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Nmap Cookbook: The Fat-free Guide to Network Scanning - download pdf or read online

By Nicholas Marsh

ISBN-10: 1449902529

ISBN-13: 9781449902520

Nmap(r) Cookbook: The fat-free consultant to community scanning offers simplified assurance of community scanning positive factors to be had within the Nmap suite of utilities. each Nmap function is roofed with visible examples that can assist you quick comprehend and establish right utilization for useful effects. subject matters coated contain:

* deploy on home windows, Mac OS X, Unix/Linux systems
* simple and complicated scanning options
* community stock and protection auditing
* Firewall evasion concepts
* Zenmap - A graphical front-end for Nmap
* NSE - The Nmap Scripting Engine
* Ndiff - A Nmap experiment comparability application Simplified insurance of Nmap 5.00 positive factors.

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Extra info for Nmap Cookbook: The Fat-free Guide to Network Scanning

Example text

Rate and negative accuracy T N. Rate) [41]. The Gmean measure represents a trade-off between positive accuracy T P. Rate and negative accuracy T N. Rate. F1 = 2 · TP 2 · T P + FP + FN (6) Gmean = TN TP · T P + FN T N + FP (7) Accuracy = TP + TN T P + FN + T N + FP (8) Where – T P (true positive): the number of examples correctly labelled as belonging to the positive class; – FN (false negative): the number of positive examples labelled as negative class; – T N (true negative): the number of examples correctly labelled as belonging to the negative class; – FP (false positive): the number of negative examples labelled as positive class.

In this case investigation data was collected and seized from a lot of sources: internet wiretaps, forensic disc images from tens of workstations, user data from server systems, e-mail servers with literally millions of e-mails, company databases, webservers, and the complete banking Performance Evaluation of a NLP Approach 33 back-end systems containing all bank transactions. This investigation had the characteristics of Big Data on both levels, a high complexity of the data (the complete banking system had to be reconstructed and analysed) and a high amount of total data (the house searches were in 2006, and in that time a total of 15 terabyte of data was seized).

Their main objective is how to optimise the searching queries. 3 LES Tool and Method of Evaluation In this section, we present briefly LES, a NLP based tool that has been developed to study the possibilities and benefits the usage of NLP techniques can provide in complex fraud investigations. Next, we describe the investigating process where we apply LES tool to analyse evidence files. Finally we present methods we used to evaluate this tool. 1 LES Tool Because of the problems of handing Big Data investigations mentioned earlier, our fraud investigation unit decided to develop tooling in-house that would be able to handle these specific types of investigations.

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Nmap Cookbook: The Fat-free Guide to Network Scanning by Nicholas Marsh


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