Detecting Internet Worms, Ransomware, and Blackouts Using Recurrent Neural Networks
| Autor: | Ljiljana Trajkovic, Zhida Li, Ana Laura Gonzalez Rios |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Route Views
Computer science business.industry ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS Blackout 020206 networking & telecommunications 02 engineering and technology Recurrent neural network Border Gateway Protocol 0202 electrical engineering electronic engineering information engineering medicine Ransomware 020201 artificial intelligence & image processing Anomaly detection The Internet medicine.symptom business Computer network |
| Zdroj: | SMC |
| Popis: | Analyzing and detecting Border Gateway Protocol (BGP) anomalies are topics of great interest in cybersecurity. Various anomaly detection approaches such as time series and historical-based analysis, statistical validation, reachability checks, and machine learning have been applied to BGP datasets. In this paper, we use BGP update messages collected from Reseaux IP Europeens and Route Views to detect BGP anomalies caused by Slammer worm, WannaCrypt ransomware, and Moscow blackout by employing recurrent neural network machine learning algorithms. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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