Andreas Bogner
ABSTRACT
Modern IoT solutions are often an intricate system of interdependent components. Traditional monitoring techniques may not be sufficient to ensure the correct operation of those systems. We present an on-line machine learning approach for anomaly detection that is optimized for interpretability. The aim is to make it as intuitive as possible for human operators to derive insights about the system. To this end we combine characteristics of the system into sets of features that can be rendered graphically. Our solution builds on open source components and applies to any time series of numerical data. This work originated within a larger project on connected mobility that uses Blockchain technology to guarantee data integrity. Hence we demonstrate some results at the example of the public Ethereum blockchain. Further work will extend the solution to more general sensor data from the IoT realm.
- Vitalik Buterin. 2014. A Next-Generation Smart Contract and Decentralized Application Platform. (2014). https://github.com/ethereum/wiki/wiki/White-Paper Accessed: 29-May-2017.Google Scholar
- Phil Daian. 2016. Analysis of the DAO exploit. (2016). http://hackingdistributed.com/2016/06/18/analysis-of-the-dao-exploit/ Accessed: 29-May-2017.Google Scholar
- Elasticsearch. 2017. The Open Source Elastic Stack. (2017). https://www.elastic.co/products Accessed: 29-May-2017.Google Scholar
- Thai Pham and Steven Lee. 2016a. Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods. (2016). https://arxiv.org/abs/1611.03941 Accessed: 29-May-2017.Google Scholar
- Thai Pham and Steven Lee. 2016b. Anomaly Detection in the Bitcoin System - A Network Perspective. (2016). https://arxiv.org/abs/1611.03942 Accessed: 29-May-2017.Google Scholar
Index Terms
- Seeing is understanding: anomaly detection in blockchains with visualized features
Recommendations
Predicting Ethereum prices with machine learning based on Blockchain information
Highlights:- Generic Blockchain information of Ethereum is relevant in Ethereum price.
- ...
AbstractWith the growing interest in cryptocurrency and its fundamental algorithm, studies of cryptocurrency price predictions have been actively conducted in various academic disciplines. Since cryptocurrency is generated and consumed by ...
Big data landscapes: improving the visualization of machine learning-based clustering algorithms
AVI '18: Proceedings of the 2018 International Conference on Advanced Visual InterfacesWith the internet, massively heterogeneous data sources need to be understood and classified to provide suitable services to users such as content observation, data exploration, e-commerce, or adaptive learning environments. The key to providing these ...
Using Visualization to Illustrate Machine Learning Models for Genomic Data
ACSW '19: Proceedings of the Australasian Computer Science Week MulticonferenceMassive amounts of genomic data are created for the advent of Next Generation Sequencing technologies. Visualizing these complex genomic data requires not only simply plotting of data but should also invite a decision or a choice. Machine learning has ...
Comments