Open AccessPosted Content
A Survey on Honeypot Software and Data Analysis.
TLDR
In this survey, an extensive overview on honeypots is given, including not only honeypot software but also methodologies to analyse honeypot data.Abstract:
In this survey, we give an extensive overview on honeypots. This includes not only honeypot software but also methodologies to analyse honeypot data.read more
Citations
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Proceedings ArticleDOI
A Honeypot with Machine Learning based Detection Framework for defending IoT based Botnet DDoS Attacks
TL;DR: A honeypot-based approach which uses machine learning techniques for malware detection and the IoT honeypot generated data is used as a dataset for the effective and dynamic training of a machine learning model.
Journal ArticleDOI
Enabling an Anatomic View to Investigate Honeypot Systems: A Survey
TL;DR: A novel decoy and captor (D-C) based taxonomy is proposed for the purpose of studying and classifying the various honeypot techniques and two subsets of features from the taxonomy are identified, which can greatly influence the honeypot performances.
Journal ArticleDOI
Systematically Understanding the Cyber Attack Business: A Survey
TL;DR: An extensive and consistent survey of the services used by the cybercrime business is conducted, organized using the value chain perspective, to understand cyber attack in a systematic way and identify 24 key value-added activities and their relations.
Journal ArticleDOI
Deception Techniques in Computer Security: A Research Perspective
TL;DR: A comprehensive classification of existing solutions is introduced and the current application of deception techniques in computer security is surveyed, including the design of strategies to help defenders to design and integrate deception within a target architecture.
Journal ArticleDOI
On data-driven curation, learning, and analysis for inferring evolving internet-of-Things (IoT) botnets in the wild
Morteza Safaei Pour,Antonio Mangino,Kurt Friday,Matthias Rathbun,Elias Bou-Harb,Farkhund Iqbal,Sagar Samtani,Jorge Crichigno,Nasir Ghani +8 more
TL;DR: This work aims to classify and infer Internet-scale compromised IoT devices by solely observing one-way network traffic, while also uncovering, reporting and thoroughly analyzing “in the wild” IoT botnets, and makes the source codes of all the developed methods and techniques available to the research community at large.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
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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.
Proceedings ArticleDOI
Automatic subspace clustering of high dimensional data for data mining applications
TL;DR: CLIQUE is presented, a clustering algorithm that satisfies each of these requirements of data mining applications including the ability to find clusters embedded in subspaces of high dimensional data, scalability, end-user comprehensibility of the results, non-presumption of any canonical data distribution, and insensitivity to the order of input records.