An Adversarial Risk Analysis Framework for Batch Acceptance Problems

Published Online:https://doi.org/10.1287/deca.2020.0420

References

  • Banks DL, Ríos J, Ríos Insua D (2015) Adversarial Risk Analysis (CRC Press, Boca Raton, FL).CrossrefGoogle Scholar
  • Boros E, Fedzhora L, Kantor PB, Saeger K, Stroud P (2009) A large-scale linear programming model for finding optimal container inspection strategies. Naval Res. Logist. 56(5):404–420.CrossrefGoogle Scholar
  • Caflisch RE (1998) Monte Carlo and quasi-Monte Carlo methods. Acta Numerica 7:1–49.CrossrefGoogle Scholar
  • Chaloner KM, Duncan GT (1983) Assessment of a beta prior distribution: PM elicitation. J. Roy. Statist. Soc. Ser. D Statistician 32(1–2):174–180.Google Scholar
  • Chung KL (2001) A Course in Probability Theory (Academic Press, San Diego).Google Scholar
  • Cooke RM (1991) Experts in Uncertainty: Opinion and Subjective Probability in Science (Oxford University Press, New York).CrossrefGoogle Scholar
  • Cormack GV, Lynam TR (2007) Online supervised spam filter evaluation. ACM Trans. Inform. Systems 25(3):Article 11-es.CrossrefGoogle Scholar
  • Dorazio RM (2009) On selecting a prior for the precision parameter of Dirichlet process mixture models. J. Statist. Planning Inference 139(9):3384–3390.CrossrefGoogle Scholar
  • Dreiding RA, McLay LA (2013) An integrated model for screening cargo containers. Eur. J. Oper. Res. 230(1):181–189.CrossrefGoogle Scholar
  • Duan Z, Chen P, Sanchez F, Dong Y, Stephenson M, Baker JM (2012) Detecting spam zombies by monitoring outgoing messages. IEEE Trans. Dependable Secure Comput. 9(2):198–210.CrossrefGoogle Scholar
  • Ferguson TS (1973) A Bayesian analysis of some nonparametric problems. Ann. Statist. 1(2):209–230.CrossrefGoogle Scholar
  • French S, Ríos Insua D (2000) Kendall’s Library of Statistics 9: Statistical Decision Theory (Wiley, New York).Google Scholar
  • Gaukler GM, Li C, Ding Y, Chirayath SS (2012) Detecting nuclear materials smuggling: Performance evaluation of container inspection policies. Risk Anal. 32(3):531–554.CrossrefGoogle Scholar
  • González-Ortega J, Ríos Insua D, Ruggeri F, Soyer R (2019) Adversarial hypothesis testing in presence of adversaries. Amer. Statistician, ePub ahead of print July 10, https://doi.org/10.1080/00031305.2019.1630001.Google Scholar
  • Haphuriwat N, Bier VM, Willis HH (2011) Deterring the smuggling of nuclear weapons in container freight through detection and retaliation. Decision Anal. 8(2):88–102.LinkGoogle Scholar
  • Hargreaves-Heap SP, Varoufakis Y (2004) Game Theory: A Critical Introduction (Routledge, New York).CrossrefGoogle Scholar
  • Hausken K, Bier VM (2011) Defending against multiple different attackers. Eur. J. Oper. Res. 211(2):370–384.CrossrefGoogle Scholar
  • Hausken K, Levitin G (2012) Review of systems defense and attack models. Internat. J. Performability Engrg. 8(4):355–366.CrossrefGoogle Scholar
  • Hausken K, Zhuang J (2011) Governments’ and terrorists’ defense and attack in a T-period game. Decision Anal. 8(1):46–70.LinkGoogle Scholar
  • Heydari A, Tavakoli M, Salim N, Heydari Z (2015) Detection of review spam: A survey. Expert Systems Appl. 42(7):3634–3642.CrossrefGoogle Scholar
  • Hooi B, Shah N, Beutel A, Günnemann S, Akoglu L, Kumar M, Makhija D, Faloutsos C (2016) BIRDNEST: Bayesian inference for ratings-fraud detection. Venkatasubramanian SC, Meira W, eds. Proc. 16th SIAM Internat. Conf. Data Mining (Society for Industrial and Applied Mathematics, Philadelphia), 495–503.Google Scholar
