10,240 research outputs found
Longitudinal performance analysis of machine learning based Android malware detectors
This paper presents a longitudinal study of the performance of machine learning classifiers for Android malware detection. The study is undertaken using features extracted from Android applications first seen between 2012 and 2016. The aim is to investigate the extent of performance decay over time for various machine learning classifiers trained with static features extracted from date-labelled benign and malware application sets. Using date-labelled apps allows for true mimicking of zero-day testing, thus providing a more realistic view of performance than the conventional methods of evaluation that do not take date of appearance into account. In this study, all the investigated machine learning classifiers showed progressive diminishing performance when tested on sets of samples from a later time period. Overall, it was found that false positive rate (misclassifying benign samples as malicious) increased more substantially compared to the fall in True Positive rate (correct classification of malicious apps) when older models were tested on newer app samples
CERN openlab Whitepaper on Future IT Challenges in Scientific Research
This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates
Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild
In this paper, we seek to better understand Android obfuscation and depict a
holistic view of the usage of obfuscation through a large-scale investigation
in the wild. In particular, we focus on four popular obfuscation approaches:
identifier renaming, string encryption, Java reflection, and packing. To obtain
the meaningful statistical results, we designed efficient and lightweight
detection models for each obfuscation technique and applied them to our massive
APK datasets (collected from Google Play, multiple third-party markets, and
malware databases). We have learned several interesting facts from the result.
For example, malware authors use string encryption more frequently, and more
apps on third-party markets than Google Play are packed. We are also interested
in the explanation of each finding. Therefore we carry out in-depth code
analysis on some Android apps after sampling. We believe our study will help
developers select the most suitable obfuscation approach, and in the meantime
help researchers improve code analysis systems in the right direction
"On the Road" - Reflections on the Security of Vehicular Communication Systems
Vehicular communication (VC) systems have recently drawn the attention of
industry, authorities, and academia. A consensus on the need to secure VC
systems and protect the privacy of their users led to concerted efforts to
design security architectures. Interestingly, the results different project
contributed thus far bear extensive similarities in terms of objectives and
mechanisms. As a result, this appears to be an auspicious time for setting the
corner-stone of trustworthy VC systems. Nonetheless, there is a considerable
distance to cover till their deployment. This paper ponders on the road ahead.
First, it presents a distillation of the state of the art, covering the
perceived threat model, security requirements, and basic secure VC system
components. Then, it dissects predominant assumptions and design choices and
considers alternatives. Under the prism of what is necessary to render secure
VC systems practical, and given possible non-technical influences, the paper
attempts to chart the landscape towards the deployment of secure VC systems
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