Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection

Furkat Safarov, Mainak Basak, Rashid Nasimov, Akmalbek Abdusalomov () and Young Im Cho ()
Additional contact information
Furkat Safarov: Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Republic of Korea
Mainak Basak: Department of AI Software, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Republic of Korea
Rashid Nasimov: Department of Artificial Intelligence, Tashkent State University of Economics, Tashkent 100066, Uzbekistan
Akmalbek Abdusalomov: Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Republic of Korea
Young Im Cho: Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Republic of Korea

Future Internet, 2023, vol. 15, issue 9, 1-19

Abstract: In the rapidly evolving landscape of internet usage, ensuring robust cybersecurity measures has become a paramount concern across diverse fields. Among the numerous cyber threats, denial of service (DoS) and distributed denial of service (DDoS) attacks pose significant risks, as they can render websites and servers inaccessible to their intended users. Conventional intrusion detection methods encounter substantial challenges in effectively identifying and mitigating these attacks due to their widespread nature, intricate patterns, and computational complexities. However, by harnessing the power of deep learning-based techniques, our proposed dense channel-spatial attention model exhibits exceptional accuracy in detecting and classifying DoS and DDoS attacks. The successful implementation of our proposed framework addresses the challenges posed by imbalanced data and exhibits its potential for real-world applications. By leveraging the dense channel-spatial attention mechanism, our model can precisely identify and classify DoS and DDoS attacks, bolstering the cybersecurity defenses of websites and servers. The high accuracy rates achieved across different datasets reinforce the robustness of our approach, underscoring its efficacy in enhancing intrusion detection capabilities. As a result, our framework holds promise in bolstering cybersecurity measures in real-world scenarios, contributing to the ongoing efforts to safeguard against cyber threats in an increasingly interconnected digital landscape. Comparative analysis with current intrusion detection methods reveals the superior performance of our model. We achieved accuracy rates of 99.38%, 99.26%, and 99.43% for Bot-IoT, CICIDS2017, and UNSW_NB15 datasets, respectively. These remarkable results demonstrate the capability of our approach to accurately detect and classify various types of DoS and DDoS assaults. By leveraging the inherent strengths of deep learning, such as pattern recognition and feature extraction, our model effectively overcomes the limitations of traditional methods, enhancing the accuracy and efficiency of intrusion detection systems.

Keywords: network; cybersecurity; DDoS; attention; IoT; Densenet (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/15/9/297/pdf (application/pdf)
https://www.mdpi.com/1999-5903/15/9/297/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:15:y:2023:i:9:p:297-:d:1230971

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().