Journal ArticleDOI
Opcode sequences as representation of executables for data-mining-based unknown malware detection
TLDR
This paper proposes a new method to detect unknown malware families based on the frequency of the appearance of opcode sequences, and describes a technique to mine the relevance of each opcode and assess the Frequency of Each opcode sequence.About:
This article is published in Information Sciences.The article was published on 2013-05-01. It has received 370 citations till now. The article focuses on the topics: Malware & Cryptovirology.read more
Citations
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An essay towards solving a problem in the doctrine of chances. [Facsimil]
TL;DR: The probability of any event is the ratio between the value at which an expectation depending on the happening of the event ought to be computed, and the value of the thing expected upon it’s 2 happening.
Journal ArticleDOI
A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting
Hamed HaddadPajouh,Ali Dehghantanha,Raouf Khayami,Kim-Kwang Raymond Choo,Kim-Kwang Raymond Choo +4 more
TL;DR: The potential of using Recurrent Neural Network (RNN) deep learning in detecting IoT malware by using RNN to analyze ARM-based IoT applications’ execution operation codes (OpCodes) is explored.
Journal ArticleDOI
Survey of machine learning techniques for malware analysis
TL;DR: This survey aims at providing an overview on the way machine learning has been used so far in the context of malware analysis in Windows environments, i.e. for the analysis of Portable Executables.
Journal ArticleDOI
The rise of machine learning for detection and classification of malware: Research developments, trends and challenges
TL;DR: This survey aims at providing a systematic and detailed overview of machine learning techniques for malware detection and in particular, deep learning techniques with special emphasis on deep learning approaches.
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.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
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Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
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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.
Journal ArticleDOI
Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.