Introduction Header Body content Todo List. Analysis. Joe Huang Anti-Spam Team Cellopoint. February 21, Joe Huang Analysis 1 / 62
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1 Analysis Joe Huang Anti-Spam Team Cellopoint February 21, 2014 Joe Huang Analysis 1 / 62
2 Joe Huang Analysis 2 / 62
3 For spam filtering, we would like to study the content of an to find discriminative features from abnormal behavior malicious behavior The source of samples is drawn from long-term analysis (LA) data considered as the ground truth Joe Huang Analysis 3 / 62
4 Data Statistics Sabrina Chang manually classified s into ham and spam Table : Long-term analysis (LA) data statistics Type # s Avg. size (GB) ham spam Joe Huang Analysis 4 / 62
5 eml parser.py eml parser.py is a python program for content extraction Written by Georges Toth It can serve as an auxiliary tool Joe Huang Analysis 5 / 62
6 From Bad references Multiple Recipients Character Transition in Subject EmptyDomain Analysis Joe Huang Analysis 6 / 62
7 Lengthy From From Bad references Multiple Recipients Character Transition in Subject EmptyDomain From fields are usually generated by clients from user settings Names or easily recognized titles Lengthy names are abnormal (more than seven words) Figure : Lengthy From in spam Figure : Lengthy From in ham Joe Huang Analysis 7 / 62
8 Statisitics From Bad references Multiple Recipients Character Transition in Subject EmptyDomain The s with more than seven words in From fields Table : Lengthy From Type # s/total Percentage ham 7/ % spam 230/ % Joe Huang Analysis 8 / 62
9 Feature From Bad references Multiple Recipients Character Transition in Subject EmptyDomain Number of words in the author name Words are separated with whitespaces Figure : Different words are in various colors Joe Huang Analysis 9 / 62
10 Multiple From From Bad references Multiple Recipients Character Transition in Subject EmptyDomain Multiple senders in From field can be spam suspects Figure : Multiple senders in spam Joe Huang Analysis 10 / 62
11 Statisitics From Bad references Multiple Recipients Character Transition in Subject EmptyDomain The number of s containing multiple senders in the From field Table : Multiple From Type # s/total Percentage ham 1/ % spam 69/ % Joe Huang Analysis 11 / 62
12 Feature From Bad references Multiple Recipients Character Transition in Subject EmptyDomain Number of senders in the From field Figure : Different senders are in various colors Joe Huang Analysis 12 / 62
13 Bad References From Bad references Multiple Recipients Character Transition in Subject EmptyDomain References fields are used when the message is a part of threads RE: in subjects In the case above, References should appear. No distinction between hams and spams Joe Huang Analysis 13 / 62
14 Statisitics From Bad references Multiple Recipients Character Transition in Subject EmptyDomain Table : Mails with bad references in the whole sample Type # s/total Percentage ham 1797/ % spam 1068/ % Joe Huang Analysis 14 / 62
15 Feature From Bad references Multiple Recipients Character Transition in Subject EmptyDomain Presence of RE: or Re: in subjects Presence of Reference fields in the header Joe Huang Analysis 15 / 62
16 Sample From Bad references Multiple Recipients Character Transition in Subject EmptyDomain Figure : An has RE: in the subject but no References fields Figure : An doesn t have RE: in the subject but References fields exist Joe Huang Analysis 16 / 62
17 Multiple Recipients From Bad references Multiple Recipients Character Transition in Subject EmptyDomain Some mails contain multiple recipients Spammers put recipients in a sorted manner with dictionary attack Figure : Sorted multiple recipients Joe Huang Analysis 17 / 62
18 From Bad references Multiple Recipients Character Transition in Subject EmptyDomain Multiple Recipients (cont d) We take the sorted multiple recipients into consideration In hams, NYUST and NCKU contribute to most of the samples Joe Huang Analysis 18 / 62
