real time analysis and visualization

1

Real time analysis and visualizationANUBISNETWORKS LABSPTCORESEC

2Agenda

Who are we?

AnubisNetworks Stream

Stream Information Processing

Adding Valuable Information to Stream Events

3Who are we?

Tiago Martins AnubisNetworks @Gank_101

João Gouveia AnubisNetworks @jgouv

Tiago Henriques Centralway @Balgan

4Anubis StreamForceEvents (lots and lots of events) Events are “volatile” by nature They exist only if someone is listening

Remember?:

“If a tree falls in a forest and no one is around to hear it, does it make a sound?”

5Anubis StreamForce Enter security Big Data

“a brave new world”

Volume

Variety Velocity

We are here

6Anubis StreamForceProblems (and ambitions) to tackle

The huge amount and variety of data to processMechanisms to share data across multiple systems,

organizations, teams, companies..Common API for dealing with all this (both from a

producer and a consumer perspective)

7Anubis StreamForceEnter the security events CEP - StreamForce

High performance, scalable, Complex Event Processor (CEP) – 1 node (commodity hw) = 50k evt/second

Uses streaming technologyFollows a publish / subscriber model

8Anubis StreamForceData format

Events are published in JSON formatEvents are consumed in JSON format

9Anubis StreamForce Yes, we love JSON

10Anubis StreamForce

Sharing Models

MFE

OpenSource / MailSpike community

DashboardDashboard

Complex Event Processing

SinkholesData-theft Trojans

Real Time Feeds

Real Time Feeds

IP Reputation

Passive DNSTraps /Honeypots

Twitter

MFE

OpenSource / MailSpike community

DashboardDashboard

Complex Event Processing

SinkholesData-theft Trojans

Real Time Feeds

Real Time Feeds

IP Reputation

Passive DNSTraps /Honeypots

Twitter

13Anubis CyberFeed

Feed galore!Sinkhole data, traps, IP reputation, etc.

Bespoke feeds (create your own view)Measure, group, correlate, de-

duplicate .. High volume (usually ~6,000 events per

second, more data being added frequently

MFE

OpenSource / MailSpike community

DashboardEvent navigation

Complex Event Processing

SinkholesData-theft Trojans

Real Time Feeds

Real Time Feeds

IP Reputation

Passive DNSTraps /Honeypots

Twitter

15Anubis CyberFeed

Apps (demo time)

Stream Information Processing

Collecting events from the Stream.

Generating reports.

Real time visualization.

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Challenge ~6k events/s and at peak over 10k events/s.

Let’s focus on trojans feed (banktrojan).

Peaks @ ~4k events/s

{"_origin":"banktrojan","env":{"server_name":"anam0rph.su","remote_addr":"46.247.141.66","path_info":"\/in.php","request_method":"POST","http_user_agent":"Mozilla\/4.0"},"data":"upqchCg4slzHEexq0JyNLlaDqX40GsCoA3Out1Ah3HaVsQj45YCqGKylXf2Pv81M9JX0","seen":1379956636,"trojanfamily":"Zeus","_provider":"lab","hostn":"lab14","_ts":1379956641}

17

18Challenge

19Challenge

Challenge Let’s use the Stream to help

Group by machine and trojan From peak ~4k/s to peak ~1k/s

Filter fields.

Geo location

We end up with{"env":{"remote_addr":"207.215.48.83"},"trojanfamily":"W32Expiro","_geo_env_remote_addr":{"country_code":"US","country_name":"United States","city":"Los Angeles","latitude":34.0067,"longitude":-118.3455,"asn":7132,"asn_name":"AS for SBIS-AS"}}

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Challenge How to process and store these events?

21

22Technologies Applications

NodeJS Server-side Javascript Platform. V8 Javascript Engine. http://nodejs.org/

Why? Great for prototyping. Fast and scalable. Modules for (almost) everything.

23Technologies Databases

MongoDB NoSQL Database. Stores JSON-style documents. GridFS http://www.mongodb.org/

Why? JSON from the Stream,

JSON in the database. Fast and scalable.

Redis Key-value storage. In-memory dataset. http://redis.io/

Why? Faster than MongoDB for

certain operations, like keeping track of number of infected machines.

Very fast and scalable.

Data Collection 24

Storage Aggregate information

MongoDB Redis

Worker

Worker

Worker

Processor

Process real time events

Applications Collector Worker Processor

Databases MongoDB Redis

Collector

Stream

Data Collection 25

Storage Aggregate information

MongoDB Redis

Worker

Worker

Worker

Processor

Process real time events

Events comes from the Stream. Collector distributes events to Workers. Workers persist event information. Processor aggregates information and stores it for statistical and

historical analysis.

