在過去的十年裡，網際網路的發展已經遠遠超過大部分學者的預計。由於這個原因，網絡犯罪攻擊已經從伺服器端攻擊轉移到客戶端攻擊。打擊用戶端攻擊的主要方式是透過客戶端誘捕技術檢測惡意網站並且公布網域黑名單，並且讓有管理權力者處理。然而，這項技術缺乏視覺化分析，以了解多個惡意網站間的整體鏈接結構，並以整體鏈結結構的角度去排名惡意網域或惡意網頁的危脅程度。
在本研究中，我們提出了以客戶端誘捕系統日誌建構可視化社會圖的表示方法。為了增加可視化社會圖的可讀性，並從他們的整體鏈接結構排名惡意網站或網頁的重要性，我們發展出圖案檢測演算法應用在可視化社會圖上 。此外，我們應用社會網絡中心性的測量並加入權重鏈結屬性函數去排名的威脅程度，其中包括：（ 1 ）高度危險網站(Katz centrality)， （ 2 ）高關鍵性網頁(Betweenness centrality)， （ 3 ）熱門惡意軟件(Weighted PageRank algorithm)。

Over the past ten years, the Internet has grown far behind anything that all researchers could have anticipated. For the reason, the cybercriminal attack has shifted away from server-side attack to client-side attack. A primary defense to combat client-side attacks is to detect malicious websites and publish their domains on blacklists and then take them over by the authority through client honeypot technology. However, the weakness for this technology is lack of visualizing analysis to understand the cooperating relationships between multiple malicious websites and ranking threatness of malicious websites by incorporating the overall link structures from and to the domain.
In this research, we proposed an approach to build sociogram representations to visualize multiple client honeypot logs. In order to simplify repeated link characteristics for aiding visualization readability and ranking the importance of malicious hosts from their overall link structure, motifs detection algorithm is developed to the socialgram. In addition, we applied social network centrality measurements incorporating the weighted link attributes functions to rank the threatness including (1) High-threatness hostname Katz centrality, (2) Critical URLs connectivity by betweenness centrality, (3) Malware Popularity by weighted PageRank algorithm. Finally, several interesting findings were explored by socializing analysis.

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