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Intrusion detection using spatial information and behavioral biometrics.

機譯：使用空間信息和行為生物識別技術進行入侵檢測。

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Multiplayer online computer games are quickly growing in popularity with millions of players logging in every day. While most play in accordance with the rules set up by the game designers, some choose to cheat, to gain an unfair advantage over other players. With the growth in the economic and social importance of the virtual game worlds incidence of cheating are becoming increasingly problematic. Cheating by some players makes the game less appealing for the honest players. As a result, it costs thousands of dollars to game designers in lost revenue from disillusioned players who stop participating and in man-hours used for prevention of different forms of cheating. Consequently, a great deal of current research in computer science is aimed at detecting, preventing and neutralizing cheating in game worlds.;As game networks become a multibillion dollar industry it is necessary to protect them from actions that attempt to compromise the reliability, confidentiality or availability of such systems and to prevent intrusions of computer systems in general. In the context of information systems, intrusion refers to any unauthorized access, not permitted attempt to access or damage, or malicious use of information resources. Intrusion detection is defined as detection of break-ins and break-in attempts via automated software system.;In particular, behavior based intrusion detection methods are frequently used for insuring network security. Atypical user activities are automatically detected and alerts are forwarded to a network administrator. Recently, with the increase in online gaming, network intrusion detection techniques are being considered for identifying imposters playing from remote sites. The features commonly used in network intrusion detection (such as patterns of opening files, saving files, mouse clicks, memory requirements, etc.) are not sufficiently discriminative in applications where the number of registered users is large and we are not only interested in detecting whether an intrusion has occurred but in also identifying the impostor from among the set of registered users (players).;Research in biometric technologies offers one of the most promising approaches to providing user friendly and reliable control methodology for access to computer systems, networks and workplaces. Majority of such research is aimed at studying well established physical biometrics such as fingerprint or iris recognition. Behavioral biometrics are usually only briefly mentioned and only those which are in large part based on muscle control such as keystrokes, gait or signature are well investigated.;Behavioral biometrics provide a number of advantages over traditional biometric technologies. They can be collected non-obtrusively or even without the knowledge of the user. Collection of behavioral data often does not require any special hardware and is so very cost effective. While behavioral biometrics are not unique enough to provide reliable human identification they have been shown to provide sufficiently high accuracy identity verification.;This dissertation begins with a review of published research in game security and behavioral biometrics. We analyze previous studies and point out trends and propose taxonomies which make understanding and improvement on previous work easier. As the capstone of this dissertation, we have developed an intrusion detection system for online poker which uses player's game strategy as the behavioral profile. We have improved our system by experimenting with different similarity measure functions and different ways of representing behavioral signatures. We were able to greatly improve performance of our system by inclusion of spatial, temporal and contextual information about the environment alongside the user's behavior. We have investigated possibility of creation of artificial behavioral profiles and use of such profiles for spoofing of behavior-based security systems. As our research progressed new interesting and unforeseen research paths were discovered. We have expanded strategy-based behavioral biometrics to a new domain of recognition and verification of intelligent agents and had created a novel CAPTCHA-based algorithm aimed at preventing intelligent agents from participating in online poker games.
機譯：多人在線計算機游戲迅速流行，每天都有數(shù)百萬玩家登錄。盡管大多數(shù)游戲都是按照游戲設計師制定的規(guī)則進行的，但有些游戲還是選擇作弊，以獲得相對于其他玩家的不公平優(yōu)勢。隨著虛擬游戲的經(jīng)濟和社會重要性的增長，世界上作弊的問題變得越來越成問題。一些玩家的作弊行為會降低游戲對誠實玩家的吸引力。結果，它使游戲設計師損失了數(shù)千美元，這是因為幻滅了的玩家停止了游戲，并在防止不同形式的作弊上花費了工時。因此，當前在計算機科學領域的大量研究旨在檢測，預防和消除游戲世界中的作弊行為。隨著游戲網(wǎng)絡成為價值數(shù)十億美元的產(chǎn)業(yè)，有必要保護它們免受試圖破壞可靠性，機密性或破壞性的行為。此類系統(tǒng)的可用性，并通常防止計算機系統(tǒng)的入侵。在信息系統(tǒng)的上下文中，入侵是指任何未經(jīng)授權的訪問，未經(jīng)允許的訪問或破壞嘗試或對信息資源的惡意使用。入侵檢測被定義為通過自動化軟件系統(tǒng)檢測闖入和闖入企圖。尤其是，基于行為的入侵檢測方法經(jīng)常用于確保網(wǎng)絡安全。系統(tǒng)會自動檢測到非典型用戶活動，并將警報轉發(fā)給網(wǎng)絡管理員。近來，隨著在線游戲的增加，正在考慮使用網(wǎng)絡入侵檢測技術來識別從遠程站點玩的冒名頂替者。網(wǎng)絡入侵檢測中常用的功能（例如打開文件的模式，保存文件，單擊鼠標，內(nèi)存需求等）在注冊用戶數(shù)量很大的應用中并不能充分區(qū)分，我們不僅對檢測有興趣生物入侵技術的研究提供了一種最有前途的方法，可為用戶提供對計算機系統(tǒng)，網(wǎng)絡和計算機的訪問的用戶友好和可靠的控制方法，這是最有希望的方法之一。工作場所。這類研究的大多數(shù)目的是研究完善的物理生物識別技術，例如指紋或虹膜識別。通常僅簡要提及行為生物特征，并且僅對很大程度上基于肌肉控制的行為生物特征（例如擊鍵，步態(tài)或簽名）進行深入研究。行為生物特征提供了許多優(yōu)于傳統(tǒng)生物特征技術的優(yōu)勢?？梢圆患痈蓴_地收集它們，甚至在用戶不知情的情況下也可以收集它們。收集行為數(shù)據(jù)通常不需要任何特殊硬件，因此非常具有成本效益。盡管行為生物識別技術不足以提供可靠的人類身份識別，但它們已被證明可以提供足夠高的準確性身份驗證。;本文首先回顧了已發(fā)表的有關游戲安全性和行為生物識別技術的研究。我們分析先前的研究并指出趨勢，并提出分類法，以使對先前工作的理解和改進更加容易。作為本文的重點，我們開發(fā)了一種在線撲克入侵檢測系統(tǒng)，該系統(tǒng)使用玩家的游戲策略作為行為特征。我們通過嘗試使用不同的相似性度量功能和表示行為簽名的不同方式來改進我們的系統(tǒng)。通過包含關于環(huán)境以及用戶行為的空間，時間和上下文信息，我們能夠極大地提高系統(tǒng)的性能。我們調(diào)查了創(chuàng)建人工行為配置文件以及將此類配置文件用于基于行為的安全系統(tǒng)的欺騙的可能性。隨著我們研究的進展，發(fā)現(xiàn)了新的有趣且不可預見的研究途徑。我們已經(jīng)將基于策略的行為生物識別技術擴展到智能代理識別和驗證的新領域，并創(chuàng)建了一種新穎的基于CAPTCHA的算法，旨在防止智能代理參與在線撲克游戲。

著錄項

  • 作者

    Yampolskiy, Roman V.;

  • 作者單位

    State University of New York at Buffalo.;

  • 授予單位 State University of New York at Buffalo.;
  • 學科 Artificial Intelligence.;Computer Science.
  • 學位 Ph.D.
  • 年度 2008
  • 頁碼 258 p.
  • 總頁數(shù) 258
  • 原文格式 PDF
  • 正文語種 eng
  • 中圖分類 人工智能理論;自動化技術、計算機技術;
  • 關鍵詞

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