Collector

Stream

Data Collection 26

Storage Aggregate information

MongoDB Redis

Worker

Worker

Worker

Processor

Process real time events

MongoDB Real time information of infected machines. Historical aggregated information.

Redis Real time counters of infected machines.

Collector

Stream

Data Collection - Collector 27

Collector

Old data is periodically remove, i.e. machines that don’t produce events for more than 24 hours.

Send events to Workers.Workers

Decrements counters of removed information. Send warnings

Country / ASN is no longer infected. Botnet X decreased Y % of its size.

Data Collection - Worker 28

Worker

Create new entries for unseen machines. Adds information about new trojans / domains. Update the last time the machine was seen.

Process events and update the Redis counters accordingly. Needs to check MongoDB to determine if:

New entry – All counters incremented Existing entry – Increment only the counters related to

that Trojan Send warnings

Botnet X increased Y % in its size. New infections seen on Country / ASN.

Data Collection - Processor

Processor

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Processor retrieves real time counters from Redis. Information is processed by:

Botnet; ASN; Country; Botnet/Country; Botnet/ASN/Country; Total.

Persisting information to MongoDB creates a historic database of counters that can be queried and analyzed.

Data Collection - MongoDB Collection for active machines in the last 24h

{"city" : "Philippine",

"country" : "PH", "region" : "N/A",

"geo" : { "lat" : 16.4499, "lng" : 120.5499 },

"created" : ISODate("2013-09-21T00:19:12.227Z "), "domains" : [

{ "domain" : "hzmksreiuojy.nl", "trojan" : "zeus", "last" : ISODate("2013-09-21T09:42:56.799Z"), "created" : ISODate("2013-09-21T00:19:12.227Z") }

], "host" : "112.202.37.72.pldt.net", "ip" : "112.202.37.72", "ip_numeric" : 1892296008, "asn" : "Philippine Long Distance Telephone Company", "asn_code" : 9299, "last" : ISODate("2013-09-21T09:42:56.799Z"), "trojan" : [ "zeus” ]}

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Data Collection - MongoDB Collection for aggregated information (the historic counters database){ "_id" : ObjectId("519c0abac1172e813c004ac3"), "0" : 744, "1" : 745, "3" : 748, "4" : 748, "5" : 746, "6" : 745,

... "10" : 745, "11" : 742, "12" : 746, "13" : 750, "14" : 753,

..."metadata" : { "country" : "CH", "date" : "2013-05-22T00:00:00+0000", "trojan" : "conficker_b", "type" : "daily" }}

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Preallocated entries for each hour when the document is created.

If we don’t, MongoDB will keep extending the documents by adding thousands of entries every hour and it becomes very slow.

Data Collection - MongoDB Collection for 24 hours

4 MongoDB Shard instances >3 Million infected machines ~2 Gb of data ~558 bytes per document.

Indexes by ip – helps inserts and updates. ip_numeric – enables queries by CIDRs. last – Faster removes for expired

machines. host – Hmm, is there any .gov? country, family, asn – Speeds MongoDB

queries and also allows faster custom queries.

Collection for aggregated information Data for 119 days (25 May to 11 July)

> 18 Million entries ~6,5 Gb of data ~366 bytes per object ~56 Mb per day

Indexes by metadata.country metadata.trojan metadata.date Metadata.asn Metadata.type,

metadata.country,metadata.date,met....... (all)

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33Data Collection - Redis Counters by Trojan / Country "cutwailbt:RO": "1256", "rbot:LA": "3", "tdss:NP": "114", "unknown4adapt:IR": "100", "unknownaff:EE": "0", "cutwail:CM": "20", "unknownhrat3:NZ": "56", "cutwailbt:PR": "191", "shylock:NO": "1", "unknownpws:BO": "3", "unknowndgaxx:CY": "77", "fbhijack:GH": "22", "pushbot:IE": "2", "carufax:US": "424“

Counters by Trojan "unknownwindcrat": "18", "tdss": "79530", "unknownsu2": "2735", "unknowndga9": "15", "unknowndga3": "17", "ircbot": "19874", "jshijack": "35570", "adware": "294341", "zeus": "1032890", "jadtre": "40557", "w32almanahe": "13435", "festi": "1412", "qakbot": "19907", "cutwailbt": "38308“

Counters by Country “BY": "11158", "NA": "314", "BW": "326", "AS": "35", "AG": "94", "GG": "43", "ID": "142648", "MQ": "194", "IQ": "16142", "TH": "105429", "MY": "35410", "MA": "15278", "BG": "15086", "PL": "27384”

Data Collection - Redis Redis performance in our machine

SET: 473036.88 requests per second GET: 456412.59 requests per second INCR: 461787.12 requests per second

Time to get real time data Getting all the data from Familys/ASN/Counters to the NodeJS application and

ready to be processed in around half a second > 120 000 entries in… (very fast..)

