{"id":2123,"date":"2013-02-20T07:00:00","date_gmt":"2013-02-20T12:00:00","guid":{"rendered":"http:\/\/rud.is\/b\/?p=2123"},"modified":"2013-02-20T07:00:00","modified_gmt":"2013-02-20T12:00:00","slug":"rnetintel-cross-check-apt-1s-ip-list-with-alienvault-reputation-db-some-graphsanalysis","status":"publish","type":"post","link":"https:\/\/rud.is\/b\/2013\/02\/20\/rnetintel-cross-check-apt-1s-ip-list-with-alienvault-reputation-db-some-graphsanalysis\/","title":{"rendered":"R\/netintel : Cross-check APT-1’s IP list with AlienVault Reputation DB (+ some graphs\/analysis)"},"content":{"rendered":"

Here’s a quick example of couple additional ways to use the netintel<\/a> R package I’ve been tinkering with. This could easily be done on the command line with other tools, but if you’re already doing scripting\/analysis with R, this provides a quick way to tell if a list of IPs is in the @AlienVault IP reputation database. Zero revelations here for regular R users, but it might help some folks who are working to make R more of a first class scripting citizen.<\/p>\n

I whipped up the following bit of code to check to see how many IP addresses in the @Mandiant APT-1 FQDN dump were already in the AlienVault database. Reverse resolution of the Mandiant APT-1 FQDN list is a bit dubious at this point so a cross-check with known current data is a good idea. I should also point out that not all the addresses resolved “well” (there are 2046 FQDNs and my quick dig<\/code> only yielded 218 usable IPs).<\/p>\n

