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• W3200739701 abstract "Receiving relevant information on possible cyber threats, attacks, and data breaches in a timely manner is crucial for early response. The social media platform Twitter hosts an active cyber security community. Their activities are often monitored manually by security experts, such as Computer Emergency Response Teams (CERTs). We thus propose a Twitter-based alert generation system that issues alerts to a system operator as soon as new relevant cyber security related topics emerge. Thereby, our system allows us to monitor user accounts with significantly less workload. Our system applies a supervised classifier, based on active learning, that detects tweets containing relevant information. The results indicate that uncertainty sampling can reduce the amount of manual relevance classification effort and enhance the classifier performance substantially compared to random sampling. Our approach reduces the number of accounts and tweets that are needed for the classifier training, thus making the tool easily and rapidly adaptable to the specific context while also supporting data minimization for Open Source Intelligence (OSINT). Relevant tweets are clustered by a greedy stream clustering algorithm in order to identify significant events. The proposed system is able to work near real-time within the required 15-min time frameand detects up to 93.8% of relevant events with a false alert rate of 14.81%." @default.
• W3200739701 created "2021-09-27" @default.
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• W3200739701 date "2021-01-01" @default.
• W3200739701 modified "2023-10-18" @default.
• W3200739701 title "CySecAlert: An Alert Generation System for Cyber Security Events Using Open Source Intelligence Data" @default.
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• W3200739701 doi "https://doi.org/10.1007/978-3-030-86890-1_24" @default.
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