Our current usage is ~ 3% CPU (of a 2.0 Ghz core) ~ 480 Mb of RAM

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Data Collection - API But! There is one more application.. How to easily retrieve stored data

MongoDB Rest API is a bit limited. NodeJS HTTP + MongoDB + Redis

Redis http://<host>/counters_countries ...

MongoDB http://<host>/family_country ...

Custom MongoDB Querys http://<host>/ips?f.ip_numeric=95.68.149.0/22 http://<host>/ips?f.country=PT http://<host>/ips?f.host=\bgov\b

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API

Data Collection - Limitations Grouping information by machine and trojan doesn’t allow to

study the real number of events per machine. Can be useful to get an idea of the botnet operations or how many

machines are behind a single IP (everyone is behind a router).

Slow MongoDB impacts everything Worker application needs to tolerate a slow MongoDB and discard

some information has a last resort. Beware of slow disks! Data persistence occurs every 60 seconds

(default) and can take too much time, having a real impact on performance..

>10s to persist is usually very bad, something is wrong with hard drives..

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Data Collection - Evolution Warnings

Which warnings to send? When? Thresholds?

Aggregate data by week, month, year.

Aggregate information in shorter intervals.

Data Mining algorithms applied to all the collected information.

Apply same principles to other feeds of the Stream. Spam Twitter Etc..

37

Reports What’s happening in country X? What about network 192.168.0.1/24? Can send me the report of Y everyday at 7 am? Ohh!! Remember the report I asked last week? Can I get a report for ASN AnubisNetwork?

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Reports 39

HTTP API Schedule Get Edit Delete List schedules List reports

Check MongoDB for work. Generate CSV report or store the JSON Document for

later querying. Send email with link to files when report is ready.

Server

Generator

Reports – MongoDB CSVs Scheduled Report{ "__v" : 0, "_id" : ObjectId("51d64e6d5e8fd0d145000008"), "active" : true, "asn_code" : "", "country" : "PT", "desc" : "Portugal Trojans", "emails" : "", "range" : "", "repeat" : true, "reports" : [ ObjectId("51d64e7037571bd24500000d"), ObjectId("51d741e8bcb161366600000c"), ObjectId("51d89367bcb161366600005f"), ObjectId("51d9e4f9bcb16136660000ca"), ObjectId("51db3678c3a15fc577000038"), ObjectId("51dc87e216eea97c20000007"), ObjectId("51ddd964a89164643b000001") ], "run_at" : ISODate("2013-07-11T22:00:00Z"), "scheduled_date" : ISODate("2013-07-05T04:41:17.067Z")}

Report{ "__v" : 0, "_id" : ObjectId("51d89367bcb161366600005f"), "date" : ISODate("2013-07-06T22:00:07.015Z"), "files" : [ ObjectId("51d89368bcb1613666000060") ], "work" : ObjectId("51d64e6d5e8fd0d145000008")}

Files Each report has an array of files that

represents the report. Each file is stored in GridFS.

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Reports – MongoDB JSONs Scheduled Report{ "__v" : 0, "_id" : ObjectId("51d64e6d5e8fd0d145000008"), "active" : true, "asn_code" : "", "country" : "PT", "desc" : "Portugal Trojans", "emails" : "", "range" : "", "repeat" : true, “snapshots" : [ ObjectId("521f761c0a45c3b00b000001"), ObjectId("521fb0848275044d420d392f"), ObjectId("52207c2f7c53a8494f010afa"), ObjectId("5221c9df4910ba3874000001"), ObjectId("522275724910ba3874001f66"), ObjectId("5223c6f24910ba3874003b7a"), ObjectId("522518734910ba3874005763")

], "run_at" : ISODate("2013-07-11T22:00:00Z"), "scheduled_date" : ISODate("2013-07-05T04:41:17.067Z")}

Snapshot{

"_id" : ObjectId("51d89367bcb161366600005f"), "date" : ISODate("2013-07-06T22:00:07.015Z"),

"work" : ObjectId("521f761c0a45c3b00b000001"), count: 123}

Results{ "machine" : { "trojan" : [ “conficker_b“ ], "ip" : "2.80.2.53", "host" : "Bl19-1-13.dsl.telepac.pt",

}, … , "metadata" : { "work" : ObjectId("521f837647b8d3ba7d000001"), "snaptshot" : ObjectId("521f837aa669d0b87d000001"), "date" : ISODate("2013-08-29T00:00:00Z") },}

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Reports – Evolution

Other reports formats.

Charts?

