NETRESEC Network Security Blog

Wednesday, 04 November 2015 15:45:00 (UTC/GMT)

From 4SICS with ICS PCAP Files

I attended to the Swedish industrial cyber security conference 4SICS last month and brought back a bunch of PCAP files. Not just any PCAP files, but captured network traffic from the ICS lab that was set up in the Geek Lounge at 4SICS. These PCAP files are now made publicly available here, because captured network traffic from ICS/SCADA networks is a really scarce resource.

4SICS logo 4SICS is the the leading Industrial Control System (ICS) security conference in Europe, which brings in speakers and attendees from all around the world. I tought a one-day class on analyzing network traffic as part of the pre-conference training at 4SICS. In this class we analyzed PCAP files containing industrial protocols, such as Modbus/TCP and IEC-104. Unfortunately there aren't many capture files around that carry these protocols, so the ICS analysis part in my class wasn't as advanced as I wanted it to be.

I have been aware of this limited access to ICS traffic for some time now, which is why I decided to work with the 4SICS crew in order to set up a sniffer in the ICS lab at the 4SICS conference. This lab contained devices such as PLCs, RTUs, servers, industrial network equipment (switches, firewalls, etc), which were available for hands-on "testing" by 4SICS attendees.

4SICS ICS Lab. Image Credit: 4SICS

The network TAP vendor Garland were Technology Partners at 4SICS, so I didn't even have to bring a network TAP to the lab. I just connected my sniffer machine and let it record for three days. Chris Sistrunk also joined the sniffing party later in the conference by connecting his SEL-3355, which runs SecurityOnion, to the network TAP.

4SICS Network TAP and SniffersImage Credit: Patrick Nixdorf

The 350MB of network traffic that was captured during the 4SICS conference is now publicly available here:


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Posted by Erik Hjelmvik on Wednesday, 04 November 2015 15:45:00 (UTC/GMT)

Tuesday, 06 October 2015 09:05:00 (UTC/GMT)

Port Independent Protocol Detection

Protocol Alphabet Soup by ThousandEyes

Our heavy-duty PCAP analyzer CapLoader comes with a feature called ”Port Independent Protocol Identification”, a.k.a. PIPI (see Richard Bejtlich's PIPI blog post from 2006). Academic research in the Traffic Measurement field often use the term ”Traffic Classification”, which is similar but not the same thing. Traffic Classification normally group network traffic in broad classes, such as Email, Web, Chat or VoIP. CapLoader, on the other hand, identifies the actual application layer protocol used in each flow. So instead of classifying a flow as ”VoIP” CapLoader will tell you if the flow carries SIP, Skype, RTP or MGCP traffic. This approach is also known as “Dynamic Protocol Detection”.

Being able to identify application layer protocols without relying on the TCP or UDP port number is crucial when analyzing malicious traffic, such as malware Command-and-Control (C2) communication, covert backdoors and rouge servers, since such communication often use services on non-standard ports. Some common examples are:

  • Many botnet C2 protocols communicate over port TCP 443, but using a proprietary protocol rather than HTTP over SSL.
  • Backdoors on hacked computers and network devices typically wither run a standard service like SSH on a port other than 22 in order to hide.
  • More advanced backdoors use port knocking to run a proprietary C2 protocol on a standard port (SYNful knock runs on TCP 80).

This means that by analyzing network traffic for port-protocol anomalies, like an outgoing TCP connection to TCP 443 that isn't SSL, you can effectively detect intrusions without having IDS signatures for all C2 protocols. This analysis technique is often used when performing Rinse-Repeat Intrusion Detection, which is a blacklist-free approach for identifying intrusions and other form of malicious network traffic. With CapLoader one can simply apply a BPF filter like “port 443” and scroll through the displayed flows to make sure they are all say “SSL” in the Protocol column.