19 Statisitics From Bad references Multiple Recipients Character Transition in Subject EmptyDomain Table : Multiple Recipients ( s containing multiple recipients w.r.t. the whole hams/spams) Type # s/total Percentage ham 2085/ % spam 2646/ % Table : Multiple Recipients ( s containing sorted multiple recipients w.r.t all the s containing multiple recipients (Percentage (1)) and all the s (Percentage (2)) Type # s/total Percentage (1) Percentage(2) ham 145/ % 1.25% spam 1136/ % 6.49% Joe Huang Analysis 19 / 62
20 Feature From Bad references Multiple Recipients Character Transition in Subject EmptyDomain Number of recipients in the To field Indicator of ordering Joe Huang Analysis 20 / 62
21 Sample From Bad references Multiple Recipients Character Transition in Subject EmptyDomain Figure : Ordered multiple recipients Figure : Non-ordered multiple recipients Joe Huang Analysis 21 / 62
22 From Bad references Multiple Recipients Character Transition in Subject EmptyDomain Character Transition in Subject Spams tend to use text in various charsets This might leads to larger amount of transitions Figure : Multiple charset transition Joe Huang Analysis 22 / 62
23 Statistics From Bad references Multiple Recipients Character Transition in Subject EmptyDomain Count the most frequent five charsets and transition times Table : Top 5 charsets and transition times in ham Charset Frequency Transition Frequency BasicLatin CJKUnifiedIdeo Latin-1Supplement HalfFullwidthForm Hiragana Table : Top 5 charsets and transition times in spam Charset Frequency Transition Frequency BasicLatin CJKUnifiedIdeo Latin-1Supplement HangulSylb Cyrillic Joe Huang Analysis 23 / 62
24 Feature From Bad references Multiple Recipients Character Transition in Subject EmptyDomain Break the subject into tokens w.r.t. different charsets Average length of tokens, number of tokens, std. of token lengths Figure : A subject consisting of tokens of multiple charsets Joe Huang Analysis 24 / 62
25 EmptyDomain From Bad references Multiple Recipients Character Transition in Subject EmptyDomain Some spams might contain Resulted from bounce attack Figure : A header with EmptySender@EmptyDomain Joe Huang Analysis 25 / 62
26 Statistics From Bad references Multiple Recipients Character Transition in Subject EmptyDomain Search for in the Receive field Table : EmptySender@EmptyDomain Type # mails/total Percentage ham 304/ % spam 1770/ % Joe Huang Analysis 26 / 62
27 Feature From Bad references Multiple Recipients Character Transition in Subject EmptyDomain Presence of Joe Huang Analysis 27 / 62
28 Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment Font size, font family and color Some s tend to apply many font type, size and colors EDMs may also have contents in complicated font settings Joe Huang Analysis 28 / 62
29 Statistics Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment Occurrences of font-size, font-family and color in the body Table : font-size occurrence Type Avg. # Occurence ham spam Table : Average font-family occurrence Type Avg. # Occurence ham spam Table : Average color occurrence Type Avg. # Occurence Avg. # unique occurence ham spam Joe Huang Analysis 29 / 62
30 Feature Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment Count the occurrences of font sizes, families and colors Unique and non-unique cases Joe Huang Analysis 30 / 62
31 Inconsistent Hyperlink Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment Valid or benign hyperlinks usually appear with related label texts Some labels may be also hyperlinks but different from those in href These abnormal hyperlinks tend to mislead users Figure : URL text with inconsistent hyperlink Joe Huang Analysis 31 / 62
32 Statistics Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment Consider s with inconsistent hyperlinks Table : Inconsistent Hyperlink (INCSTHLINK) Type # s/total Percentage Total # links Avg. links/mail ham 68/ % spam 1023/ % Joe Huang Analysis 32 / 62