  • Jagielski M, Oprea A, Biggio B, Liu C, Nita-Rotaru C, Li B (2018) Manipulating machine learning: Poisoning attacks and countermeasures for regression learning. Parno B, Kruegel C, eds. Proc. 39th IEEE Symp. Security Privacy (Institute of Electrical and Electronics Engineers Computer Society, Los Alamitos, CA), 19–35.Google Scholar
  • Jiang AX, Procaccia AD, Qian Y, Shah N, Tambe M (2013) Defender (mis) coordination in security games. Rossi F, ed. Proc. 23rd Internat. Joint Conf. Artificial Intelligence (Association for the Advancement of Artificial Intelligence Press, Palo Alto, CA), 220–226.Google Scholar
  • Jindal N, Liu B (2008) Opinion spam and analysis. Broder A, Chakrabarti S, eds. Proc. 2008 Internat. Conf. Web Search and Data Mining (Association for Computing Machinery, New York), 219–230.Google Scholar
  • Kantarcioglu M, Xi B, Clifton C (2011) Classifier evaluation and attribute selection against active adversaries. Data Mining Knowledge Discovery 22(1–2):291–335.CrossrefGoogle Scholar
  • Lau RYK, Liao SY, Kwok RCW, Xu K, Xia Y, Li Y (2012) Text mining and probabilistic language modeling for online review spam detection. ACM Trans. Management Inform. Systems 2(4):Article 25-es.Google Scholar
  • Li FH, Huang M, Yang Y, Zhu X (2011) Learning to identify review spam. Walsh T, ed. Proc. 22nd Internat. Joint Conf. Artificial Intelligence (Association for the Advancement of Artificial Intelligence Press, Palo Alto, CA), 2488–2493.Google Scholar
  • Lim EP, Nguyen VA, Jindal N, Liu B, Lauw HW (2010) Detecting product review spammers using rating behaviors. Koudas N, Jones G, Wu X, Collins-Thompson K, An A, eds. Proc. 19th ACM Internat. Conf. Inform. Knowledge Management (Association for Computing Machinery, New York), 939–948.Google Scholar
  • Lindley DV, Singpurwalla ND (1991) On the evidence needed to reach agreed action between adversaries, with application to acceptance sampling. J. Amer. Statist. Assoc. 86(416):933–937.CrossrefGoogle Scholar
  • Luckner M, Gad M, Sobkowiak P (2014) Stable web spam detection using features based on lexical items. Comput. Security 46(10):79–93.CrossrefGoogle Scholar
  • McLay LA, Rothschild C, Guikema S (2012) Robust adversarial risk analysis: A level-k approach. Decision Anal. 9(1):41–54.LinkGoogle Scholar
  • Merrick JRW, Albert LA (2018) Expert judgment based nuclear threat assessment for vessels arriving in the US. Dias LC, Morton A, Quigley J, eds. Elicitation: The Science and Art of Structuring Judgement (Springer, Cham, Switzerland), 495–509.CrossrefGoogle Scholar
  • Merrick JRW, Parnell GS (2011) A comparative analysis of PRA and intelligent adversary methods for counterterrorism risk management. Risk Anal. 31(9):1488–1510.CrossrefGoogle Scholar
  • O’Hagan A, Buck CE, Daneshkhah A, Eiser JR, Garthwaite PH, Jenkinson DJ, Oakley JE, Rakow T (2006) Uncertain Judgements: Eliciting Experts’ Probabilities (Wiley, Chichester, UK).CrossrefGoogle Scholar
  • Ramilli M, Prandini M (2009) Comment spam injection made easy. Buford JF, ed. Proc. 6th IEEE Consumer Comm. Network Conf. (Institute of Electrical and Electronics Engineers Computer Society, Red Hook, NY), 1–5.Google Scholar
  • Ríos J, Ríos Insua D (2012) Adversarial risk analysis for counterterrorism modeling. Risk Anal. 32(5):894–915.CrossrefGoogle Scholar
