A Two-Fold Machine Learning Approach to Prevent and Detect IoT Botnet Attacks
The botnet attack is a multi-stage and the most prevalent cyber-attack in the Internet of Things (IoT) environment that initiates with scanning activity and ends at the distributed denial of service (DDoS) attack. The existing studies mostly focus on detecting botnet attacks after the IoT devices ge...
Main Authors: | Faisal Hussain, Syed Ghazanfar Abbas, Ivan Miguel Pires, Sabeeha Tanveer, Ubaid U. Fayyaz, Nuno M. Garcia, Ghalib A. Shah, Farrukh Shahzad |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9627657/ |
Similar Items
-
Memcached: An Experimental Study of DDoS Attacks for the Wellbeing of IoT Applications
by: Nivedita Mishra, et al.
Published: (2021-12-01) -
Present Status of Distributed Denial of Service (DDoS) Attacks in Internet World
by: Rajeev Singh, et al.
Published: (2019-08-01) -
Evaluating Awareness and Perception of Botnet Activity within Consumer Internet-of-Things (IoT) Networks
by: Christopher D. McDermott, et al.
Published: (2019-02-01) -
A Low-Cost Distributed Denial-of-Service Attack Architecture
by: Kaifan Huang, et al.
Published: (2020-01-01) -
Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron
by: Sheeraz Ahmed, et al.
Published: (2023-02-01)