Redirigiendo al acceso original de articulo en 18 segundos...
Inicio  /  Future Internet  /  Vol: 16 Par: 3 (2024)  /  Artículo
ARTÍCULO
TITULO

A Transferable Deep Learning Framework for Improving the Accuracy of Internet of Things Intrusion Detection

Haedam Kim    
Suhyun Park    
Hyemin Hong    
Jieun Park and Seongmin Kim    

Resumen

As the size of the IoT solutions and services market proliferates, industrial fields utilizing IoT devices are also diversifying. However, the proliferation of IoT devices, often intertwined with users? personal information and privacy, has led to a continuous surge in attacks targeting these devices. However, conventional network-level intrusion detection systems with pre-defined rulesets are gradually losing their efficacy due to the heterogeneous environments of IoT ecosystems. To address such security concerns, researchers have utilized ML-based network-level intrusion detection techniques. Specifically, transfer learning has been dedicated to identifying unforeseen malicious traffic in IoT environments based on knowledge distillation from the rich source domain data sets. Nevertheless, since most IoT devices operate in heterogeneous but small-scale environments, such as home networks, selecting adequate source domains for learning proves challenging. This paper introduces a framework designed to tackle this issue. In instances where assessing an adequate data set through pre-learning using transfer learning is non-trivial, our proposed framework advocates the selection of a data set as the source domain for transfer learning. This selection process aims to determine the appropriateness of implementing transfer learning, offering the best practice in such scenarios. Our evaluation demonstrates that the proposed framework successfully chooses a fitting source domain data set, delivering the highest accuracy.

Palabras claves

 Artículos similares

       
 
Thanh-Nam Tran, Van-Cuu Ho, Thoai Phu Vo, Khanh Ngo Nhu Tran and Miroslav Voznak    
The requirements of low latency, low cost, less energy consumption, high flexibility, high network capacity, and high data safety are crucial challenges for future Internet of Things (IoT) wireless networks. Motivated by these challenges, this study deal... ver más
Revista: Future Internet

 
Gengxian Li, Chundong Wang and Huaibin Wang    
Decentralized networks bring us many benefits, but as networks evolve, many nodes either actively or passively become unreachable behind an NAT or a firewall. This has become a hindrance to the development of decentralized networks, where peer-to-peer co... ver más
Revista: Future Internet

 
Hossein Chegini, Ranesh Kumar Naha, Aniket Mahanti and Parimala Thulasiraman    
The number of IoT sensors and physical objects accommodated on the Internet is increasing day by day, and traditional Cloud Computing would not be able to host IoT data because of its high latency. Being challenged of processing all IoT big data on Cloud... ver más
Revista: IoT

 
Nishu Gupta, Ravikanti Manaswini, Bongaram Saikrishna, Francisco Silva and Ariel Teles    
The amalgamation of Vehicular Ad hoc Network (VANET) with the Internet of Things (IoT) leads to the concept of the Internet of Vehicles (IoV). IoV forms a solid backbone for Intelligent Transportation Systems (ITS), which paves the way for technologies t... ver más
Revista: Future Internet

 
Sebastian R. Bader and Maria Maleshkova    
The digital revolution affects every aspect of society and economy. In particular, the manufacturing industry faces a new age of production processes and connected collaboration. The underlying ideas and concepts, often also framed as a new ?Internet of ... ver más
Revista: Future Internet