Mobile malware has been growing in scale and complexity as smartphone usage
continues to rise. Android has surpassed other mobile platforms as the most
popular whilst also witnessing a dramatic increase in malware targeting the
platform. A worrying trend that is emerging is the increasing sophistication of
Android malware to evade detection by traditional signature-based scanners. As
such, Android app marketplaces remain at risk of hosting malicious apps that
could evade detection before being downloaded by unsuspecting users. Hence, in
this paper we present an effective approach to alleviate this problem based on
Bayesian classification models obtained from static code analysis. The models
are built from a collection of code and app characteristics that provide
indicators of potential malicious activities. The models are evaluated with
real malware samples in the wild and results of experiments are presented to
demonstrate the effectiveness of the proposed approach.Comment: IEEE 27th International Conference on Advanced Information Networking
and Applications (AINA 2013),pp.121-128, 25-28 March 201
Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, in this paper we develop and analyze proactive Machine Learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification based solutions for detecting unknown Android malware.
Funded under the European Union's Horizon 2020 research and innovation programme, SAFEcrypto will provide a new generation of practical, robust and physically secure postquantum cryptographic solutions that ensure long-term security for future ICT systems, services and applications. The project will focus on the remarkably versatile field of Lattice-based cryptography as the source of computational hardness, and will deliver optimised public key security primitives for digital signatures and authentication, as well identity based encryption (IBE) and attribute based encryption (ABE). This will involve algorithmic and design optimisations, and implementations of lattice-based cryptographic schemes addressing cost, energy consumption, performance and physical robustness. As the National Institute of Standards and Technology (NIST) prepares for the transition to a post-quantum cryptographic suite B, urging organisations that build systems and infrastructures that require longterm security to consider this transition in architectural designs; the SAFEcrypto project will provide Proof-of-concept demonstrators of schemes for three practical real-world case studies with long-term security requirements, in the application areas of satellite communications, network security and cloud. The goal is to affirm Lattice-based cryptography as an effective replacement for traditional number-theoretic * Principal Investigator.public-key cryptography, by demonstrating that it can address the needs of resource-constrained embedded applications, such as mobile and battery-operated devices, and of real-time high performance applications for cloud and network management infrastructures.
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