprehensive feature set. In our evaluation on the largest Android malware classification data set to date, comprised of over 135,000 Android apps and 15,000 malware samples, MARVIN correctly classifies 98.24 % of malicious apps with less than 0.04 % false positives. We further estimate the necessary retraining interval to maintain the detection performance and demonstrate the long-term practicality of our approach. Keywords-mobile security; malware analysis; classification I.
In-Cloud Malware Analysis and Detection: State of the Art
"... With the advent of Internet of Things, we are facing an-other wave of malware attacks, that encompass intelligent embedded devices. Because of the limited energy resources, running a complete malware detector on these devices is quite challenging. There is a need to devise new techniques to detect m ..."
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With the advent of Internet of Things, we are facing an-other wave of malware attacks, that encompass intelligent embedded devices. Because of the limited energy resources, running a complete malware detector on these devices is quite challenging. There is a need to devise new techniques to detect malware on these devices. Malware detection is one of the services that can be provided as an in-cloud ser-vice. This paper reviews current such systems, discusses there pros and cons, and recommends an improved in-cloud malware analysis and detection system. We introduce a new three layered hybrid system with a lightweight antimalware engine. These features can provide faster malware detection response time, shield the client from malware and reduce the bandwidth between the client and the cloud, compared to other such systems. The paper serves as a motivation for improving the current and developing new techniques for in-cloud malware analysis and detection system.