Other type of reports. (Not only botnets). Need to evolve Collector first.

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43Globe

How to visualize real time events from the stream? Where are the botnets located? Who’s the most infected? How many infections?

44Globe – Stream

origin = banktrojan Modules

Group trojanfamily _geo_env_remote_addr.count

ry_name grouptime=5000

Geo Filter fields

trojanfamily

Geolocation _geo_env_remote_addr.l*

KPI trojanfamily _geo_env_remote_addr.countr

y_name kpilimit = 10

Stream NodeJS Browser

Request botnets from stream

45Globe – NodeJSStream NodeJS Browse

r NodeJS

HTTP Get JSON from Stream.

Socket.IO Multiple protocol support (to bypass some proxys and

handle old browsers). Redis

Get real time number of infected machines.

46Globe – Browser Stream NodeJS Browse

r

Browser Socket.IO Client

Real time apps. Websockets and other

types of transport.

WebGL ThreeJS Tween

jQuery

WebWorkers Runs in the background. Where to place the red dots? Calculations from geolocation

to 3D point goes here.

47Globe – Evolution Some kind of HUD to get better interaction and

notifications.

Request actions by clicking in the globe. Generate report of infected in that area. Request operations in a specific that area.

Real time warnings New Infections Other types of warnings...

48Adding Valuable Information to Stream Events

How to distribute workload to other machines?

Adding value to the information we already have.

Minions

Typically the operations that would had value are expensive in terms of resources CPU Bandwidth

Master-slave approach that distributes work among distributed slaves we called Minions.

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Master

Minion

Minion

Minion

Minion

Minions 50

Master receives work from Requesters and store the work in MongoDB.

Minions request work. Requesters receive real time information on the work from the

Master or they can ask for work information at a later time.

Process / Storage Minions

Master MongoDB

DNS

Scan

Minion

Minion

Requesters

Minion

51Minions Master has an API that allows custom Requesters to

ask for work and monitor the work.

Minion have a modular architecture Easily create a custom module.

Information received from the Minions can then be processed by the Requesters and Sent to the Stream Saved on the database Update existing database

Minion

DNS

ScanningData Minin

g

52Extras...

So what else could we possibly do using the Stream? Distributed Portscanning Distributed DNS Resolutions Transmit images Transmit videos Realtime tools Data agnostic. Throw stuff at it and it will deal with it.

53Extras...

So what else could we possibly do using the Stream? Distributed Portscanning Distributed DNS Resolutions Transmit images Transmit videos Realtime tools Data agnostic. Throw stuff at it and it will deal with it.

FOCUS

FOCUS

54Portscanning Portscanning done right…

Its not only about your portscanner being able to throw 1 billion packets per second.

Location = reliability of scans. Distributed system for portscanning is much better. But its not

just about having it distributed. Its about optimizing what it scans.

55Portscanning

56Portscanning

57Portscanning

58Portscanning

IP Australia(intervolve)

China(ChinaVPShosting)

Russia(NQHost)

USA (Ramnode)

Portugal(Zon PT)

41.63.160.0/19 (Angola)

0 hosts up 0 hosts up 0 hosts up 0 hosts up 3 hosts up (sometimes)

5.1.96.0/21 (China)

10 hosts up 70 hosts up 40 hosts up 10 hosts up 40 hosts up

41.78.72.0/22 (Somalia)

0 hosts up 0 hosts up 0 hosts up 0 hosts up 33 hosts up

92.102.229.0/24(Russia)

20 hosts up 100 hosts up 2 hosts up 2 hosts up 150 hosts up

59Portscanning problems...

Doing portscanning correctly brings along certain problems. If you are not HD Moore or Dan Kaminsky, resource wise you are gonna have a bad time

60Portscanning problems...

Doing portscanning correctly brings along certain problems. If you are not HD Moore or Dan Kaminsky, resource wise you are gonna have a bad time

61Portscanning problems...

Doing portscanning correctly brings along certain problems. If you are not HD Moore or Dan Kaminsky, resource wise you are gonna have a bad

time You need lots of minions in different parts of the world Doesn’t actually require an amazing CPU or RAM if you do it correctly. Storing all that data... Querying that data...

Is it possible to have a cheap, distributed portscanning system?

62Portscanning problems...

Minion

63Portscanning

64Data….

65Data

66Internet status...

67Internet status...

68If we‘re doing it... Anyone else can.Evil side?

69Anubis StreamForce Have cool ideas? Contact us

Access for Brucon participants:API Endpoint:

http://brucon.cyberfeed.net:8080/stream?key=brucon2013

Web UI Dashboard maker:http://brucon.cyberfeed.net:8080/webgui

70Lol

Last minute testing

71Questions?