DL‐IDS: a deep learning–based intrusion detection framework for securing IoT

By Yazan Otoum, Dandan Liu, Amiya R. Nayak. Transactions on Emerging Telecommunications Technologies, 2019

309 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/57dccfe4c076d88951dbba9391a55ef88c8057d4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Farhan Ahmad
2. Johari Abdullah
3. Ke He
4. Dan Dongseong Kim
5. Wai Shiang Cheah
6. Muhammad Rizwan Asghar
7. Adnan Shahid Khan
8. Zeeshan Ahmad

In Computer Networks and Communications

1. Ke He
2. Dan Dongseong Kim
3. Muhammad Rizwan Asghar

In Signal Processing

1. Ke He
2. Dan Dongseong Kim
3. Muhammad Rizwan Asghar

Citations in the corpus

• Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey (2023, methodology): “DNNs have been frequently used in NIDS with different parameters and structures proposed [43], [49], [52], [68].”
• Network intrusion detection system: A systematic study of machine learning and deep learning approaches (2020, background): “But there are still some DL algorithms that require more attention like deep reinforcement learning, Hidden Markov Models, etc. for proposing IDS solution for IoT network.”