CapLoader detects non-SSL traffic to 1.web-counter.infoImage: Miuref/Boaxxe Trojan C2 traffic to "1.web-counter[.]info" on TCP 443 doesn't use SSL (or HTTPS)

Statistical Analysis

CapLoader relies on statistical analysis of each TCP, UDP and SCTP session's behavior in order to compare it to previously computed statistical models for known protocols. These statistical models are generated using a multitude of metrics, such as inter-packet delays, packet sizes and payload data. The port number is, on the other hand, a parameter that is intentionally not used by CapLoader to determine the application layer protocol.

The PIPI/Dynamic Protocol Detection feature in CapLoader has been designed to detect even encrypted and obfuscated binary protocols, such as Tor and Encrypted BitTorrent (MSE). These protocols are designed in order to deceive protocol detection mechanisms, and traditional signature based protocol detection algorithms can't reliably detect them. The statistical approach employed by CapLoader can, on the other hand, actually detect even these highly obfuscated protocols. It is, however, important to note that being a statistical method it will never be 100% accurate. Analysts should therefore not take for granted that a flow is using the protocol stated by CapLoader. There are some situations when it is very difficult to accurately classify an encrypted protocol, such as when the first part of a TCP session is missing in the analyzed data. This can occur when there is an ongoing session that was established before the packet capture was started.

Identified Protocols

The following protocols are currently available for detection in CapLoader's protocol database:

AOL Instant Messenger
BitTorrent Encrypted - MSE
DropBox LSP
eDonkey Obfuscated
Gh0st RAT
Groove LAN DPP
MikroTik NDP
Modbus TCP
MSN Messenger
NetBIOS Datagram Service
NetBIOS Name Service
NetBIOS Session Service
Poison Ivy RAT
Reverse Shell
SopCast P2P
Spotify P2P
Spotify Server
TeamViewer UDP
XMPP Jabber
Zeus TCP
Zeus UDP

The list of implemented protocols is constantly being increased with new protocols.

PIPI in NetworkMiner

NetworkMiner Logo

NetworkMiner Professional, which is the commercial version of NetworkMiner, also comes with an implementation of our protocol detection mechanism. Even though NetworkMiner Professional doesn't detect as many protocols as CapLoader, the PIPI feature built into NetworkMiner Pro still helps a lot when analyzing HTTP traffic on ports other that 80 or 8080 as well as in order to reassemble files downloaded from FTP or TFTP servers running on non-standard ports.


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Posted by Erik Hjelmvik on Tuesday, 06 October 2015 09:05:00 (UTC/GMT)

Monday, 28 September 2015 07:30:00 (UTC/GMT)

CapLoader 1.3 Released

CapLoader Logo

A new version of our heavy-duty PCAP parser tool CapLoader is now available. There are many new features and improvements in this release, such as the ability to filter flows with BPF, domain name extraction via passive DNS parser and matching of domain names against a local white list.

Filtering with BPF

The main focus in the work behind CapLoader 1.3 has been to fully support the Rinse-Repeat Intrusion Detection methodology. We've done this by improving the filtering capabilities in CapLoader. For starters, we've added an input filter, which can be used to specify IP addresses, IP networks, protocols or port numbers to be parsed or ignored. The input filter uses the Berkeley Packet Filter (BPF) syntax, and is designed to run really fast. So if you wanna analyze only HTTP traffic you can simply write “port 80” as your input filter to have CapLoader only parse and display flows going to or from port 80. We have also added a display filter, which unlike Wireshark also uses BPF. Thus, once a set of flows is loaded one can easily apply different display filters, like “host” or “net”, to apply different views on the parsed data.

CapLoader BPF Input Filter and Display Filter
Image: CapLoader with input filter "port 80 or port 443" and display filter "not net".

The main differences between the input filter and display filter are:

  • Input filter is much faster than the display filter, so if you know beforehand what ports, protocols or IP addresses you are interested in then make sure to apply them as an input filter. You will notice a delay when applying a display filter to a view of 10.000 flows or more.
  • In order to apply a new input filter CapLoader has to reload all the opened PCAP files (which is done by pressing F5). Modifying display filters, on the other hand, only requires you to press Enter or hit the “Apply” button.
  • Previously applied display filters are accessible in a drop-down menu in the GUI, but no history is kept of previous input filters.

