Journal ArticleDOI
Using Dalvik opcodes for malware detection on android
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TLDR
An approach to detect malware on Android is presented, by using the techniques of reverse engineering and putting an emphasis on operational codes used for these applications.Abstract:
Over the last few years, computers and smartphones have become essential tools in our ways of communicating with each-other. Nowadays, the amount of applications in the Google store has grown exponentially, therefore, malware developers have introduced malicious applications in that market. The Android system uses the Dalvik virtual machine. Through reverse engineering, we may be able to get the di erent opcodes for each application. For this reason, in this paper an approach to detect malware on Android is presented, by using the techniques of reverse engineering and putting an emphasis on operational codes used for these applications. After obtaining these opcodes, machine learning techniques are used to classify apps.read more
Citations
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Journal ArticleDOI
A feature-hybrid malware variants detection using CNN based opcode embedding and BPNN based API embedding
TL;DR: This work proposes a feature-hybrid malware variants detection approach which integrates multi-types of features and achieves more than 95% malware detection accuracy and almost 90% classification accuracy of malware families.
Journal ArticleDOI
Dalvik Opcode Graph Based Android Malware Variants Detection Using Global Topology Features
TL;DR: A novel method to build a graph of Dalvik opcode and analyze its global topology properties, which will first construct a weighted probability graph of operations, and use information entropy to prune this graph while retaining information as more as possible to detect Android malware variants.
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Opcode sequence analysis of Android malware by a convolutional neural network
TL;DR: An optimized deep convolutional neural network is trained multiple times by the raw opcode sequences extracted from the decompiled Android file, so that the feature information is effectively learned and the malicious program can be detected more accurately.
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An efficient combined deep neural network based malware detection framework in 5G environment
TL;DR: In this article, the authors proposed an efficient malware detection framework based on deep neural network called DLAMD that can face large-scale samples, which combines the pre-detection phase of rapid detection and the deep detection phase of deep detection.
Proceedings ArticleDOI
DeepIntent: ImplicitIntent based Android IDS with E2E Deep Learning architecture
TL;DR: This work conducts exhaustive experiments with over 40 different end-to-end Deep Learning configurations of Auto-Encoders and Multi-Layer-Perceptron to create a benchmark for a malware classifier that works exclusively on implicit Intent.
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Book
C4.5: Programs for Machine Learning
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Programs for Machine Learning
Steven L. Salzberg,Alberto Segre +1 more
TL;DR: In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments, which will be a welcome addition to the library of many researchers and students.
Journal Article
Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines
TL;DR: The sequential minimal optimization (SMO) algorithm as mentioned in this paper uses a series of smallest possible QP problems to solve a large QP problem, which avoids using a time-consuming numerical QP optimization as an inner loop.
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.