NITIDS: a robust network intrusion dataset
by Santosh Kumar Sahu; Durga Prasad Mohapatra; Sanjaya Kumar Panda
International Journal of Embedded Systems (IJES), Vol. 14, No. 4, 2021

Abstract: In predictive analytics, many multi-disciplinary techniques have been used to analyse the known data in order to make a prediction about the unknown data. For this, an enormous amount of processed data is required to analyse the same irrespective of the domain. Specifically, most of the researchers use superannuated signatures in intrusion detection modelling. This model is unable to train the recent attacks' signatures; hence it is not useful to detect the modern sophisticated attacks. Moreover, it is essential to infer the statistical properties of the dataset and select the samples of the dataset watchfully, to achieve high accuracy, low training and generalisation error. In this paper, we create National Institute of Technology Intrusion Detection System (NITIDS) dataset, an open intrusion dataset that contains the signatures of recent attacks. The dataset contains 60 recent signatures of denial of service, probing, user to root and root to local attacks. The dataset is useful in terms of availability, recent signatures, real-time traffic and openly available for the research community. In the simulation, we process the acquired data with a proposed data preprocessing technique, which deals with missing value imputation, handling redundant samples, balancing data distribution and removing outliers of the dataset.

Online publication date: Tue, 05-Oct-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Embedded Systems (IJES):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com