  • Ríos Insua D, Ríos J, Banks DL (2009) Adversarial risk analysis. J. Amer. Statist. Assoc. 104(486):841–854.CrossrefGoogle Scholar
  • Ríos Insua D, Banks DL, Ríos J, González-Ortega J (2021) Adversarial risk analysis as a structured expert judgement decomposition tool. French S, Nane T, Hanea A, Bedford T, eds. Expert Judgement in Risk and Decision Analysis (Springer, Cham, Switzerland). Forthcoming.Google Scholar
  • Robert CP, Casella G (2013) Monte Carlo Statistical Methods (Springer, New York).Google Scholar
  • Ruan X, Wu Z, Wang H, Jajodia S (2015) Profiling online social behaviors for compromised account detection. IEEE Trans. Inform. Forensics Security 11(1):176–187.CrossrefGoogle Scholar
  • Schilling EG, Neubauer DV (2009) Acceptance Sampling in Quality Control (CRC Press, Boca Raton, FL).Google Scholar
  • Schlenker A, Brown M, Sinha A, Tambe M, Mehta R (2016) Get me to my GATE on time: Efficiently solving general-sum Bayesian threat screening games. Kaminka GA, Fox M, Bouquet P, Hüllermeier E, Dignum V, eds. Proc. 22nd Eur. Conf. Artificial Intelligence (IOS Press, Amsterdam), 1476–1484.Google Scholar
  • Sculley D, Wachman GM (2007) Relaxed online SVMs for spam filtering. Clarke CLA, Fuhr N, Kando N, eds. Proc. 30th Annual Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (Association for Computing Machinery, New York), 415–422.Google Scholar
  • Shachter RD (1986) Evaluating influence diagrams. Oper. Res. 34(6):871–882.LinkGoogle Scholar
  • Shapiro D, Shi X, Zillante A (2014) Level-k reasoning in a generalized beauty contest. Games Econom. Behav. 86(7):308–329.CrossrefGoogle Scholar
  • Stidham S (1985) Optimal control of admission to a queueing system. IEEE Trans. Automatic Control 30(8):705–713.CrossrefGoogle Scholar
  • Tapiero CS (1995) Acceptance sampling in a producer-supplier conflicting environment: Risk neutral case. Appl. Stochastic Models Bus. Indust. 11(1):3–12.CrossrefGoogle Scholar
  • Tartakovsky AG, Nikiforov IV, Basseville M (2014) Sequential Analysis: Hypothesis Testing and Changepoint Detection (CRC Press, Boca Raton, FL).CrossrefGoogle Scholar
  • Tondi B, Merhav N, Barni M (2019) Detection games under fully active adversaries. Entropy 21(1):Article 23-es.CrossrefGoogle Scholar
  • Xie S, Wang G, Lin S, Yu PS (2012) Review spam detection via temporal pattern discovery. Agarwal D, Pei J, eds. Proc. 18th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 823–831.Google Scholar
  • Ye N, Newman C, Farley T (2006) A system-fault-risk framework for cyber attack classification. Inform. Knowledge Systems Management 5(2):135–151.Google Scholar
  • Young HP (2004) Strategic Learning and Its Limits (Oxford University Press, Oxford, NY).CrossrefGoogle Scholar
  • Yu S, Vorobeychik Y, Alfeld S (2018) Adversarial classification on social networks. Dastani M, Sukthankar G, eds. Proc. 17th Internat. Conf. Autonomous Agents Multiagent Systems (International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC), 211–219.Google Scholar
  • Zhang Y, Wang S, Phillips P, Ji G (2014) Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowledge-Based Systems 64(7):22–31.CrossrefGoogle Scholar
  • Zhuang J, Bier VM, Alagoz O (2010) Modeling secrecy and deception in a multiple-period attacker-defender signaling game. Eur. J. Oper. Res. 203(2):409–418.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.