NetFlow + DNS == true

The “Flows” view in CapLoader gives a great overview of all TCP, UDP and SCTP flows in the loaded PCAP files. However, it is usually not obvious to an analyst what every IP address is used for. We have therefore added a DNS parser to CapLoader, so that all DNS packets can be parsed in order to map IP addresses to domain names. The extracted domain names are displayed for each flow, which is very useful when performing Rinse-Repeat analysis in order to quickly remove “known good servers” from the analysis.

Leveraging the Alexa top 1M list

As we've show in in our previous blog post “DNS whitelisting in NetworkMiner”, using a list of popular domain names as a whitelist can be an effective method for finding malware. We often use this approach in order to quickly remove lots of known good servers when doing Rinse-Repeat analysis in large datasets.

Therefore, just as we did for NetworkMiner 1.5, CapLoader now includes Alexa's list of the 1 million most popular domain names on the Internet. All domain names, parsed from DNS traffic, are checked against the Alexa list. Domains listed in the whitelist are shown in CapLoader's “Server_Alexa_Domian” column. This makes it very easy to sort on this column in order to remove (hide) all flows going to “normal” servers on the Internet. After removing all those flows, what you're left with is pretty much just:

  • Local traffic (not sent over the Internet)
  • Outgoing traffic to either new or obscure domains

Manually going through the remaining flows can be very rewarding, as it can reveal C2 traffic from malware that has not yet been detected by traditional security products like anti-virus or IDS.

Flows in CapLoader with DNS parsing and Alexa lookup
Image: CapLoader with malicious flow to 1.web-counter[.]info (Miuref/Boaxxe Trojan) singled out due to missing Alexa match.

Many new features in CapLoader 1.3

The new features highlighted above are far from the only additions made to CapLoader 1.3. Here is a more complete list of improvements in this release:

  • Support for “Select Flows in PCAP” to extract and select 5-tuples from a PCAP-file. This can be a Snort PCAP with packets that have triggered IDS signatures. This way you can easily extract the whole TCP or UDP flow for each signature match, instead of just trying to make sense of one single packet per alert.
  • Improved packet carver functionality to better carve IP, TCP and UPD packets from any file. This includes memory dumps as well as proprietary and obscure packet capture formats.
  • Support for SCTP flows.
  • DNS parser.
  • Alexa top 1M matching.
  • Input filter and display filter with BPF syntax.
  • Flow Producer-Consumer-Ratio PCR.
  • Flow Transcript can be opened simply by double-clicking a flow.
  • Find form updated with option to hide non-matching flows instead of just selecting the flows that matched the keyword search criteria.
  • New flow transcript encoding with IP TTL, TCP flags and sequence numbers to support analysis of Man-on-the-Side attacks.
  • Faster loading of previously opened files, MD5 hashes don't need to be recalculated.
  • A selected set of flows in the GUI can be inverted simply by right-clicking the flow list and selecting “Invert Selection” or by hitting Ctrl+I.

Downloading CapLoader 1.3

All these new features, except for the Alexa lookup of domain names, are available in our free trial version of CapLoader. So to try out these new features in CapLoader, simply grab a trial download here: (no registration needed)

All paying customers with an older version of CapLoader can grab a free update for version 1.3 at our customer portal.

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Posted by Erik Hjelmvik on Monday, 28 September 2015 07:30:00 (UTC/GMT)

Monday, 21 September 2015 08:23:00 (UTC/GMT)

Covert Man-on-the-Side Attacks


After Edward Snowden exposed NSA's Man-on-the-Side attack capabilities we've started to see IDS signatures that can detect such attacks being released and re-discovered. However, despite these efforts Man-on-the-Side attacks, such as QUANTUM INSERT, can still be carried out without triggering these IDS signatures.

I recently taught a network forensics class in Stockholm. One of the topics covered in this training was how to detect Man-on-the-Side attacks in full content PCAP files.

