Universität zu Köln

Zeeshan, Muhammad, Riaz, Qaiser ORCID: 0000-0003-3722-4764, Bilal, Muhammad Ahmad ORCID: 0000-0002-7673-9309, Shahzad, Muhammad K., Jabeen, Hajira, Haider, Syed Ali and Rahim, Azizur (2022). Protocol-Based Deep Intrusion Detection for DoS and DDoS Attacks Using UNSW-NB15 and Bot-IoT Data-Sets. IEEE Access, 10. S. 2269 - 2284. PISCATAWAY: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. ISSN 2169-3536

Full text not available from this repository.

Abstract

Since its inception, the Internet of Things (IoT) has witnessed mushroom growth as a breakthrough technology. In a nutshell, IoT is the integration of devices and data such that processes are automated and centralized to a certain extent. IoT is revolutionizing the way business is done and is transforming society as a whole. As this technology advances further, the need to exploit detection and weakness awareness increases to prevent unauthorized access to critical resources and business functions, thereby rendering the system unavailable. Denial of Service (DoS) and Distributed DoS attacks are all too common. In this paper, we propose a Protocol Based Deep Intrusion Detection (PB-DID) architecture, in which we created a data-set of packets from IoT traffic by comparing features from the UNSWNB15 and Bot-IoT data-sets based on flow and Transmission Control Protocol (TCP). We classify non-anomalous, DoS, and DDoS traffic uniquely by taking care of the problems like imbalanced and over-fitting. We have achieved a classification accuracy of 96.3% by using deep learning (DL) technique.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Zeeshan, Muhammad UNSPECIFIED UNSPECIFIED UNSPECIFIED Riaz, Qaiser UNSPECIFIED orcid.org/0000-0003-3722-4764 UNSPECIFIED Bilal, Muhammad Ahmad UNSPECIFIED orcid.org/0000-0002-7673-9309 UNSPECIFIED Shahzad, Muhammad K. UNSPECIFIED UNSPECIFIED UNSPECIFIED Jabeen, Hajira UNSPECIFIED UNSPECIFIED UNSPECIFIED Haider, Syed Ali UNSPECIFIED UNSPECIFIED UNSPECIFIED Rahim, Azizur UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-675305
DOI:	10.1109/ACCESS.2021.3137201
Journal or Publication Title:	IEEE Access
Volume:	10
Page Range:	S. 2269 - 2284
Date:	2022
Publisher:	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Place of Publication:	PISCATAWAY
ISSN:	2169-3536
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language SYSTEM; DESIGN Multiple languages Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/67530