ID2T-toolkit/README.md

ID2T - Intrusion Detection Dataset Toolkit

A toolkit for injecting synthetic attacks into PCAP files.

Synopsis

As Intrusion Detection Systems encounter growing importance in the area of network security, the need of high quality network datasets for evaluation against real-world attacks rises.

Comparability of the results must be ensured by use of publicly available datasets. Existing datasets, however, suffer from several disadvantages. Often they do not provide ground trouth, consist of outdated traffic and do not contain any payload because of privacy reasons. Moreover, frequently datasets do not contain latest attacks and missing attack labels make it difficult to identify existing attacks and enable a transparent comparison of Intrusion Detection Systems.

The ID2T application was first proposed in [1] and targets the injection of attacks into existing network datasets. At first, it analyzes a given dataset and collects statistics from it. These statistics are stored into a local database. Next, these statistics can be used to define attack parameters for the injection of one or multiple attacks. Finally, the application creates the required attack packets and injects them into the existing file. Resulting in a new PCAP with the injected attacks and a label file indicating the position (timestamps) of the first and last attack packet.

References

[1] Cordero, Vasilomanolakis, Milanov et al.: ID2T: a DIY Dataset Creation Toolkit for Intrusion Detection System

Getting Started

Dependencies

ID2T is written using Python 3 and C++ 11. The main logic is programmed in Python whereas performance critical components are programmed in C++11. The C++11 module uses the Libtins library. The python and c++ modules interact with each other through the Boost.Python library .

Required C++ Libraries

The following packages/libraries are required to compile the ID2T C++ modules

• cmake (minimum version 3.5)
  • ubuntu: apt install build-essential cmake
  • arch: pacman -S cmake
• boost with the python component (minimum version 1.54)
  • ubuntu: apt install libboost-dev libboost-python.61-dev
  • arch: pacman -S boost boost-libs
• libtins (minimum version 3.4)
  • ubuntu: apt install libtins-dev
  • arch: (install from AUR, i.e. pacaur -S libtins)
• python development libraries
  • ubuntu: apt install python3-dev
  • arch: pacman -S python
• sqlite (minimum version 3.0)
  • ubuntu: apt install sqlite3
  • arch: pacman -S sqlite

Required Python Packages

The following packages are required to run ID2T. Install the packages with your preferred package manager. For example, use sudo pip install <packagename>.

• scapy (make sure its the python3 version)
• lea

Notes on the Minimum Package Versions

The minimum version stated in the previous requirements are the versions we have used in the development of ID2T. Other (older) versions might also work; however, we cannot guarantee nor support them. Furthermore, some compilation scripts would need to be manually modified to accommodate these older versions.

Compilation and Installation

Clone the repository to get started: git clone https://git.tk.informatik.tu-darmstadt.de/SPIN/ID2T-toolkit

After cloning the repository, initialize its submodules with

git submodule init
git submodule update

Build the C++ modules and create the ID2T executable: ./build.sh

Run ID2T with the command ./id2t.

Usage examples

In this section, we provide examples on how ID2T is used.

Injecting an attack into an existing dataset

In the following we inject the PortscanAttack into the dataset pcap_capture.pcap:

./CLI.py -i /home/user/pcap_capture.pcap -a PortscanAttack ip.src=10.192.168.178.2 mac.src=32-08-24-DC-8D-27 inject.at-timestamp=1476301843

Explanation: The parameter -i/--input takes the path to the PCAP file. This triggers the statistics calculation of the file. After the calculation, the statistics are stored into a SQLite database. If the statistics were already computed in an earlier run, the data is retrieved from the generated database. This saves time as the calculation of the statistics may take long time - depending on the PCAP file size.

An attack can be injected by providing -a/--attack followed by the attack name and the attack parameters. The available attacks and the allowed attack parameters vary, see section Attack Parameters for details. The parameter -a/--attack can be provided multiple times for injection of multiple attacks. In this case the attacks are injected sequentially.

The Statistics database

Whenever ID2T processes a pcap file, it creates a database detailing many things related to the network traffic it has processed. These details can be seen using the query mode of ID2T. To specify a query against a pcap file, use the option `-q/--query. For example, if we want to know the IP address with the most activity in the pcap file 'test.pcap' we can issue the command:

./CLI.py -i test.pcap -q most_used(ipAddress)

The query mode serves as a place where standard SQL queries (known as user-defined queries) can be issued against the database created for a pcap file. Furthermore, the most commonly used queries are provided with special keywords known as named queries.

• A user-defined query looks like this:
  • e.g. SELECT ipAddress FROM ip_statistics WHERE pktsSent > 1000
• A pre-defined query, known as named query, looks like this:
  • e.g. most_used(ipAddress), random(all(ipAddress))

The named queries can be further divided into two classes:

• selectors - gather information from the database; the result can be a list of values
  • e.g. all(ipAddress)
• extractors - can be applied on gathered data and always reduce the result set to a single element
  • e.g. random(...) returns a randomly chosen element of the list

A complete list of supported named queries can be found in section Named Queries.