Man-on-the-Side Explained A Man-on-the-Side (MOTS) attack has the following two characteristics:
  • The attacker can read the traffic and insert new messages, but not to modify or delete messages sent by other participants.
  • The attacker relies on a timing advantage to make sure that the response he sends to the request of a victim arrives before the legitimate response.

In practice this means that the attacker relies on packet injection to insert a TCP packet with a payload to be executed by the victim, such as an HTTP redirect to a malicious web site (source The Intercept). The TCP sequence number of this injected packet will typically be the same as that in the real HTTP response coming from the legitimate web server. Thus, the end node will see two overlapping TCP segments with different application layer data.

In one of the labs, in the network forensics training, students were tasked with finding a Man-on-the-Side attack in a 2.3 GB PCAP dataset. However, the way this MOTS attack was carried out made it invisible to normal signatures designed to detect TCP stream overlaps with different data, such as the Suricata signature 2210050.

alert tcp any any -> any any (msg:"SURICATA STREAM reassembly overlap with different data"; stream-event:reassembly_overlap_different_data; classtype:protocol-command-decode; sid:2210050; rev:2;)

The reason why Suricata and other methods fail to detect this attack is because the injected packet contained both application layer data (an HTTP redirect) and a TCP FIN flag. Upon receiving this spoofed packet the client (victim) followed the redirect as well as closed down its current TCP socket to the web server, by responding with a FIN+ACK packet. Subsequent packets sent by the real web server were then ignored by the client since the TCP socket was already closed when they arrived.

Stream reassembly engines in intrusion detection systems also ignore packets sent after the TCP tear-down, since the TCP session is assumed to be closed at this point. Overlapping TCP segments with different data are therefore not detected by intrusion detection systems when an injected TCP packet carries the FIN flag. I've created an example PCAP file, which illustrate this behavior, called mots-with-fin.pcap (this is not the MOTS attack analyzed in my training). Here's what the PCAP file looks like when analyzed with Tshark:

tshark -r mots-with-fin.pcap -T fields -e ip.src -e ip.dst -e ip.ttl -e tcp.seq -e tcp.flags -e http.response.code -e http.response.phrase 64 189665416  0x0002       54 4114717473 0x0012 64 189665417  0x0010 64 189665417  0x0018       64 4114717474 0x0019 302 Found <--INJECTED 64 189665756  0x0010       54 4114717474 0x0010 64 189665756  0x0011       54 4114717474 0x0018 301 Moved Permanently

Frame number 5 is the injected “302 Found” packet spoofed by the attacker. The TCP flag value 0x19 translates to FIN+PUSH+ACK, which is the attackers attempt to tear-down the TCP connection. The client responds with a FIN+ACK (0x11) in frame 8. The final frame is the real HTTP response coming from the legitimate web server.

Detecting MOTS Attacks

Martin Bruse was one of the guys taking the network forensics class last week. After realizing that there currently doesn't seem to exist any effective method for automatically detecting TCP segment overlaps with different data, regardless of the TCP state, Martin developed a tool called qisniff. This is what it looks like when mots-with-fin.pcap is analyzed with qisniff:

go run qisniff.go -file mots_with_fin.pcap
-> 4114717474
HTTP/1.1 302 Found
Location: //
Content-Length: 0

HTTP/1.1 301 Moved Permanently
Date: Tue, 21 Apr 2015 00:40:01 GMT

In the output above we can see the injected content <A> and the legitimate content from the real web server <B>. What qisniff does is basically reassembling streams and comparing the application layer data in new TCP segments with that in previously received segments. This is a very generic way of detecting any form of packet injection in a TCP stream, regardless if it is done as part of a Quantum Insert attack, an Airpwn injection or some brand new packet injection attack.

Martin's qisniff tool is open sourced under a GPLv2 license and is available on GitHub here:

To run qisniff you need to have Go 1.5 installed as well as gopacket.


