125 research outputs found
Fraud Pattern Detection for NFT Markets
Non-Fungible Tokens (NFTs) enable ownership and transfer of digital assets using blockchain technology. As a relatively new financial asset class, NFTs lack robust oversight and regulations. These conditions create an environment that is susceptible to fraudulent activity and market manipulation schemes. This study examines the buyer-seller network transactional data from some of the most popular NFT marketplaces (e.g., AtomicHub, OpenSea) to identify and predict fraudulent activity. To accomplish this goal multiple features such as price, volume, and network metrics were extracted from NFT transactional data. These were fed into a Multiple-Scale Convolutional Neural Network that predicts suspected fraudulent activity based on pattern recognition. This approach provides a more generic form of time series classification at different frequencies and timescales to recognize fraudulent NFT patterns. Results showed that over 80% of confirmed fraudulent cases were identified by modeling (recall). For every predicted fraud case, the model was correct 50% of the time (precision). Investors, regulators, and other entities can use these techniques to reduce risk exposure to NFT fraudulent activity
Classification of Time-Series Images Using Deep Convolutional Neural Networks
Convolutional Neural Networks (CNN) has achieved a great success in image
recognition task by automatically learning a hierarchical feature
representation from raw data. While the majority of Time-Series Classification
(TSC) literature is focused on 1D signals, this paper uses Recurrence Plots
(RP) to transform time-series into 2D texture images and then take advantage of
the deep CNN classifier. Image representation of time-series introduces
different feature types that are not available for 1D signals, and therefore
TSC can be treated as texture image recognition task. CNN model also allows
learning different levels of representations together with a classifier,
jointly and automatically. Therefore, using RP and CNN in a unified framework
is expected to boost the recognition rate of TSC. Experimental results on the
UCR time-series classification archive demonstrate competitive accuracy of the
proposed approach, compared not only to the existing deep architectures, but
also to the state-of-the art TSC algorithms.Comment: The 10th International Conference on Machine Vision (ICMV 2017
Using Side Channel Information and Artificial Intelligence for Malware Detection
Cybersecurity continues to be a difficult issue for society especially as the
number of networked systems grows. Techniques to protect these systems range
from rules-based to artificial intelligence-based intrusion detection systems
and anti-virus tools. These systems rely upon the information contained in the
network packets and download executables to function. Side channel information
leaked from hardware has been shown to reveal secret information in systems
such as encryption keys. This work demonstrates that side channel information
can be used to detect malware running on a computing platform without access to
the code involved.Comment: 7 page
Classifying motion states of AUV based on graph representation for multivariate time series
Acknowledgement This work is supported by Natural Science Foundation of Shandong Province (ZR2020MF079) and China Scholarship Council (CSC).Peer reviewedPostprin
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