Distributed attack detection scheme using deep learning approach for Internet of Things

By Abebe Abeshu Diro, Naveen K. Chilamkurti. Future generations computer systems, 2017

901 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/977a7b78cb0e4abce35aa778d18164671f3ced08

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. Hadis Karimipour
2. Petros Spachos
3. Syeda Manjia Tahsien
4. Ekram Hossain
5. Syed Ali Hassan
6. Rasheed Hussain
7. Fatima Hussain
8. Mohammed Ali Al-Garadi

In Computer Networks and Communications

1. Syed Ali Hassan
2. Ekram Hossain
3. Rasheed Hussain
4. Fatima Hussain
5. Petros Spachos
6. Syeda Manjia Tahsien
7. Hadis Karimipour
8. Xiaojiang Du

In Signal Processing

1. Petros Spachos
2. Syeda Manjia Tahsien
3. Hadis Karimipour
4. Mohammed Ali Al-Garadi
5. A. Al-Ali
6. Amr M. Mohamed
7. Ekram Hossain
8. Xiaojiang Du

Citations in the corpus

• Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “Diro and Chilamkurti [324] IoT distributed attacks detection Denial of service, probing, remote to user & user to root Supervised MLP”
• A Survey of Deep Learning Methods for Cyber Security (2019, methodology): “Diro and Chilamkurti [146] tested a DL algorithm’s ability to detect intrusion detection in IoT with a fog ecosystem, which is an architectural style in which computation is shifted from the cloud to the edge of the network routed over the network backbone closer to the user and devices.”
• Machine Learning Based Intrusion Detection Systems for IoT Applications (2019, background): “Diro et al. [21] proposed a deep learning based IDS for defending DoS against IoT networks.”
• Anomaly-based intrusion detection system for IoT networks through deep learning model (2022, neutral): “Thus, attacks on the internet can also be classified as passive or active [7, 18].”
• Machine Learning in IoT Security: Current Solutions and Future Challenges (2019, background): “2) DL-Based Attack Detection and Mitigation in IoT: Diro and Chilamkurti [136] proposed a DL-based attack detection mechanism in IoT by leveraging fog ecosystem.”
• Network Intrusion Detection for IoT Security Based on Learning Techniques (2019, methodology): “Diro and Chilamkurti [150] chose multi-layer DL since it is the most prevalent form of DL.”
• Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection (2018, background): “Diro and Chilamkurti [25] presented IOT/fog network attack detection system.”
• A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, background): “ture abstraction capability; thus, they can provide resilient solutions to the variations of formerly identified cyberattacks or new attacks [264].”
• Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2020, methodology): “being used to detect the intrusion attack [88], [147][148].”
• TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems (2020, neutral): “Even though several studies did use such datasets to evaluate their IoT-related intrusion detection solutions [24]–[28], one can argue that these datasets do not reflect IIoT characteristics since none”