Studying Ransomware Attacks Using Web Search Logs

Cyber attacks are increasingly becoming prevalent and causing significant damage to individuals, businesses and even countries. In particular, ransomware attacks have grown significantly over the last decade. We do the first study on mining insights about ransomware attacks by analyzing query logs from Bing web search engine. We first extract ransomware related queries and then build a machine learning model to identify queries where users are seeking support for ransomware attacks. We show that user search behavior and characteristics are correlated with ransomware attacks. We also analyse trends in the temporal and geographical space and validate our findings against publicly available information. Lastly, we do a case study on 'Nemty', a popular ransomware, to show that it is possible to derive accurate insights about cyber attacks by query log analysis.Comment: To appear in the proceedings of SIGIR 202

Investing in Prevention or Paying for Recovery - Attitudes to Cyber Risk

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Broadly speaking an individual can invest time and effort to avoid becoming victim to a cyber attack and/or they can invest resource in recovering from any attack. We introduce a new game called the pre-vention and recovery game to study this trade-off. We report results from the experimental lab that allow us to categorize different approaches to risk taking. We show that many individuals appear relatively risk loving in that they invest in recovery rather than prevention. We find little difference in behavior between a gain and loss framing