<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/CINECAstyle.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-10-10T20:12:28Z</responseDate><request verb="GetRecord" identifier="oai:iris.cnr.it:20.500.14243/139482" metadataPrefix="oai_dc">https://iris.cnr.it/oai/request</request><GetRecord><record><header><identifier>oai:iris.cnr.it:20.500.14243/139482</identifier><datestamp>2024-04-10T12:08:50Z</datestamp><setSpec>com_20.500.14243_29</setSpec><setSpec>com_20.500.14243_21</setSpec><setSpec>col_20.500.14243_30</setSpec><setSpec>ou_ou198</setSpec><setSpec>ou_ou239</setSpec><setSpec>ou_ou627</setSpec></header><metadata><oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:doc="http://www.lyncode.com/xoai" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
<dc:title>Analysis, Development and Deployment of Statistical Anomaly Detection Techniques for real e-mail Traffic</dc:title>
<dc:creator>G Papaleo</dc:creator>
<dc:creator>D Chiarella</dc:creator>
<dc:creator>M Aiello</dc:creator>
<dc:creator>L Caviglione</dc:creator>
<dc:contributor>Papaleo, G</dc:contributor>
<dc:contributor> Chiarella, D</dc:contributor>
<dc:contributor> Aiello, M</dc:contributor>
<dc:contributor> Caviglione, L</dc:contributor>
<dc:description>Even if new interaction paradigms, such as the Voice over IP (VoIP), are becoming popular and widely 
adopted, the e-mail is still one of the most utilized ways to communicate across the Internet. However, many 
malicious threats are conveyed via e-mails. Usually, the authors can exploit two different approaches: 
i) analyzing the logs produced by e-mail servers or ii) reconstruct the e-mail flows by capturing data 
directly from the network by placing ad-hoc probes. In this vein, this Chapter discusses the analysis, 
development and deployment of statistical detection techniques aimed at the detection of Internet worms. 
For what concerns i), they introduce a tool called Log Mail Analyzer (LMA), which allows to overcome 
the complexity of inspecting multiple logs created from a heterogeneous population of mail servers. In 
the perspective of ii) they briefly discuss an alternative solution, based on ad-hoc network probes, to be 
properly placed to collect traffic and then reconstruct the e-mail flow to be monitored. Lastly, the authors 
introduce a threshold mechanism, based on a simple statistical framework, to automatically detect and 
identify different worm activities.</dc:description>
<dc:date>2011</dc:date>
<dc:type>info:eu-repo/semantics/bookPart</dc:type>
<dc:identifier>https://hdl.handle.net/20.500.14243/139482</dc:identifier>
<dc:identifier>10.4018/978-1-61350-507-6.ch003</dc:identifier>
<dc:identifier>info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-84898396318</dc:identifier>
<dc:identifier>http://www.igi-global.com/chapter/attacks-systems-categories-motives/61218</dc:identifier>
<dc:language>eng</dc:language>
<dc:relation>ispartofbook:Information Assurance and Security Technologies for Risk Assessment and Threat Management: Advances</dc:relation>
<dc:relation>firstpage:47</dc:relation>
<dc:relation>lastpage:71</dc:relation>
<dc:publisher>IGI Global</dc:publisher>
<dc:publisher>country:USA</dc:publisher>
<dc:publisher>place:Hershey</dc:publisher>
</oai_dc:dc></metadata></record></GetRecord></OAI-PMH>