Ah Reum Kang

Online game bot detection based on party-play log analysis

Abstract As online games become popular and the boundary between virtual and real economies blurs, cheating in games has proliferated in volume and method. In this paper, we propose a framework for user behavior analysis for bot detection in online games. Specifically, we focus on party play which reflects the social activities among gamers: in a Massively Multi-user Online Role Playing Game (MMORPG), party play is a major activity that game bots exploit to keep their characters safe and facilitate the acquisition of cyber assets in a fashion very different from that of normal humans. Through a comprehensive statistical analysis of user behaviors in game activity logs, we establish threshold levels for the activities that allow us to identify game bots. Based on this, we also build a knowledge base of detection rules, which are generic. We apply our rule reasoner to AION, a popular online game serviced by NCsoft, Inc., a leading online game company based in Korea.

Analysis of Context Dependence in Social Interaction Networks of a Massively Multiplayer Online Role-Playing Game

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMORPGs), here we present a network science-based analysis of the interplay between distinct types of user interaction networks in the virtual world. We find that their properties depend critically on the nature of the context-interdependence of the interactions, highlighting the complex and multilayered nature of human interactions, a robust understanding of which we believe may prove instrumental in the designing of more realistic future virtual arenas as well as provide novel insights to the science of collective human behavior.

Chatting Pattern Based Game BOT Detection: Do They Talk Like Us?

Localization of sensor nodes is a key technology in Wireless Sensor Networks(WSNs). Trilateration is an important position determination strategy. To further improve the localization accuracy, a novel Trilateration based on Point In Triangle testing Localization (TPITL)algorithm is proposed in the paper. Unlike the traditional trilateration localization algorithm which randomly selects three neighbor anchors, the proposed TPITL algorithm selects three special neighbor anchors of the unknown node for trilateration. The three anchors construct the smallest anchor triangle which encloses the unknown node. To choose the optimized anchors, we propose Point In Triangle testing based on Distance(PITD) method, which applies the estimated distances for trilateration to reduce the PIT testing errors. Simulation results show that the PIT testing errors of PITD are much lower than Approximation PIT(APIT) method and the proposed TPITL algorithm significantly improves the localization accuracy.

The contagion of malicious behaviors in online games

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.

Modeling of bot usage diffusion across social networks in MMORPGs

User interactions in massively multiplayer online role-playing games (MMORPGs) generate social networks and diffuse user behavior throughout the network. We test the diffusion model in the adoption of a game bot among players connected via goal-oriented communities using real data provided by a major MMORPG company. In the model based on a probabilistic diffusion process, we used expectation maximization to infer the diffusion probability of game bot usage. The experimental results showed that the diffusion model can explain the spread of malicious behavior.

Multi-relational social networks in a large-scale MMORPG

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 worlds 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.

A Longitudinal Analysis of .i2p Leakage in the Public DNS Infrastructure

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.

Altruism in games: Helping others help themselves

Altruistic behavior is often considered the building block of a civic society. As more people interact with others in online environments, altruistic behaviors therein have become an interesting issue for analysis. In this paper, we investigate it in MMORPGs (Massively Multiplayer Online Role-Playing Games) that provide the most realistic, lifelike experiences. Specifically we look at the altruistic behaviors in Aion, an MMORPG serviced by NC Soft, Inc., of Korea. We analyze two types of behaviors, namely donation of cyber assets and guided co-play with lowerlevel users. We find that benefiting from altruistic behaviors can initiate pay-it-forward phenomena, and increases a players customer loyalty and satisfaction from the game.

A Statistical-Based Anomaly Detection Method for Connected Cars in Internet of Things Environment

A connected car is the most successful thing in the era of Internet of Things IoT. The connections between vehicles and networks grow and provide more convenience to users. However, vehicles become exposed to malicious attacks from outside. Therefore, a connected car now needs strong safeguard to protect malicious attacks that can cause security and safety problems at the same time. In this paper, we proposed a method to detect the anomalous status of vehicles. We extracted the in-vehicle traffic data from the well-known commercial car and performed the one-way ANOVA test. As a result, our statistical-based detection method can distinguish the abnormal status of the connected cars in IoT environment.

Does Query Blocking Improve DNS Privacy

DNS leakage happens when queries for names within a private namespace spread out to the public DNS infrastructure (Internet), which has various privacy implications. An example of this leakage includes the documented [1] leakage of .onion names associated with Tor hidden services to the public DNS infrastructure. To mitigate this leakage, and improve Tor’s privacy, Appelbaum and Muffet [2] proposed the special use .onion domain name, and various best practice recommendations of blocking of .onion strings (hidden service addresses) at the stub (browser), recursive, and authoritative resolvers. Without any form of analysis of those recommendations in practice, it is very difficult to tell how much of privacy is provided by following them in various deployment settings. In this paper, we initiate for the study of those recommendations by analyzing them under various settings and conclude that while the unlikely universal deployment will naturally improve privacy by preventing leakage, partial deployment, which is the case for early adoption, will degrade the privacy of individuals not adopting those recommendations.