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Decade of Cybersecurity Data Could Predict Future Malicious Online Activity


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The purpose of the FinalBlacklist dataset is to help cybersecurity specialists derive new insights on cybersecurity threats, and potentially predict future malicious online activity.

Researchers have developed a comprehensive dataset of the global cybersecurity threat landscape from 2007 to 2017, in the hope it can help cybersecurity specialists derive new insights on cybersecurity threats.

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Researchers from Australia’s Commonwealth Scientific and Industrial Research Organization (CSIRO) Data61 digital research network and Macquarie University, in collaboration with Nokia Bell Labs and the University of Sydney, have developed a comprehensive dataset of the global cybersecurity threat landscape from 2007 to 2017.

The purpose of the FinalBlacklist dataset is to help cybersecurity specialists derive new insights on cybersecurity threats, and potentially predict future malicious online activity.

The team collected 51.6 million reports of malicious online activity involving 662,000 unique IP addresses worldwide.

The data was categorized using machine learning techniques into six classes: malware, phishing, fraudulent services, potentially unwanted programs, exploits, and spamming.

Said Macquarie University’s Dali Kaafar, "Our analysis revealed a consistent minority of repeat offenders that contributed a majority of the mal-activity reports. Detecting and quickly reacting to the emergence of these mal-activity contributors could significantly reduce the damage inflicted.”

From CSIRO (Australia)
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Abstracts Copyright © 2019 SmithBucklin, Washington, DC, USA


 

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