On the application of active learning for efficient and effective IoT botnet detection-Reference-Cited by-同舟云学术

On the application of active learning for efficient and effective IoT botnet detection

Published:2023-04 Issue: Volume:141 Page:40-53
ISSN:0167-739X
Container-title:Future Generation Computer Systems
language:en
Short-container-title:Future Generation Computer Systems

Author:

Guerra-Manzanares Alejandro^ORCID,Bahsi Hayretdin

Publisher

Elsevier BV

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Reference34 articles.

1. Botnets: A survey;Silva;Comput. Netw.,2013

2. Cisco annual internet report (2018–2023) white paper;Cisco,2020

3. M. Antonakakis, T. April, M. Bailey, M. Bernhard, E. Bursztein, J. Cochran, Z. Durumeric, J.A. Halderman, L. Invernizzi, M. Kallitsis, D. Kumar, C. Lever, Z. Ma, J. Mason, D. Menscher, C. Seaman, N. Sullivan, K. Thomas, Y. Zhou, Understanding the Mirai Botnet, in: Proceedings of the 26th USENIX Security Symposium, 2017, pp. 1093–1110.

4. The cost of malware containment;Ponemon Institute LLC,2015

5. A survey of data mining and machine learning methods for cyber security intrusion detection;Buczak;IEEE Commun. Surv. Tutor.,2015

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Stream clustering guided supervised learning for classifying NIDS alerts;Future Generation Computer Systems;2024-06

2. Adaptive and augmented active anomaly detection on dynamic network traffic streams;Frontiers of Information Technology & Electronic Engineering;2024-03

3. Network IDS alert classification with active learning techniques;Journal of Information Security and Applications;2024-03

4. Unmasking Cybercrime with Artificial-Intelligence-Driven Cybersecurity Analytics;Sensors;2023-07-11