We would like to thank Fox-IT for publishing their great blog post Deep dive into QUANTUM INSERT, in which they shed some light on many technical details of Man-on-the-Sida attacks as well as published IDS signatures designed to detect such attacks.

UPDATE 2015-09-28

Suricata's lead developer Victor Julien has now updated Suricata to fix this bug. The updated code is now merged into the main branch of Suricata and released as part of Suricata 2.0.9.

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Posted by Erik Hjelmvik on Monday, 21 September 2015 08:23:00 (UTC/GMT)

Monday, 17 August 2015 08:45:00 (UTC/GMT)

Rinse-Repeat Intrusion Detection

I am a long time skeptic when it comes to blacklists and other forms of signature based detection mechanisms. The information security industry has also declared the signature based anti-virus approach dead several times during the past 10 years. Yet, we still rely on anti-virus signatures, IDS rules, IP blacklists, malware domain lists, YARA rules etc. to detect malware infections and other forms of intrusions in our networks. This outdated approach puts a high administrative burden on IT and security operations today, since we need to keep all our signature databases up to date, both when it comes to end point AV signatures as well as IDS rules and other signature based detection methods and threat feeds. Many organizations probably spend more time and money on updating all these blacklists and signature databases than actually investigating the security alerts these detection systems generate. What can I say; the world is truly upside down...

Shower image by Nevit Dilmen Image: Shower by Nevit Dilmen.

I would therefore like to use this blog post to briefly describe an effective blacklist-free approach for detecting malware and intrusions just by analyzing network traffic. My approach relies on a combination of whitelisting and common sense anomaly detection (i.e. not the academic statistical anomaly detection algorithms that never seem to work in reality). I also encourage CERT/CSIRT/SOC/SecOps units to practice Sun Tzu's old ”know yourself”, or rather ”know your systems and networks” approach.

Know your enemy and know yourself and you can fight a hundred battles without disaster.
- Sun Tzu in The Art of War
Art of War in Bamboo by vlasta2
Image: Art of War in Bamboo by vlasta2

My method doesn't rely on any dark magic, it is actually just a simple Rinse-Repeat approach built on the following steps:

  1. Look at network traffic
  2. Define what's normal (whitelist)
  3. Remove that
  4. GOTO 1.

After looping through these steps a few times you'll be left with some odd network traffic, which will have a high ratio of maliciousness. The key here is, of course, to know what traffic to classify as ”normal”. This is where ”know your systems and networks” comes in.

What Traffic is Normal?

I recently realized that Mike Poor seems to be thinking along the same lines, when I read his foreword to Chris Sanders' and Jason Smith's Applied NSM:

The next time you are at your console, review some logs. You might think... "I don't know what to look for". Start with what you know, understand, and don't care about. Discard those. Everything else is of interest.

Applied NSM

Following Mike's advice we might, for example, define“normal” traffic as:

  • HTTP(S) traffic to popular web servers on the Internet on standard ports (TCP 80 and 443).
  • SMB traffic between client networks and file servers.
  • DNS queries from clients to your name server on UDP 53, where the servers successfully answers with an A, AAAA, CNAME, MX, NS or SOA record.
  • ...any other traffic which is normal in your organization.

Whitelisting IP ranges belonging to Google, Facebook, Microsoft and Akamai as ”popular web servers” will reduce the dataset a great deal, but that's far from enough. One approach we use is to perform DNS whitelisting by classifying all servers with a domain name listed in Alexa's Top 1 Million list as ”popular”.

You might argue that such a method just replaces the old blacklist-updating-problem with a new whitelist-updating-problem. Well yes, you are right to some extent, but the good part is that the whitelist changes very little over time compared to a blacklist. So you don't need to update very often. Another great benefit is that the whitelist/rinse-repeat approach also enables detection of 0-day exploits and C2 traffic of unknown malware, since we aren't looking for known badness – just odd traffic.

