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From 31790-31995-4808-6033-christian.gabriel=ift-informatik.de@mail.rofongbsuns.bid  Tue May  8 17:29:14 2018
Return-Path: <31790-31995-4808-6033-christian.gabriel=ift-informatik.de@mail.rofongbsuns.bid>
X-Original-To: cgabriel@ift-informatik.de
Delivered-To: cgabriel@ift-informatik.de
Received: by ift-informatik.de (Postfix, from userid 5555)
	id F2B363D20003A; Tue,  8 May 2018 17:29:13 +0200 (CEST)
Received: from localhost by h2486555.stratoserver.net
	with SpamAssassin (version 3.4.0);
	Tue, 08 May 2018 17:29:13 +0200
From: "Roofing Business Sales**" <info@rofongbsuns.bid>
To: <christian.gabriel@ift-informatik.de>
Subject: *****SPAM***** *Over 22 Million Dollers In Roofing sales And Climbing...
Date: Tue, 8 May 2018 11:16:39 -0400
Message-Id: <k7z4il6zdup3vjju-ncqlblklirp69gdl-7cfb-12c8@rofongbsuns.bid>
X-Spam-Checker-Version: SpamAssassin 3.4.0 (2014-02-07) on
	h2486555.stratoserver.net
X-Spam-Flag: YES
X-Spam-Level: ******
X-Spam-Status: Yes, score=6.6 required=5.0 tests=BAYES_00,DKIM_SIGNED,
	DKIM_VALID,DKIM_VALID_AU,HTML_FONT_LOW_CONTRAST,HTML_MESSAGE,LOTS_OF_MONEY,
	RAZOR2_CF_RANGE_51_100,RAZOR2_CF_RANGE_E8_51_100,RAZOR2_CHECK,RCVD_IN_PSBL,
	RDNS_NONE,T_REMOTE_IMAGE,US_DOLLARS_3 autolearn=no autolearn_force=no
	version=3.4.0
MIME-Version: 1.0
Content-Type: multipart/mixed; boundary="----------=_5AF1C249.917FF43B"

This is a multi-part message in MIME format.

------------=_5AF1C249.917FF43B
Content-Type: text/plain; charset=iso-8859-1
Content-Disposition: inline
Content-Transfer-Encoding: 8bit

Spam detection software, running on the system "h2486555.stratoserver.net",
has identified this incoming email as possible spam.  The original
message has been attached to this so you can view it or label
similar future email.  If you have any questions, see
@@CONTACT_ADDRESS@@ for details.

Content preview:  *Over 22 Million Dollers In Roofing sales And Climbing...
  http://rofongbsuns.bid/uHUwyQch8PO9fl6Gz5O1T3YR8yOFiiGQaTBMMvTE42dmVvQ_31790_31995
   http://rofongbsuns.bid/_Jz_IrRmVjjtwdRzQ4RsSkYmJ3VcwvSVdXPx-vZYgt9r-y4_31790_31995
   [...] 

Content analysis details:   (6.6 points, 5.0 required)

 pts rule name              description
---- ---------------------- --------------------------------------------------
 2.7 RCVD_IN_PSBL           RBL: Received via a relay in PSBL
                            [103.70.136.107 listed in psbl.surriel.com]
 1.8 US_DOLLARS_3           BODY: Mentions millions of $ ($NN,NNN,NNN.NN)
-1.9 BAYES_00               BODY: Bayes spam probability is 0 to 1%
                            [score: 0.0000]
 0.0 HTML_MESSAGE           BODY: HTML included in message
 0.0 HTML_FONT_LOW_CONTRAST BODY: HTML font color similar or identical to
                            background
 0.5 RAZOR2_CF_RANGE_51_100 Razor2 gives confidence level above 50%
                            [cf: 100]
-0.1 DKIM_VALID_AU          Message has a valid DKIM or DK signature from author's
                            domain
 0.9 RAZOR2_CHECK           Listed in Razor2 (http://razor.sf.net/)
 1.9 RAZOR2_CF_RANGE_E8_51_100 Razor2 gives engine 8 confidence level
                            above 50%
                            [cf: 100]
-0.1 DKIM_VALID             Message has at least one valid DKIM or DK signature
 0.1 DKIM_SIGNED            Message has a DKIM or DK signature, not necessarily valid
 0.0 LOTS_OF_MONEY          Huge... sums of money
 0.8 RDNS_NONE              Delivered to internal network by a host with no rDNS
 0.0 T_REMOTE_IMAGE         Message contains an external image

The original message was not completely plain text, and may be unsafe to
open with some email clients; in particular, it may contain a virus,
or confirm that your address can receive spam.  If you wish to view
it, it may be safer to save it to a file and open it with an editor.