If -q/--query is called without an argument, the application enters into REPL query mode. This mode is like a standard read-eval-print-loop (REPL) for SQL queries. In this mode, the user can repeatedly input queries (each query must finish with a ";" (semicolon)); send the query by pressing ENTER and see the response in the terminal:

Example query mode usage: ./CLI.py -i test.pcap -q

Example output:

Input file: /home/user/pcap_capture.pcap
Located statistics database at: /home/pjattke/ID2T_data/db/99/137/81a0a71b0f36.sqlite3
Loaded file statistics in 0.00 sec from statistics database.
Entering into query mode...
Enter statement ending by ';' and press ENTER to send query. Exit by sending an empty query..
most_used(ipAddress);
Query 'most_used(ipAddress);' returned:
203.114.236.243
avg(ttlValue);
Query 'avg(pktsSent);' returned:
5.322

Command reference

Application Arguments

By calling ./CLI.py -h, a list of available application arguments with a short description is shown.

Attack Parameters

In this section the allowed attack parameter for all available attacks are presented.

Portscan Attack

The PortscanAttack currently supports the following attack parameters:

Field name	Description	Notes
mac.src	MAC address of the attacker
mac.dst	MAC address of the victim
ip.src	IP address of the attacker
ip.src.shuffle	Randomizes the source IP address if port.src is a list of ports
ip.dst	IP address of the attacker
port.src	Ports used by the attacker	Can be specified in different ways, e.g.: "22, 23, 24, 8080", "22-24, 8080"
port.src.shuffle	Randomizes the source ports if port.src is a list of ports
port.dst	Ports to be scanned	Can be specified in different ways, e.g.: "22, 23, 24, 8080", "22-24, 8080"
port.dst.shuffle	Randomizes the destination ports if port.dst is a list of ports
port.open	Open ports at the victim's side	Can be specified in different ways, e.g.: "22, 23, 24, 8080", "22-24, 8080"
port.dst.order-desc	Changes the destination port order from ascending (False) to descending (True)
inject.at-timestamp	Starts injecting the attack at the given unix timestamp
inject.after-pkt	Starts injecting the attack after the given packet number
packets.per-second	Number of packets sent per second by the attacker

Statistics DB Queries

SQL Queries

Querying the SQLite database by standard SQL queries requires knowledge about the database scheme. Therefore we provide a short overview about the tables and fields:

Table: ip_statistics

Field name	Description
ipAddress	IP Address of the host these statistics belong to
kybtesSent	KBytes of data sent
kybtesReceived	KBytes of data received
pktsSent	Number of packets sent
pktsReceived	Number of packets received

Table: ip_ttl

Field name	Description
ipAddress	IP Address of the host
ttlValue	TTL value
ttlCount	Number of packets using this TTL value

Table: ip_mac

Field name	Description
ipAddress	IP Address of the host
macAddress	MAC Address of the host

Table: ip_ports

Field name	Description
ipAddress	IP Address of the host
portDirection	If data was received on this port "in", if data was sent from this port "out"
portNumber	Port number
portCount	Number of packets using this port

Table: ip_protocols

Field name	Description
ipAddress	IP Address of the host
protocolName	Name of the protocol, e.g. TCP, UDP, IPv4
protocolCount	Number of packets using this protocol

Table: tcp_mss

Field name	Description
ipAddress	IP Address of the host
mss	Maximum Segment Size (TCP option) used by the host

Named Queries

Selectors are named queries which return a single element or a list of elements, depending on the values in the database and the query.

For example, the named query most_used(ipAddress) may return a single IP address if the most used IP address, based on the sum of packets sent and received, is unique. If there are multiple IP addresses with the same number of packets sent plus packets received, a list of IP addresses is returned. As the user cannot know how many values are returned, the extractors are ignored if the result is a single element.

most_used(ipAddress | macAddress | portNumber | protocolName | ttlValue)

least_used(ipAddress | macAddress | portNumber | protocolName | ttlValue)

avg(pktsReceived | pktsSent | kbytesSent | kbytesReceived | ttlValue | mss)

all(ipAddress | ttlValue | mss | macAddress | portNumber | protocolName)

There are also parameterizable selectors which take conditions as input. Following two examples to show the syntax by example:

ipAddress(macAddress=AA:BB:CC:DD:EE:FF, pktsSent > 1000, kbytesReceived < 1000)
-> returns one or multiple IP addresses matching the given criterias
Supports the fields: macAddress, ttlValue, ttlCount, portName, portNumber, portDirection, kbytesSent, kbytesReceived, pktsSent, pktsReceived,

macAddress(ipAddress=192.168.178.2)
-> returns the MAC address matching the given criteria
Supports the field: ipAddress

Extractors are to be used on the result of a named query. If the result is a list, applying an extractor reduces the result set to a single element. If the result is already a single element, the extractor is ignored.

random(...)  -> returns a random element from a list
first(...)   -> returns the first element from a list
last(...)    -> returns the last element from a list

Attention: Named queries are designed to be combined with extractors, like random(all(ipAddress)). But it is currently NOT possible to encapsulate multiple named queries, like macAddress(ipAddress=most_used(ipAddress)). This can be circumvented by first querying most_used(ipAddress) and then inserting the result as argument in macAddress(…).

Versioning

The SemVer is used for versioning. For currently available versions of ID2T, see page releases.

Release History

• 0.1.0: Initial release
  • Added attack: Portscan Attack

Authors

• Emmanouil Vasilomanolakis - ID2T idea, guidance and suggestions during development

• Carlos Garcia - ID2T idea, guidance and suggestions during development

• Nikolay Milanov - development of first prototype within his Master Thesis

• Patrick Jattke - development of first public release as part of his Bachelor Thesis

License

Distributed under the MIT license. See LICENSE for more information.