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Time-relevant and accurate threat information from public domains areessential for cyber security. In a constantly evolving threat landscape, suchinformation assists security researchers in thwarting attack strategies. Inthis work, we collect and analyze threat-related information from Twitter toextract intelligence for proactive security. We first use a convolutionalneural network to classify the tweets as containing or not valuable threatindicators. In particular, to gather threat intelligence from social media, theproposed approach collects pertinent Indicators of Compromise (IoCs) fromtweets, such as IP addresses, URLs, File hashes, domain addresses, and CVE IDs.Then, we analyze the IoCs to confirm whether they are reliable and valuable forthreat intelligence using performance indicators, such as correctness,timeliness, and overlap. We also evaluate how fast Twitter shares IoCs comparedto existing threat intelligence services. Furthermore, through machine learningmodels, we classify Twitter accounts as either automated or human-operated anddelve into the role of bot accounts in disseminating cyber threat informationon social media. Our results demonstrate that Twitter is growing into apowerful platform for gathering precise and pertinent malware IoCs and areliable source for mining threat intelligence.

Can Twitter be used to Acquire Reliable Alerts against Novel Cyber Attacks?