------------=_5AF1C249.917FF43B
Content-Type: message/rfc822; x-spam-type=original
Content-Description: original message before SpamAssassin
Content-Disposition: attachment
Content-Transfer-Encoding: 8bit

Received: from vince.rofongbsuns.bid (unknown [103.70.136.107])
	by ift-informatik.de (Postfix) with ESMTP id 220833D20003A
	for <christian.gabriel@ift-informatik.de>; Tue,  8 May 2018 17:29:10 +0200 (CEST)
DKIM-Signature: v=1; a=rsa-sha1; c=relaxed/relaxed; s=k1; d=rofongbsuns.bid;
 h=Mime-Version:Content-Type:Date:From:Reply-To:Subject:To:Message-ID; i=info@rofongbsuns.bid;
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DomainKey-Signature: a=rsa-sha1; c=nofws; q=dns; s=k1; d=rofongbsuns.bid;
 b=z6GdDnstRxDoJ5Fp1DoLTC3v5uCXyz3QOPaBHUugg4/mddN1SzwWuuMdRp5hj6qwx9qsbaC+jxaP
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Mime-Version: 1.0
Content-Type: multipart/alternative; boundary="c46d54b90245868ae30bd5c72a22e78d_7cfb_12c8"
Date: Tue, 8 May 2018 11:16:39 -0400
From: "Roofing Business Sales**" <info@rofongbsuns.bid>
Reply-To: "Roofing Business Sales**" <support@rofongbsuns.bid>
Subject: *Over 22 Million Dollers In Roofing sales And Climbing...
To: <christian.gabriel@ift-informatik.de>
Message-ID: <k7z4il6zdup3vjju-ncqlblklirp69gdl-7cfb-12c8@rofongbsuns.bid>

--c46d54b90245868ae30bd5c72a22e78d_7cfb_12c8
Content-Type: text/plain;
Content-Transfer-Encoding: 8bit

*Over 22 Million Dollers In Roofing sales And Climbing...

http://rofongbsuns.bid/uHUwyQch8PO9fl6Gz5O1T3YR8yOFiiGQaTBMMvTE42dmVvQ_31790_31995

http://rofongbsuns.bid/_Jz_IrRmVjjtwdRzQ4RsSkYmJ3VcwvSVdXPx-vZYgt9r-y4_31790_31995

The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate.e actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps.he related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations

--c46d54b90245868ae30bd5c72a22e78d_7cfb_12c8
Content-Type: text/html;
Content-Transfer-Encoding: 8bit

<html>
<head>
	<title>Roofing Business</title>
</head>
<body><a href="http://rofongbsuns.bid/W2EXaaCPoLD5vJxsMMWImAEoIqnb6wBh8E9LjDSddubnVdc_31790_31995"><img src="http://rofongbsuns.bid/996527094af2290021.jpg" /></a><img height="1" src="http://www.rofongbsuns.bid/TI2dYmbJCiTPApQvlo3lePaTl6Z0onAUlCHCCLiP3hFmdng_31790_31995" width="1" /><br />
<br />
&nbsp;
<center>
<div style="width:600px;text-align:left;border:5px solid black;padding:6px;">
<div style="font-family:candara;font-size:20px;">&nbsp;
<table>
	<tbody>
		<tr>
			<td style="font-family:candara; font-size:20px; text-align:left;padding:4px;">
			<div style="text-align: center;"><span style="font-size:24px;"><strong>&quot;<span style="color:#FF0000;">Small Town Roofing Contractor Goes From Near Bankrupty To</span> <span style="color: red;"><span style="background-color: rgb(255, 215, 0);"><a href="http://rofongbsuns.bid/uHUwyQch8PO9fl6Gz5O1T3YR8yOFiiGQaTBMMvTE42dmVvQ_31790_31995">Pocketing Over $24,000,000(Yes Million) in the Roofing Bussiness</a>&nbsp; </span></span><span style="color:#FF0000;">while easily Dominating Local Search Engine and Video Networks like a PRO...</span>&quot;</strong></span></div>
			&nbsp;

			<center><a href="http://rofongbsuns.bid/uHUwyQch8PO9fl6Gz5O1T3YR8yOFiiGQaTBMMvTE42dmVvQ_31790_31995"><img alt=" " src="http://rofongbsuns.bid/8b0ef87a70036fbc1b.gif" /></a></center>
			&nbsp;<br />
			The <a href="http://rofongbsuns.bid/uHUwyQch8PO9fl6Gz5O1T3YR8yOFiiGQaTBMMvTE42dmVvQ_31790_31995"><strong>&quot;Insider&nbsp; Secret&quot;</strong></a> To making Millions in the <strong>Roofing Business&nbsp;&nbsp; </strong>Quickly and Easily!<br />
			<br />
			&nbsp;
			<center>
			<div style="background-color:rgb(255, 215, 0);width:400px;border-radius:12px;padding:12px;"><span style="font-size:18px;"><span style="font-family: comic\ sans\ ms, cursive;"><strong><a href="http://rofongbsuns.bid/uHUwyQch8PO9fl6Gz5O1T3YR8yOFiiGQaTBMMvTE42dmVvQ_31790_31995" style="color:red;">Do You Need A Video For Your Business?</a></strong></span></span></div>
			</center>
			&nbsp;</td>
		</tr>
	</tbody>
</table>
</div>
</div>
&nbsp;<br />
&nbsp;<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
&nbsp;
<center><a href="http://rofongbsuns.bid/KeXAVYEy9VFXwageWZiParjwaPw9S7ahczadOglAWC-xF6g_31790_31995"><img alt=" " src="http://rofongbsuns.bid/b435f61e9e10fe9734.jpg" /></a></center>
<br />
<br />
<br />
<br />
<br />
<span style="font-size:6px;color:#ffffff;">The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics &ndash; or, when referring to actual methods, artificial intelligence and machine learning &ndash; are more appropriate.e actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps.he related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations</span><br />
<br />
&nbsp;</center>
<br />
<br />
&nbsp;</body>
</html>

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bypass 1.0, Devloped By El Moujahidin (the source has been moved and devloped)
Email: contact@elmoujehidin.net bypass 1.0, Devloped By El Moujahidin (the source has been moved and devloped) Email: contact@elmoujehidin.net