Movement pattern based authentication for smart mobile devices - Rahman, Khandaker Abir; Tubbs, Dustyn J.; Hossain, Md Shafaeat (2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018)
We outline a novel method of user authentication for smart mobile devices, such as smartphones or tablets and propose movement pattern based authentication as an alternate to current methods that relies on a pin or drawn-pattern. While the current methods are vulnerable against common attacks (e.g., smudge attacks, shoulder surfing), our method, in contrast, is more resilient against the attacks of these kinds because it utilizes sensory data given off by the device during a preset movement for authentication. In our experiment, we recorded the values given off by four physical observational sensors: (1) accelerometer, (2) linear accelerometer, (3) gyroscope and (4) tilt sensor, which each had three axes, over a set of movements. We experimented with 10 arbitrary movement-patterns and gathered 12 samples of each (net 120 samples) to test with. We developed our own method of authentication, through which we performed 35,650 authentication attempts and found a 20.36% Equal Error Rate.
Keywords: Accelerometers; Authentication; Equal error rate; Gyroscopes; Linear accelerometers; Machine learning; Mobile computing; Mobile device; Movement pattern; Screen unlock; Sensory data; Shoulder surfing; Smart phones; Three axes; Tilt sensor; Training; User authentication; accelerometers; authentication; authorisation; dept.csc; gyroscope; gyroscopes; linear accelerometer; message authentication; mobile computing; mobile device; movement pattern; movement pattern based authentication; movement pattern, authentication, screen unlock, mobile device; physical observational sensors; preset movement; screen unlock; smart mobile devices; smart phones; smudge attacks; tilt sensor; user authentication

A game theoretic analysis of the twitter follow-unfollow mechanism - Chen, Jundong; Hossain, Md Shafaeat; Brust, Matthias R.; Johnson, Naomi (Springer International Publishing, 2018)
Twitter users often crave more followers to increase their social popularity. While a variety of factors have been shown to attract the followers, very little work has been done to analyze the mechanism how Twitter users follow or unfollow each other. In this paper, we apply game theory to modeling the follow-unfollow mechanism on Twitter. We first present a two-player game which is based on the Prisoner’s Dilemma, and subsequently evaluate the payoffs when the two players adopt different strategies. To allow two players to play multiple rounds of the game, we propose a multi-stage game model. We design a Twitter bot analyzer which follows or unfollows other Twitter users by adopting the strategies from the multi-stage game. We develop an algorithm which enables the Twitter bot analyzer to automatically collect and analyze the data. The results from analyzing the data collected in our experiment show that the follow-back ratios for both of the Twitter bots are very low, which are 0.76%0.76%0.76% and 0.86%0.86%0.86%. This means that most of the Twitter users do not cooperate and only want to be followed instead of following others. Our results also exhibit the effect of different strategies on the follow-back followers and on the non-following followers as well.
Keywords: Decision theory; Game theoretic analysis; Game theory; Learning systems; Machine learning; Multi stage games; Social network; Social networking (online); Twitter bot; Twitter classification; Two-player games; dept.csc