Journal ArticleDOI
A review on feature selection in mobile malware detection
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TLDR
This paper studied 100 research works published between 2010 and 2014 with the perspective of feature selection in mobile malware detection, and categorizes available features into four groups, namely, static features, dynamic features, hybrid features and applications metadata.About:
This article is published in Digital Investigation.The article was published on 2015-06-01. It has received 190 citations till now. The article focuses on the topics: Mobile malware & Mobile search.read more
Citations
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Journal ArticleDOI
Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning
TL;DR: This paper transmute OpCodes into a vector space and applies a deep Eigenspace learning approach to classify malicious and benign applications and presents a deep learning based method to detect Internet of Battlefield Things malware via the device’s Operational Code (OpCode) sequence.
Journal ArticleDOI
AndroDialysis: Analysis of Android Intent Effectiveness in Malware Detection
TL;DR: It is shown that Intents are semantically rich features that are able to encode the intentions of malware when compared to other well-studied features such as permissions, and it is argued that this type of feature is not the ultimate solution.
Journal ArticleDOI
A Comprehensive Review on Malware Detection Approaches
Omer Aslan,Refik Samet +1 more
TL;DR: This paper presents a detailed review on malware detection approaches and recent detection methods which use these approaches, and the pros and cons of each detection approach, and methods that are used in these approaches.
Journal ArticleDOI
A Review of Android Malware Detection Approaches Based on Machine Learning
TL;DR: This paper presents a comprehensive survey of Android malware detection approaches based on machine learning and analyzes the research status from key perspectives such as sample acquisition, data preprocessing, feature selection, machine learning models, algorithms, and the evaluation of detection effectiveness.
Journal ArticleDOI
A Comprehensive Analysis of Nature-Inspired Meta-Heuristic Techniques for Feature Selection Problem
Manik Sharma,Prableen Kaur +1 more
TL;DR: The research gaps have been identified for the researcher who inclines to design or analyze the performance of divergent meta-heuristic techniques in solving feature selection problem and the detailed publication trend of meta- heuristic feature selection approaches has been presented.
References
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Journal ArticleDOI
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
Book
Modern Information Retrieval
TL;DR: In this article, the authors present a rigorous and complete textbook for a first course on information retrieval from the computer science (as opposed to a user-centred) perspective, which provides an up-to-date student oriented treatment of the subject.
Proceedings ArticleDOI
The relationship between Precision-Recall and ROC curves
Jesse Davis,Mark Goadrich +1 more
TL;DR: It is shown that a deep connection exists between ROC space and PR space, such that a curve dominates in R OC space if and only if it dominates in PR space.
Proceedings ArticleDOI
TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones
William Enck,Peter Gilbert,Byung-Gon Chun,Landon P. Cox,Jaeyeon Jung,Patrick McDaniel,Anmol Sheth +6 more
TL;DR: Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, this work found 68 instances of misappropriation of users' location and device identification information across 20 applications.
Proceedings ArticleDOI
Dissecting Android Malware: Characterization and Evolution
Yajin Zhou,Xuxian Jiang +1 more
TL;DR: Systematize or characterize existing Android malware from various aspects, including their installation methods, activation mechanisms as well as the nature of carried malicious payloads reveal that they are evolving rapidly to circumvent the detection from existing mobile anti-virus software.