1,370 research outputs found
ConXsense - Automated Context Classification for Context-Aware Access Control
We present ConXsense, the first framework for context-aware access control on
mobile devices based on context classification. Previous context-aware access
control systems often require users to laboriously specify detailed policies or
they rely on pre-defined policies not adequately reflecting the true
preferences of users. We present the design and implementation of a
context-aware framework that uses a probabilistic approach to overcome these
deficiencies. The framework utilizes context sensing and machine learning to
automatically classify contexts according to their security and privacy-related
properties. We apply the framework to two important smartphone-related use
cases: protection against device misuse using a dynamic device lock and
protection against sensory malware. We ground our analysis on a sociological
survey examining the perceptions and concerns of users related to contextual
smartphone security and analyze the effectiveness of our approach with
real-world context data. We also demonstrate the integration of our framework
with the FlaskDroid architecture for fine-grained access control enforcement on
the Android platform.Comment: Recipient of the Best Paper Awar
In-Vivo Bytecode Instrumentation for Improving Privacy on Android Smartphones in Uncertain Environments
In this paper we claim that an efficient and readily applicable means to
improve privacy of Android applications is: 1) to perform runtime monitoring by
instrumenting the application bytecode and 2) in-vivo, i.e. directly on the
smartphone. We present a tool chain to do this and present experimental results
showing that this tool chain can run on smartphones in a reasonable amount of
time and with a realistic effort. Our findings also identify challenges to be
addressed before running powerful runtime monitoring and instrumentations
directly on smartphones. We implemented two use-cases leveraging the tool
chain: BetterPermissions, a fine-grained user centric permission policy system
and AdRemover an advertisement remover. Both prototypes improve the privacy of
Android systems thanks to in-vivo bytecode instrumentation.Comment: ISBN: 978-2-87971-111-
Permission based Mobile Malware Detection System using Machine Learning Techniques
Mobile technology has grown dramatically around the world. Nowadays smart mobile devices are ubiquitous, i.e. they serve multiple purposes such as personal mobile communication, data storage, multimedia and entertainment etc. They have become important part of life. Implementing secure mobile and wireless networks is crucial for enterprises operating in the Internet-based business environment. Mobile market share has grown significantly in past few years so that we need to think about mobile security. Mobile security can be compromised due to design flaws, vulnerabilities, and protocol failures in any mobile applications, viruses, spyware, malware and other threats. In this paper we will more focus on mobile malware. Many tools are available in the market to detect malware but new research trend in the mobile security is users should be aware of app before he/she installs from the app store. Hence we propose a novel approach for permission based mobile malware detection system. It is based on static analysis. It has 3 major parts in it 1) a signature database for storing analysis results of training and testing. 2) An Android client who is used by end users for making analysis requests, and 3) a central server plays important role as it communicates with both signature database and smartphone client. We can say that he is the manager of whole analysis process. It alerts user if the app is malicious or the benign based on it user can proceed whether to continue with it or not
TRAWL: Protection against rogue sites for the masses
The number of smartphones reached 3.4 billion in the third quarter of 2016 [1]. These devices facilitate our daily lives and have become the primary way of accessing the web. Although all desktop browsers filter rogue websites, their mobile counterparts often do not filter them at all, exposing their users to websites serving malware or hosting phishing attacks. In this paper we revisit the anti-phishing filtering mechanism which is offered in the most popular web browsers of Android, iOS and Windows Phone. Our results show that mobile users are still unprotected against phishing attacks, as most of the browsers are unable to filter phishing URLs. Thus, we implement and evaluate TRAWL (TRAnsparent Web protection for alL), as a cost effective security control that provides DNS and URL filtering using several blacklists
The Android Platform Security Model
Android is the most widely deployed end-user focused operating system. With
its growing set of use cases encompassing communication, navigation, media
consumption, entertainment, finance, health, and access to sensors, actuators,
cameras, or microphones, its underlying security model needs to address a host
of practical threats in a wide variety of scenarios while being useful to
non-security experts. The model needs to strike a difficult balance between
security, privacy, and usability for end users, assurances for app developers,
and system performance under tight hardware constraints. While many of the
underlying design principles have implicitly informed the overall system
architecture, access control mechanisms, and mitigation techniques, the Android
security model has previously not been formally published. This paper aims to
both document the abstract model and discuss its implications. Based on a
definition of the threat model and Android ecosystem context in which it
operates, we analyze how the different security measures in past and current
Android implementations work together to mitigate these threats. There are some
special cases in applying the security model, and we discuss such deliberate
deviations from the abstract model
Harnessing the Speed and Accuracy of Machine Learning to Advance Cybersecurity
As cyber attacks continue to increase in frequency and sophistication,
detecting malware has become a critical task for maintaining the security of
computer systems. Traditional signature-based methods of malware detection have
limitations in detecting complex and evolving threats. In recent years, machine
learning (ML) has emerged as a promising solution to detect malware
effectively. ML algorithms are capable of analyzing large datasets and
identifying patterns that are difficult for humans to identify. This paper
presents a comprehensive review of the state-of-the-art ML techniques used in
malware detection, including supervised and unsupervised learning, deep
learning, and reinforcement learning. We also examine the challenges and
limitations of ML-based malware detection, such as the potential for
adversarial attacks and the need for large amounts of labeled data.
Furthermore, we discuss future directions in ML-based malware detection,
including the integration of multiple ML algorithms and the use of explainable
AI techniques to enhance the interpret ability of ML-based detection systems.
Our research highlights the potential of ML-based techniques to improve the
speed and accuracy of malware detection, and contribute to enhancing
cybersecurit
- …