Hunting with Rinse-Repeat

Mike Poor isn't the only well merited incident handler who seems to have adopted a strategy similar to the Rinse-Repeat method; Richard Bejtlich (former US Air Force CERT and GE CIRT member) reveal some valuable insight in his book “The Practice of Network Security Monitoring”:

I often use Argus with Racluster to quickly search a large collection of session data via the command line, especially for unexpected entries. Rather than searching for specific data, I tell Argus what to omit, and then I review what’s left.

In his book Richard also mentions that he uses a similar methodology when going on “hunting trips” (i.e. actively looking for intrusions without having received an IDS alert):

Sometimes I hunt for traffic by telling Wireshark what to ignore so that I can examine what’s left behind. I start with a simple filter, review the results, add another filter, review the results, and so on until I’m left with a small amount of traffic to analyze.

The Practice of NSM

I personally find Rinse-Repeat Intrusion Detection ideal for hunting, especially in situations where you are provided with a big PCAP dataset to answer the classic question “Have we been hacked?”. However, unfortunately the “blacklist mentality” is so conditioned among incident responders that they often choose to crunch these datasets through blacklists and signature databases in order to then review thousands of alerts, which are full of false positives. In most situations such approaches are just a huge waste of time and computing power, and I'm hoping to see a change in the incident responders' mindsets in the future.

I teach this “rinse-repeat” intrusion detection method in our Network Forensics Training. In this class students get hands-on experience with a dataset of 3.5 GB / 40.000 flows, which is then reduced to just a fraction through a few iterations in the rinse-repeat loop. The remaining part of the PCAP dataset has a very high ratio of hacking attacks as well as command-and-control traffic from RAT's, backdoors and botnets.

UPDATE 2015-10-07

We have now published a blog post detailing how to use dynamic protocol detection to identify services running on non-standard ports. This is a good example on how to put the Rinse-Repeat methodology into practice.

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Posted by Erik Hjelmvik on Monday, 17 August 2015 08:45:00 (UTC/GMT)

Wednesday, 03 June 2015 21:30:00 (UTC/GMT)

Two-day Network Forensics Class in Stockholm

Network Forensics Training in Stockholm

We are running a two-day Network Forensics class in Stockholm on 15-16th of September. logo Our class is held the days before the SEC-T conference, which is a great technical information security conference in Stockholm, and at the same venue (Nalen). Visitors can thereby plan 4 days of training and conferencing in Stockholm without having to transfer between hotels.

The Network Forensics class consists of a mix of theory and hands-on labs, where students will learn to analyze Full Packet Capture (FPC) files. The scenarios in the labs are primarily focused at network forensics for incident response, but are also relevant for law enforcement/internal security etc. where the network traffic of a suspect or insider is being monitored.

You can find more information about the class here:

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Posted by Erik Hjelmvik on Wednesday, 03 June 2015 21:30:00 (UTC/GMT)

Tuesday, 02 June 2015 20:00:00 (UTC/GMT)

T-shirt : PCAP or it didn't happen

We received the first batch of our awesome “PCAP or it didn't happen” T-shirts today!

PCAP or it didn't happen T-shirt

Tshirt specs:

  • Color: Black
  • Print: RJ45 socket in silver, "PCAP or it didn't happen" in white
  • Fabric: 100% cotton

Want one for yourself? Visit to order your own PCAP or it didn't happen t-shirt!

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Posted by Erik Hjelmvik on Tuesday, 02 June 2015 20:00:00 (UTC/GMT)


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Recommended Books

» The Practice of Network Security Monitoring, Richard Bejtlich (2013)

» Applied Network Security Monitoring, Chris Sanders and Jason Smith (2013)

» Network Forensics, Sherri Davidoff and Jonathan Ham (2012)

» The Tao of Network Security Monitoring, Richard Bejtlich (2004)

» Practical Packet Analysis, Chris Sanders (2011)

» Windows Forensic Analysis, Harlan Carvey (2009)

» TCP/IP Illustrated, Volume 1, Kevin Fall and Richard Stevens (2011)

» Industrial Network Security, Eric D. Knapp and Joel Langill (2014)