Scalable and Configurable End-to-End Collection and Analysis of IoT Security Data : Towards End-to-End Security in IoT Systems
Title | Scalable and Configurable End-to-End Collection and Analysis of IoT Security Data : Towards End-to-End Security in IoT Systems |
Publication Type | Conference Paper |
Year of Publication | 2019 |
Authors | Roukounaki, Aikaterini, Efremidis, Sofoklis, Soldatos, John, Neises, Juergen, Walloschke, Thomas, Kefalakis, Nikos |
Conference Name | 2019 Global IoT Summit (GIoTS) |
Date Published | June 2019 |
ISBN Number | 978-1-7281-2171-0 |
Keywords | collecting processing security-related datasets, complex IoT deployments, compositionality, Computer architecture, computer network security, configurable data collection infrastructure, configurable end-to-end collection, Data collection, data-driven IoT security, deep learning techniques, effective security analytics algorithms, Internet of Things, IoT platforms, IoT security, IoT security data, IoT systems, learning (artificial intelligence), leverage machine learning, machine learning, modelling security data, Monitoring, Probes, pubcrawl, Scalability, scalable data collection infrastructure, scalable end-to-end collection, scalable infrastructures, Scalable Security, security, security analytics, security data collection systems, security data modelling, security scalability, towards end-to-rnd security |
Abstract | In recent years, there is a surge of interest in approaches pertaining to security issues of Internet of Things deployments and applications that leverage machine learning and deep learning techniques. A key prerequisite for enabling such approaches is the development of scalable infrastructures for collecting and processing security-related datasets from IoT systems and devices. This paper introduces such a scalable and configurable data collection infrastructure for data-driven IoT security. It emphasizes the collection of (security) data from different elements of IoT systems, including individual devices and smart objects, edge nodes, IoT platforms, and entire clouds. The scalability of the introduced infrastructure stems from the integration of state of the art technologies for large scale data collection, streaming and storage, while its configurability relies on an extensible approach to modelling security data from a variety of IoT systems and devices. The approach enables the instantiation and deployment of security data collection systems over complex IoT deployments, which is a foundation for applying effective security analytics algorithms towards identifying threats, vulnerabilities and related attack patterns. |
URL | https://ieeexplore.ieee.org/document/8766407 |
DOI | 10.1109/GIOTS.2019.8766407 |
Citation Key | roukounaki_scalable_2019 |
- scalable infrastructures
- machine learning
- modelling security data
- Monitoring
- Probes
- pubcrawl
- Scalability
- scalable data collection infrastructure
- scalable end-to-end collection
- leverage machine learning
- Scalable Security
- security
- security analytics
- security data collection systems
- security data modelling
- security scalability
- towards end-to-rnd security
- deep learning techniques
- complex IoT deployments
- Compositionality
- computer architecture
- computer network security
- configurable data collection infrastructure
- configurable end-to-end collection
- Data collection
- data-driven IoT security
- collecting processing security-related datasets
- effective security analytics algorithms
- Internet of Things
- IoT platforms
- IoT security
- IoT security data
- IoT systems
- learning (artificial intelligence)