With increasing amount of data, the threat of malware keeps growing recently. The malicious actions embedded in nonexecutable documents especially (e.g., PDF files) can be more dangerous, because it is difficult to detect and most users are not aware of such type of malicious attacks. In this paper, we design a convolutional neural network to tackle the malware detection on the PDF files. We collect malicious and benign PDF files and manually label the byte sequences within the files. We intensively examine the structure of the input data and illustrate how we design the proposed network based on the characteristics of data. The proposed network is designed to interpret high-level patterns among collectable spatial clues, thereby predicting whether the given byte sequence has malicious actions or not. By experimental results, we demonstrate that the proposed network outperform several representative machine-learning models as well as other networks with different settings.

The Invisible Internet Project (I2P) is an overlay network that provides secure and anonymous communication channels. EepSites are the anonymous websites hosted in the I2P network. To access the eepSites, DNS requests of a domain name suffixed with the {\sf .i2p} pseudo top-level domain (TLD) are routed within the I2P network. However, not only that {\sf .i2p} queries are leaking in the public DNS infrastructure, but also such leakage has various plausible root causes and implications that are different from other related leakage. In this paper, we analyze the leaked {\sf .i2p} requests captured in the A and J root name servers of the public DNS, showing that a large number of queries are observed and outlining various potential directions of addressing such leakage.

MMORPG (Massively Multiplayer Online Role-Playing Game) is one of the best platforms to observe human's behaviors. In collaboration with a leading online game company, NCSoft, we can observe all behaviors in a large-scale of commercialized MMORPG. Especially, we analyzed the behavioral differences between game bots and human users. We categorized the five groups, Bot-Bot, Bot-All, Human-Human, Human-All and All-All, and we observe the characteristics of six social interaction networks for each group. As a result, we found that there are significant differences in social behaviors between game bots and human.

This article investigates whether individual users are more likely to display malicious behavior after receiving social reinforcement from friends in their online social networks. We analyze the dynamics of game bot diffusion on the basis of real data supplied by a major massively multiplayer online role-playing game company. We find that the social reinforcement, measured by the ratio of bot friends over total friends, affects the likelihood of game bot adoption and the commitment in terms of usage time.

We analyze multi-relational social interaction networks in a large-scale commercial Massively Multiplayer Online Role-Playing Game(MMORPG). Our work is based on data from AION, currently the world's second most-played MMORPG with 3.4 million subscribers as of mid 2010, created and serviced by NCSoft, Inc. We construct and characterize six distinct interactivity networks (Friend, Private Messaging, Party invitation, Trade, Mail, and Shop), each representing diverse player interaction types.