library(netintel)\r\n\r\n# get the @AlienVault reputation DB\r\nav.rep = Alien.Vault.Reputation()\r\n\r\n# read in resolved APT-1 FQDNs list\r\napt.1 = read.csv(\"apt-1-ips.csv\")\r\n\r\n# basic set operation\r\nwhats.left = intersect(apt.1$ip,av.rep$IP)\r\n\r\n# how many were in the quickly resolved apt-1 ip list?\r\nlength(apt.1)\r\n[1]218\r\n\r\n# how many are common across the lists?\r\nlength(whats.left)\r\n[1] 44\r\n\r\n# take a quick look at them\r\nwhats.left\r\n[1] \"12.152.124.11\"   \"140.112.19.195\"  \"161.58.182.205\"  \"165.165.38.19\"   \"173.254.28.80\"  \r\n[6] \"184.168.221.45\"  \"184.168.221.54\"  \"184.168.221.56\"  \"184.168.221.58\"  \"184.168.221.68\" \r\n[11] \"192.31.186.141\"  \"192.31.186.149\"  \"194.106.162.203\" \"199.59.166.109\"  \"203.170.198.56\" \r\n[16] \"204.100.63.18\"   \"204.93.130.138\"  \"205.178.189.129\" \"207.173.155.44\"  \"207.225.36.69\"  \r\n[21] \"208.185.233.163\" \"208.69.32.230\"   \"208.73.210.87\"   \"213.63.187.70\"   \"216.55.83.12\"   \r\n[26] \"50.63.202.62\"    \"63.134.215.218\"  \"63.246.147.10\"   \"64.12.75.1\"      \"64.12.79.57\"    \r\n[31] \"64.126.12.3\"     \"64.14.81.30\"     \"64.221.131.174\"  \"66.228.132.20\"   \"66.228.132.53\"  \r\n[36] \"68.165.211.181\"  \"69.43.160.186\"   \"69.43.161.167\"   \"69.43.161.178\"   \"70.90.53.170\"   \r\n[41] \"74.14.204.147\"   \"74.220.199.6\"    \"74.93.92.50\"     \"8.5.1.34\"<\/pre>\n
So, roughly a 20% overlap between (quickly-I’m sure there’s a more comprehensive list) resolved & “clean” APT-1 FQDNs IPs and the AlienVault reputation database.<\/p>\n
For kicks, we can see where all the resolved APT-1 nodes live (BGP\/network-wise) in relation to each other using some of the other library functions:<\/p>\n
library(netintel)\r\nlibrary(igraph)\r\nlibrary(plyr)\r\n\r\napt.1 = read.csv(\"apt-1-ips.csv\")\r\nips = apt.1$ip\r\n\r\n# get BGP origin & peers\r\norigin = BulkOrigin(ips)\r\npeers = BulkPeer(ips)\r\n\r\n# start graphing\r\ng = graph.empty()\r\n\r\n# Make IP vertices; IP endpoints are red\r\ng = g + vertices(ips,size=1,color=\"red\",group=1)\r\n\r\n# Make BGP vertices ; BGP nodes are light blue\r\ng = g + vertices(unique(c(peers$Peer.AS, origin$AS)),size=1.5,color=\"orange\",group=2)\r\n\r\n# no labels\r\nV(g)$label = \"\"\r\n\r\n# Make IP\/BGP edges\r\nip.edges = lapply(ips,function(x) {\r\n  iAS = origin[origin$IP==x,]$AS\r\n  lapply(iAS,function(y){\r\n    c(x,y)\r\n  })\r\n})\r\n\r\n# Make BGP\/peer edges\r\nbgp.edges = lapply(unique(origin$BGP.Prefix),function(x) {\r\n  startAS = unique(origin[origin$BGP.Prefix==x,]$AS)\r\n  lapply(startAS,function(z) {\r\n    pAS = peers[peers$BGP.Prefix==x,]$Peer.AS\r\n    lapply(pAS,function(y) {\r\n      c(z,y)\r\n    })\r\n  })\r\n})\r\n\r\n# get total graph node count\r\nnode.count = table(c(unlist(ip.edges),unlist(bgp.edges)))\r\n\r\n# add edges \r\ng = g + edges(unlist(ip.edges))\r\ng = g + edges(unlist(bgp.edges))\r\n\r\n# base edge weight == 1\r\nE(g)$weight = 1\r\n\r\n# simplify the graph\r\ng = simplify(g, edge.attr.comb=list(weight=\"sum\"))\r\n\r\n# no arrows\r\nE(g)$arrow.size = 0\r\n\r\n# best layout for this\r\nL = layout.fruchterman.reingold(g)\r\n\r\n# plot the graph\r\nplot(g,margin=0)<\/pre>\n
\"apt-1\"<\/center><\/p>\n
If we take out the BGP peer relationships from the graph (i.e. don’t add the bgp.edges<\/code> in the above code) we can see the mal-host clusters even more clearly (the pseudo “Death Star” look is unintentional but appropro):<\/p>\n
\"Rplot01\"<\/center><\/p>\n
We can also determine which ASNs the bigger clusters belong to by checking out the degree. The “top” 5 clusters are:<\/p>\n
\r\n16509 40676 36351 26496 15169 \r\n    7     8     8    13    54 <\/pre>\n
While my library doesn’t support direct ASN detail lookup yet (an oversight), we can take those ASN’s, check them out manually and see the results:<\/p>\n
16509   | US | arin     | 2000-05-04 | AMAZON-02 - Amazon.com, Inc.\r\n40676   | US | arin     | 2008-02-26 | PSYCHZ - Psychz Networks\r\n36351   | US | arin     | 2005-12-12 | SOFTLAYER - SoftLayer Technologies Inc.\r\n26496   | US | arin     | 2002-10-01 | AS-26496-GO-DADDY-COM-LLC - GoDaddy.com, LLC\r\n15169   | US | arin     | 2000-03-30 | GOOGLE - Google Inc.<\/pre>\n
So Google servers are hosting the most mal-nodes from the resolved ASN-1 list, followed by GoDaddy. I actually expected Amazon to be higher up in the list.<\/p>\n
I’ll be adding igraph<\/code> and ASN lookup functions to the netintel<\/code> library soon. Also, if anyone has a better APT-1 IP list, please shoot me a link.<\/p>\n","protected":false},"excerpt":{"rendered":"
Here’s a quick example of couple additional ways to use the netintel R package I’ve been tinkering with. This could easily be done on the command line with other tools, but if you’re already doing scripting\/analysis with R, this provides a quick way to tell if a list of IPs is in the @AlienVault IP […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"activitypub_content_warning":"","activitypub_content_visibility":"","activitypub_max_image_attachments":3,"activitypub_interaction_policy_quote":"anyone","activitypub_status":"","footnotes":"","jetpack_post_was_ever_published":false},"categories":[24,677,678,673,674,3],"tags":[685,682,683,684],"class_list":["post-2123","post","type-post","status-publish","format-standard","hentry","category-charts-graphs","category-data-analysis-2","category-data-visualization","category-datavis-2","category-dataviz","category-information-security","tag-alienvault","tag-apt-1","tag-mandiant","tag-netintel"],"yoast_head":"\nR\/netintel : Cross-check APT-1's IP list with AlienVault Reputation DB (+ some graphs\/analysis) - rud.is<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/rud.is\/b\/2013\/02\/20\/rnetintel-cross-check-apt-1s-ip-list-with-alienvault-reputation-db-some-graphsanalysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"R\/netintel : Cross-check APT-1's IP list with AlienVault Reputation DB (+ some graphs\/analysis) - rud.is\" \/>\n<meta property=\"og:description\" content=\"Here’s a quick example of couple additional ways to use the netintel R package I’ve been tinkering with. 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