33 Feature Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment Count the occurrences of inconsistent hyperlinks Joe Huang Analysis 33 / 62
34 Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment Random strings in Received Host names usually appear after From in Received fields Some might be random strings Figure : Random string Figure : Invalid host name Joe Huang Analysis 34 / 62
35 Statistics Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment Strings with consonant/vowel ratio less than 2 are considered random Consider random host name and invalid host name separately Table : Random host name (RNDHNAME) Type # s/total Percentage Total # RNDHNAME Avg. #/mail ham 94/ % 94 1 spam 131/ % Table : Invalid host name (INVLDHNAME) Type # s/total Percentage Total # INVLDHNAME Avg. #/mail ham 631/ % spam 423/ % Joe Huang Analysis 35 / 62
36 Feature Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment Presence of random strings in Received Joe Huang Analysis 36 / 62
37 Random strings in From Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment The From field usually contains the address of the message composser The user name in the address sometimes appears to be abnormal in spams Figure : Random string in an user name of an address Joe Huang Analysis 37 / 62
38 Statistics Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment Strings with consonant/vowel ratio less than 2.5 are considered random Table : Random user name in from (RNDUNAME) Type # s/total Percentage ham 574/ % spam 1777/ % Joe Huang Analysis 38 / 62
39 Statistics (cont d) Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment Frequencies of alphabets is also counted Table : Alphabet frequencies in the user name of From field in hams (total: 17359) Alphabet Frequency Percentage Alphabet Frequency Percentage a % n % b % o % c % p % d % q % e % r % f % s % g % t % h % u % i % v % j % w % k % x % l % y % m % z % Joe Huang Analysis 39 / 62
40 Statistics (cont d) Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment Frequencies of alphabets is also counted Table : Alphabet frequencies in the user name of From field in spams (total: 22457) Alphabet Frequency Percentage Alphabet Frequency Percentage a % n % b % o % c % p % d % q % e % r % f % s % g % t % h % u % i % v % j % w % k % x % l % y % m % z % Joe Huang Analysis 40 / 62
41 Statistics (cont d) Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment Figure : Percentage of alphabet occurrences in user names of From Joe Huang Analysis 41 / 62
42 Feature Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment Presence of random strings in From Joe Huang Analysis 42 / 62
43 Body Existence Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment Some spams might contain empty body Empty parts should at least have information of themselves Joe Huang Analysis 43 / 62
44 Sample Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment Figure : An with multipart message Figure : An with an empty body Joe Huang Analysis 44 / 62
45 Statistics Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment An with empty body from which only one part consisting of whitespaces can be extracted Table : Body existence Type # mails/total Percentage ham 6/ % spam 29/ % Joe Huang Analysis 45 / 62
46 Attachment Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment s with HTML format often has pictures in the attachment Some spams might arrive with viruses files appearing as compressed files Joe Huang Analysis 46 / 62
47 Statistics Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment Count the s containing attachments Table : Top 5 attachment extensions in ham Extension # Occurence jpg 3375 pdf 847 png 523 xls 520 gif 432 Table : Top 5 attachment extensions in spam Extension # Occurence zip 1598 jpg 1032 rar 371 jpeg 292 xlsx 185 Joe Huang Analysis 47 / 62
