Modeling Human Behavior from Low-Level Input Analytics

Transcription

1 Modeling Human Behavior from Low-Level Input Analytics Arpan Chakraborty Ph.D. Candidate David Roberts, Robert St. Amant, Titus Barik, Brent Harrison

2 Motivation How is human behavior related to security? What can low-level input analytics tell us? Bot or human? Alice or Ivan the impostor? Deceptive behavior

3 Existing Security Proofs Human Interactive Proofs (HIPs) Stop bots, spam Explicit, interruptive Human Observational Proofs (HOPs) Identify humans using biometric signatures Unobtrusive, but weak for behavioral analysis

4 Goal: Human Subtlety Proofs Passive observation of interactions Small changes to UI Cognitive models help recognize behavior Hard to deceive

5 Practical Applications Weed out bots from monetized games and social applications, including advertising Monitor user behavior for abnormal patterns within sensitive systems Identify deceptive behavior in online tests and interviews

6 Basis: Human Cognition Humans choose a cognitive strategy based on situations, conditions What order shall I proceed in? How much time should I spend on a task? Some decisions are made subconsciously

7 Microstrategies Ways of accomplishing a task that vary in timing, accuracy, payoff etc. Think lay up vs. slam dunk Affected by higher-level cognitive decisions Reflected in low-level motor behavior

8 Microstrategies When alternative microstrategies can be applied, users tend to select the one that is most efficient in the particular task context. [Gray & Boehm-Davis, 2000]

9 Method: Low-Level Input Analytics Mouse events Movement speed, click distribution Key presses Typing speed, inter-key and inter-word pauses Situation-specific interactions Correct actions, mistakes

10 Test Domain: Casual Games Rich interaction Goals and payoffs can be controlled Subtle changes possible Little distraction from target task Can be made part of the game!

11 I. Scrabble Can we tell bot vs. human from mouse behavior?

12 Spatial Signatures for Bot Detection Pixel-level signatures distinguish humans Click Unclick

13 II. Concentration Game Conditions Speed: Aim for less time Accuracy: Fewer mistakes Study 179 players, 10 games each Can we predict condition from player performance?

14 Visualizing Gameplay

15 Predicting Game Condition Speed Accuracy Results: 82.4% accuracy with SVM classifier

16 Speed-Accuracy Tradeoffs Can we identify different microstrategies people use under speed/accuracy conditions? Order of exploration Time spent in decision-making Speed of mouse movement Precision of clicks within tiles

17 Human Memory and Cognition What can we tell about human memory? Number of tiles one can remember How accurately are locations stored Duration one can remember a single fact Pattern of errors due to memory failures Cognitive model being developed

18 III. Ninja Typing

19 Typing Analytics Basic level: Type common dictionary words Then introduce subtle changes: Uncommon words Uncommon bigrams (e.g. ht ) Random letters (e.g. zhqv ) Observe inter-key delay, etc.

20 Studying Deceptive Behavior Words given to players before game starts Players try to act as if words are unknown Players incentivized for fooling system Can we identify deceptive behavior from low-level input analytics?

21 Summary Define low-level input metrics Identify microstrategies, tradeoffs Develop cognitive models of human behavior Recognize abnormal behavior to detect bots, deception