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Register a new userEnergy Attacks on Mobile Devices
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Added by
Ashish Kundu
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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All mobile devices are energy-constrained. They use batteries that allows using the device for a limited amount of time. In general, energy attacks on mobile devices are denial of service (DoS) type of attacks. While previous studies have analyzed the energy attacks in servers, no existing work has analyzed the energy attacks on mobile devices. As such, in this paper, we present the first systematic study on how to exploit the energy attacks on smartphones. In particular, we explore energy attacks from the following aspect: hardware components, software resources, and network communications through the design and implementation of concrete malicious apps, and malicious web pages. We quantitatively show how quickly we can drain the battery through each individual attack, as well as their combinations. Finally, we believe energy exploit will be a practical attack vector and mobile users should be aware of this type of attacks.
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Todays mobile devices contain densely packaged system-on-chips (SoCs) with multi-core, high-frequency CPUs and complex pipelines. In parallel, sophisticated SoC-assisted security mechanisms have become commonplace for protecting device data, such as trusted execution environments, full-disk and file-based encryption. Both advancements have dramatically complicated the use of conventional physical attacks, requiring the development of specialised attacks. In this survey, we consolidate recent developments in physical fault injections and side-channel attacks on modern mobile devices. In total, we comprehensively survey over 50 fault injection and side-channel attack papers published between 2009-2021. We evaluate the prevailing methods, compare existing attacks using a common set of criteria, identify several challenges and shortcomings, and suggest future directions of research.
Voice input has been tremendously improving the user experience of mobile devices by freeing our hands from typing on the small screen. Speech recognition is the key technology that powers voice input, and it is usually outsourced to the cloud for the best performance. However, the cloud might compromise users privacy by identifying their identities by voice, learning their sensitive input content via speech recognition, and then profiling the mobile users based on the content. In this paper, we design an intermediate between users and the cloud, named VoiceMask, to sanitize users voice data before sending it to the cloud for speech recognition. We analyze the potential privacy risks and aim to protect users identities and sensitive input content from being disclosed to the cloud. VoiceMask adopts a carefully designed voice conversion mechanism that is resistant to several attacks. Meanwhile, it utilizes an evolution-based keyword substitution technique to sanitize the voice input content. The two sanitization phases are all performed in the resource-limited mobile device while still maintaining the usability and accuracy of the cloud-supported speech recognition service. We implement the voice sanitizer on Android systems and present extensive experimental results that validate the effectiveness and efficiency of our app. It is demonstrated that we are able to reduce the chance of a users voice being identified from 50 people by 84% while keeping the drop of speech recognition accuracy within 14.2%.
Currently, Android malware detection is mostly performed on server side against the increasing number of malware. Powerful computing resource provides more exhaustive protection for app markets than maintaining detection by a single user. However, apart from the applications provided by the official market, apps from unofficial markets and third-party resources are always causing serious security threats to end-users. Meanwhile, it is a time-consuming task if the app is downloaded first and then uploaded to the server side for detection, because the network transmission has a lot of overhead. In addition, the uploading process also suffers from the security threats of attackers. Consequently, a last line of defense on mobile devices is necessary and much-needed. In this paper, we propose an effective Android malware detection system, MobiTive, leveraging customized deep neural networks to provide a real-time and responsive detection environment on mobile devices. MobiTive is a preinstalled solution rather than an app scanning and monitoring engine using after installation, which is more practical and secure. Original deep learning models cannot be directly deployed and executed on mobile devices due to various performance limitations, such as computation power, memory size, and energy. Therefore, we evaluate and investigate the following key points:(1) the performance of different feature extraction methods based on source code or binary code;(2) the performance of different feature type selections for deep learning on mobile devices;(3) the detection accuracy of different deep neural networks on mobile devices;(4) the real-time detection performance and accuracy on different mobile devices;(5) the potential based on the evolution trend of mobile devices specifications; and finally we further propose a practical solution (MobiTive) to detect Android malware on mobile devices.
Androids security model severely limits the capabilities of anti-malware software. Unlike commodity anti-malware solutions on desktop systems, their Android counterparts run as sandboxed applications without root privileges and are limited by Androids permission system. As such, PHAs on Android are usually willingly installed by victims, as they come disguised as useful applications with hidden malicious functionality, and are encountered on mobile app stores as suggestions based on the apps that a user previously installed. Users with similar interests and app installation history are likely to be exposed and to decide to install the same PHA. This observation gives us the opportunity to develop predictive approaches that can warn the user about which PHAs they will encounter and potentially be tempted to install in the near future. These approaches could then be used to complement commodity anti-malware solutions, which are focused on post-fact detection, closing the window of opportunity that existing solutions suffer from. In this paper we develop Andruspex, a system based on graph representation learning, allowing us to learn latent relationships between user devices and PHAs and leverage them for prediction. We test Andruspex on a real world dataset of PHA installations collected by a security company, and show that our approach achieves very high prediction results (up to 0.994 TPR at 0.0001 FPR), while at the same time outperforming alternative baseline methods. We also demonstrate that Andruspex is robust and its runtime performance is acceptable for a real world deployment.
With the proliferation of Android malware, the demand for an effective and efficient malware detection system is on the rise. The existing device-end learning based solutions tend to extract limited syntax features (e.g., permissions and API calls) to meet a certain time constraint of mobile devices. However, syntax features lack the semantics which can represent the potential malicious behaviors and further result in more robust model with high accuracy for malware detection. In this paper, we propose an efficient Android malware detection system, named SeqMobile, which adopts behavior-based sequence features and leverages customized deep neural networks on mobile devices instead of the server. Different from the traditional sequence-based approaches on server, to meet the performance demand, SeqMobile accepts three effective performance optimization methods to reduce the time cost. To evaluate the effectiveness and efficiency of our system, we conduct experiments from the following aspects 1) the detection accuracy of different recurrent neural networks; 2) the feature extraction performance on different mobile devices, 3) the detection accuracy and prediction time cost of different sequence lengths. The results unveil that SeqMobile can effectively detect malware with high accuracy. Moreover, our performance optimization methods have proven to improve the performance of training and prediction by at least twofold. Additionally, to discover the potential performance optimization from the SOTA TensorFlow model optimization toolkit for our approach, we also provide an evaluation on the toolkit, which can serve as a guidance for other systems leveraging on sequence-based learning approach. Overall, we conclude that our sequence-based approach, together with our performance optimization methods, enable us to detect malware under the performance demands of mobile devices.
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