48 Statistics (cont d) Font size, family and color Inconsistent Hyperlink Random strings Body Existence Attachment Table : Statistics of ham # mail/total Percentage Max. # attachment Avg. # attachment 5309/ % Table : Statistics of spam # mail/total Percentage Max. # attachment Avg. # attachment 4670/ % Joe Huang Analysis 48 / 62
49 Bad X-Mailer Bad X-Mailer URL Address in From Domains in Received Some mail clients write in X-Mailer to promote their brands or to gain popularity This field may contain meaningless names or symbols in spams Figure : X-Mailer field in spam Figure : X-Mailer field in spam Figure : X-Mailer field in ham Joe Huang Analysis 49 / 62
50 Statisitics Bad X-Mailer URL Address in From Domains in Received We consider X-Mailer fields containing mail, microsoft, aol, bat and zimbra as good ones Table : X-Mailer Type # s/total Percentage ham 268/ % spam 1603/ % Joe Huang Analysis 50 / 62
51 URL Bad X-Mailer URL Address in From Domains in Received Spammers hide URLs in messages We study the sources of URLs in bodies Table : Total number of URLs in s Type # URLs ham spam Joe Huang Analysis 51 / 62
52 Bad X-Mailer URL Address in From Domains in Received Top 45 Hostnames for Spam - Part 1 Hostname Frequency clicks.att.com 1581 www208.americanexpress.com www2.bt.com 864 bt.custhelp.com Joe Huang Analysis 52 / 62
53 Bad X-Mailer URL Address in From Domains in Received Top 45 Hostnames for Spam - Part 2 Hostname Frequency shopping.windmill.com.tw 605 schemas.microsoft.com 604 ecom.wireless.att.com 488 hhvim.com 487 twedm203.com medictixk.com Joe Huang Analysis 53 / 62
54 Bad X-Mailer URL Address in From Domains in Received Top 45 Hostnames for Spam - Part 3 Hostname Frequency webs.com 364 bit.ly 289 webtwwebtw.phpforms.net contact.bellsouth.com 191 tw.mall.yahoo.com 185 x.co webs.com 172 herovajob.0catch.com 166 Joe Huang Analysis 54 / 62
55 Bad X-Mailer URL Address in From Domains in Received Top 45 Hostnames for Spam - Part 3 Hostname Frequency redirect.simba.taobao.com 165 0rz.tw askey.com.tw 117 s1.mailhunter.com.tw 102 twitter.com 93 bizcommunity.att.com 93 att.com 93 Joe Huang Analysis 55 / 62
56 Bad X-Mailer URL Address in From Domains in Received Top 45 Hostnames for Spam - Part 4 Hostname Frequency url.cn 86 ecom.wirelessatt.com 85 dwz.cn 80 whatsappcdn.appspot.com microsoft.com 74 schemas.openxmlformats.org 72 e.delta.com 72 goo.gl 65 Joe Huang Analysis 56 / 62
57 Bad X-Mailer URL Address in From Domains in Received Top 12 Hostnames for Ham - Part 1 Hostname Frequency schemas.microsoft.com microsoft.com schemas.openxmlformats.org 236 schema.org Joe Huang Analysis 57 / 62
58 Bad X-Mailer URL Address in From Domains in Received Top 12 Hostnames for Ham - Part 2 Hostname Frequency money.chinatimes.com Joe Huang Analysis 58 / 62
59 Address in From Bad X-Mailer URL Address in From Domains in Received Spammers tend to use anomaly addresses in From field Figure : An illegal address in from Joe Huang Analysis 59 / 62
60 Statistics Bad X-Mailer URL Address in From Domains in Received Top-level domains (TLD)s in the From field Table : Top 10 TLDs in ham TLDs in ham Frequency com 2376 tw 1579 vn 405 net 389 uk 243 cn 207 it 72 org 70 jp 54 eu 37 Table : Top 10 TLDs in spam TLDs in spam Frequency com 5344 tw 1738 ru 975 net 869 edu 629 org 279 ltd 245 uk 196 it 115 us 92 Joe Huang Analysis 60 / 62
61 Domains in Received Bad X-Mailer URL Address in From Domains in Received Received fields keep track of relay information Some invalid host names are contained in received fields Joe Huang Analysis 61 / 62
62 Statistics Bad X-Mailer URL Address in From Domains in Received Top-level domains (TLD)s in the From field Table : Top 10 TLDs in received of ham TLDs Frequency com tw 7142 net 2780 vn 1613 uk 493 cn 462 it 337 localdomain 328 local 278 group 224 Table : Top 10 TLDs in received of spam TLDs Frequency com tw net 2814 ru 1529 org 752 cn 712 edu 641 it 349 su 324 va 291 corp 171 Joe Huang Analysis 62 / 62
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