Darknets are formed by ranges of IP-addresses that do not host services. Darknets constantly receive and record unsolicited traffic, making them valuable instruments to characterize and detect Internet-wide events such as the spreading of new malware, network scans and misconfigurations. Darknets observe scanning activities from thousands of sources, analyzing darknet traffic and detecting coordinated activities can provide meaningful information for network security analysis to detect cyber threats and to counter them more effectively. Methods like DarkVec, inspired from Natural Language Processing to utilize word embedding Word2Vec for darknet traffic analysis, can extract meaningful insight from large amounts of data to learn representations of activities associated with IP addresses.