An unsupervised approach for the detection of zero‐day distributed denial of service attacks in Internet of Things networks
Abstract The authors introduce an unsupervised Intrusion Detection System designed to detect zero‐day distributed denial of service (DDoS) attacks in Internet of Things (IoT) networks. This system can identify anomalies without needing prior knowledge or training on attack information. Zero‐day atta...
Κύριοι συγγραφείς: | Monika Roopak, Simon Parkinson, Gui Yun Tian, Yachao Ran, Saad Khan, Balasubramaniyan Chandrasekaran |
---|---|
Μορφή: | Άρθρο |
Γλώσσα: | English |
Έκδοση: |
Wiley
2024-09-01
|
Σειρά: | IET Networks |
Θέματα: | |
Διαθέσιμο Online: | https://doi.org/10.1049/ntw2.12134 |
Παρόμοια τεκμήρια
Παρόμοια τεκμήρια
-
Channel state information based physical layer authentication for Wi‐Fi sensing systems using deep learning in Internet of things networks
ανά: Monika Roopak, κ.ά.
Έκδοση: (2024-12-01) -
Intelligent Unsupervised Network Traffic Classification Method Using Adversarial Training and Deep Clustering for Secure Internet of Things
ανά: Weijie Zhang, κ.ά.
Έκδοση: (2023-09-01) -
Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things
ανά: Kuburat Oyeranti Adefemi Alimi, κ.ά.
Έκδοση: (2022-07-01) -
Distributed Denial of Service Attacks Detection in Internet of Things Using the Majority Voting Approach
ανά: Habibollah Mazarei, κ.ά.
Έκδοση: (2024-02-01) -
Deep learning in distributed denial-of-service attacks detection method for Internet of Things networks
ανά: Aswad Firas Mohammed, κ.ά.
Έκδοση: (2023-01-01)