Deep Reinforcement Learning for intrusion detection in Internet of Things: Best practices, lessons learnt, and open challenges
Abstract The Internet of Things (IoT) scenario places important challenges even for deep
learning-based intrusion detection systems. IoTs are highly heterogeneous networks in …
learning-based intrusion detection systems. IoTs are highly heterogeneous networks in …
CCNSim: An artificial intelligence enabled classification, clustering and navigation simulator for Social Internet of Things
SD Mohana, SPS Prakash, K Krinkin - Engineering Applications of Artificial …, 2023 - Elsevier
With the advent of the IoT technology stack and the need for information and process
ubiquity, social IoT (SIoT) is becoming popular to cater to intelligent and connected living …
ubiquity, social IoT (SIoT) is becoming popular to cater to intelligent and connected living …
Class imbalance and concept drift invariant online botnet threat detection framework for heterogeneous IoT edge
A Nitish, J Hanumanthappa, SPS Prakash… - Computers & Security, 2024 - Elsevier
Heterogeneous networks (HetIoT) of high-capacity and resource-constrained IoT devices
and their edge associations for on-device distributed critical workloads—called the edge-of …
and their edge associations for on-device distributed critical workloads—called the edge-of …
Heterogeneity Tolerance in IoT Botnet Attack Classification
S Kalenowski, D Arnold, M Gromov… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
Due to the rapid adoption of Internet of Things (IoT) technologies, many networks are
composed of a patchwork of devices designed by different software and hardware …
composed of a patchwork of devices designed by different software and hardware …
Lightweight Network Context Change Detection for Online Defense Against Heterogeneous IoT Attacks
A Nitish, J Hanumanthappa… - … and Security (ICCINS …, 2023 - ieeexplore.ieee.org
The heterogeneous resource-constrained IoT devices with their edge associations-called
the edge-of-things (EoT), handle dynamic, time-critical, distributed workloads and are …
the edge-of-things (EoT), handle dynamic, time-critical, distributed workloads and are …
Deep Reinforcement Learning for intrusion detection in Internet of Things:: Best practices, lessons learnt, and open challenges
Abstract The Internet of Things (IoT) scenario places important challenges even for deep
learning-based intrusion detection systems. IoTs are highly heterogeneous networks in …
learning-based intrusion detection systems. IoTs are highly heterogeneous networks in …
CCNSim:: An artificial intelligence enabled classification, clustering and navigation simulator for Social Internet of Things
M SD, SPS Prakash, K Krinkin - 2023 - dl.acm.org
With the advent of the IoT technology stack and the need for information and process
ubiquity, social IoT (SIoT) is becoming popular to cater to intelligent and connected living …
ubiquity, social IoT (SIoT) is becoming popular to cater to intelligent and connected living …
Deep Reinforcement Learning for Intrusion Detection in Internet of Things: Best Practices, Lessons Learnt, and Open Challenges
Abstract The Internet of Things (IoT) scenario places important challenges even for deep
learning-based intrusion detection systems. IoTs are highly heterogeneous networks in …
learning-based intrusion detection systems. IoTs are highly heterogeneous networks in …