Journal ArticleDOI
AMAL: High-fidelity, behavior-based automated malware analysis and classification
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TLDR
An evaluation of both AutoMal and MaLabel based on medium-scale and large-scale datasets shows AMAL's effectiveness in accurately characterizing, classifying, and grouping malware samples, and several benchmarks, cost estimates and measurements highlight the merits of AMAL.About:
This article is published in Computers & Security.The article was published on 2015-07-01. It has received 177 citations till now. The article focuses on the topics: Malware analysis & Malware.read more
Citations
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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
A state-of-the-art survey of malware detection approaches using data mining techniques
Alireza Souri,Rahil Hosseini +1 more
TL;DR: A systematic and detailed survey of the malware detection mechanisms using data mining techniques and classifies the malware Detection approaches in two main categories including signature-based methods and behavior-based detection.
Journal ArticleDOI
Dynamic Malware Analysis in the Modern Era—A State of the Art Survey
TL;DR: A comprehensive and up-to-date overview of existing methods used to dynamically analyze malware is provided, which includes a description of each method, its strengths and weaknesses, and its resilience against malware evasion techniques.
Journal ArticleDOI
A Survey on Malware Analysis Techniques: Static, Dynamic, Hybrid and Memory Analysis
TL;DR: A semantic and detailed survey of methods used for malware detection like signature-based and heuristic-based, and the importance of memory-based analysis in malware detection is discussed.
References
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LIBLINEAR: A Library for Large Linear Classification
TL;DR: LIBLINEAR is an open source library for large-scale linear classification that supports logistic regression and linear support vector machines and provides easy-to-use command-line tools and library calls for users and developers.
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Introduction to Machine Learning
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Proceedings ArticleDOI
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
Robin Sommer,Vern Paxson +1 more
TL;DR: The main claim is that the task of finding attacks is fundamentally different from these other applications, making it significantly harder for the intrusion detection community to employ machine learning effectively.
Proceedings Article
BotMiner: clustering analysis of network traffic for protocol- and structure-independent botnet detection
TL;DR: This paper presents a general detection framework that is independent of botnet C&C protocol and structure, and requires no a priori knowledge of botnets (such as captured bot binaries and hence the botnet signatures, and C &C server names/addresses).
Proceedings ArticleDOI
A dual coordinate descent method for large-scale linear SVM
TL;DR: A novel dual coordinate descent method for linear SVM with L1-and L2-loss functions that reaches an ε-accurate solution in O(log(1/ε)) iterations is presented.