Generating Security Intelligence through Social Network Sentiment Analysis

• Item type: Symposium(1)
• 公開日: 2018-10-15
• タイトル: Generating Security Intelligence through Social Network Sentiment Analysis
• タイトル / 言語: en
• タイトル: Generating Security Intelligence through Social Network Sentiment Analysis
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: Text Mining，Computer Security，Machine Learning，Security Analytics，Social Networking
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_5794
• 資源タイプ: conference paper
• 著者所属: 九州大学
• 著者所属: 九州大学
• 著者所属(英): en
• 著者所属(英): Graduate School Faculty of Information Science and Electrical Engineering, Kyushu University
• 著者所属(英): en
• 著者所属(英): Graduate School Faculty of Information Science and Electrical Engineering, Kyushu University
• 著者名: ロドリゲズ, アリエル; Koji, Okamura
• 著者名(英): Ariel, Rodriguez; Koji, Okamura
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Cybersecurity has moved to the forefront of the technology world in recent years with the increase in number and sophistication of attacks. Even Though security has become such a crucial aspect of all organisations the security industry still largely holds a defensive stance where reacting to attacks after they have occurred is more common than proactively finding ways to counter these threats. There are many data sources such as social networking sites, security news sites and blogs that can be used to improve this situation and create solutions that help prepare for attacks before they occur. In this paper we present a framework that performs sentiment analysis on security based tweets with the aim to provide relevant security information that can be used by analysts or by security devices.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Cybersecurity has moved to the forefront of the technology world in recent years with the increase in number and sophistication of attacks. Even Though security has become such a crucial aspect of all organisations the security industry still largely holds a defensive stance where reacting to attacks after they have occurred is more common than proactively finding ways to counter these threats. There are many data sources such as social networking sites, security news sites and blogs that can be used to improve this situation and create solutions that help prepare for attacks before they occur. In this paper we present a framework that performs sentiment analysis on security based tweets with the aim to provide relevant security information that can be used by analysts or by security devices.
• 書誌レコードID / 識別子タイプ: NCID
• 書誌レコードID / 関連識別子: ISSN 1882-0840
• 書誌情報: コンピュータセキュリティシンポジウム2018論文集; 巻 2018, 号 2, p. 93-100
• 出版者 / 言語: ja
• 出版者: 情報処理学会