A Deeper Dive into the NSL-KDD Data Set

scenery of mountain canyon

Have you ever wondered how your computer/network is able to avoid being infected with malware and bad traffic inputs from the internet? The reason why it can detect it so well is because there are systems in place to protect your valuable information held in your computer or networks. These systems that detect malicious traffic inputs are called Intrusion Detection Systems (IDS) and are trained on internet traffic record data. The most common data set is the NSL-KDD, and is the benchmark for modern-day internet traffic.

A brief history lesson on the NSL-KDD dataset

The NSL-KDD data set is not the first of its kind. The KDD cup was an International Knowledge Discovery and Data Mining Tools Competition. In 1999, this competition was held with the goal of collecting traffic records. The competition task was to build a network intrusion detector, a predictive model capable of distinguishing between “bad’’ connections, called intrusions or attacks, and “good’’ normal connections. As a result of this competition, a mass amount of internet traffic records were collected and bundled into a data set called the KDD’99, and from this, the NSL-KDD data set was brought into existence, as a revised, cleaned-up version of the KDD’99 from the University of New Brunswick.

An overarching breakdown

This data set is comprised of four sub data sets: KDDTest+, KDDTest-21, KDDTrain+, KDDTrain+_20Percent, although KDDTest-21 and KDDTrain+_20Percent are subsets of the KDDTrain+ and KDDTest+. From now on, KDDTrain+ will be referred to as train and KDDTest+ will be referred to as test. The KDDTest-21 is a subset of test, without the most difficult traffic records (Score of 21), and the KDDTrain+_20Percent is a subset of train, whose record count makes up 20% of the entire train dataset. That being said, the traffic records that exist in the KDDTest-21 and KDDTrain+_20Percent are already in test and train respectively and aren’t new records held out of either dataset.

These data sets contain the records of the internet traffic seen by a simple intrusion detection network and are the ghosts of the traffic encountered by a real IDS and just the traces of its existence remains. The data set contains 43 features per record, with 41 of the features referring to the traffic input itself and the last two are labels (whether it is a normal or attack) and Score (the severity of the traffic input itself).

Types of attacks

Within the data set exists 4 different classes of attacks: Denial of Service (DoS), Probe, User to Root(U2R), and Remote to Local (R2L). A brief description of each attack can be seen below:

  • DoS is an attack that tries to shut down traffic flow to and from the target system. The IDS is flooded with an abnormal amount of traffic, which the system can’t handle, and shuts down to protect itself. This prevents normal traffic from visiting a network. An example of this could be an online retailer getting flooded with online orders on a day with a big sale, and because the network can’t handle all the requests, it will shut down preventing paying customers to purchase anything. This is the most common attack in the data set.
  • Probe or surveillance is an attack that tries to get information from a network. The goal here is to act like a thief and steal important information, whether it be personal information about clients or banking information.
  • U2R is an attack that starts off with a normal user account and tries to gain access to the system or network, as a super-user (root). The attacker attempts to exploit the vulnerabilities in a system to gain root privileges/access.
  • R2L is an attack that tries to gain local access to a remote machine. An attacker does not have local access to the system/network, and tries to “hack” their way into the network.

It is noticed from the descriptions above that DoS acts differently from the other three attacks, where DoS attempts to shut down a system to stop traffic flow altogether, whereas the other three attempts to quietly infiltrate the system undetected.

In the table below, a breakdown of the different subclasses of each attack that exists in the data set is shown:

NSLKDD Attack Subclasses

Although these attacks exist in the data set, the distribution is heavily skewed. A breakdown of the record distribution can be seen in the table below. Essentially, more than half of the records that exist in each data set are normal traffic, and the distribution of U2R and R2L are extremely low. Although this is low, this is an accurate representation of the distribution of modern-day internet traffic attacks, where the most common attack is DoS and U2R and R2L are hardly ever seen.

NSLKDD Attack Distribution

Feature breakdown

The features in a traffic record provide the information about the encounter with the traffic input by the IDS and can be broken down into four categories: Intrinsic, Content, Host-based, and Time-based. Below is a description of the different categories of features:

  • Intrinsic features can be derived from the header of the packet without looking into the payload itself, and hold the basic information about the packet. This category contains features 1–9.
  • Content features hold information about the original packets, as they are sent in multiple pieces rather than one. With this information, the system can access the payload. This category contains features 10–22.
  • Time-based features hold the analysis of the traffic input over a two-second window and contains information like how many connections it attempted to make to the same host. These features are mostly counts and rates rather than information about the content of the traffic input. This category contains features 23–31.
  • Host-based features are similar to Time-based features, except instead of analyzing over a 2-second window, it analyzes over a series of connections made (how many requests made to the same host over x-number of connections). These features are designed to access attacks, which span longer than a two-second window time-span. This category contains features 32–41.

The feature types in this data set can be broken down into 4 types:

  • 4 Categorical (Features: 2, 3, 4, 42)
  • 6 Binary (Features: 7, 12, 14, 20, 21, 22)
  • 23 Discrete (Features: 8, 9, 15, 23–41, 43)
  • 10 Continuous (Features: 1, 5, 6, 10, 11, 13, 16, 17, 18, 19)

A breakdown of the possible values for the categorical features can be seen in the table below. There are 3 possible Protocol Type values, 60 possible Service values, and 11 possible Flag values.

NSLKDD Categorical Features

Unlike Protocol Type and Service whose values are self-explanatory (these values describe the connection), Flag is not very easy to understand. The Flag feature describes the status of the connection, and whether a flag was raised or not. Each value in Flag represents a status a connection had and the explanations of each value can be found in the table below.

NSLKDD Flags

TL; DR

A description of each feature and a breakdown of the data set can be seen in the google spreadsheet here.

Share on facebook
Share on twitter
Share on linkedin

Related Articles

Gerry Saporito

CTO & Software Engineer

Full Stack and Distributed Systems

Gerry is a co-founder & the CTO of Lumaki Labs, a startup empowering companies  to build future-proof talent pipelines by building a platform to maximize the internship lifecycle. When he isn’t working, he is playing tennis, watching anime, or with his Shiba Inu named Primo.

My Personal Favourites
Explore