Acar Tamersoy

In Proceedings of the 2022 Conference on Human Factors in Computing Systems (CHI 2022)

In Proceedings of the 37th Annual Computer Security Applications Conference (ACSAC 2021)

In Proceedings of the 30th USENIX Security Symposium (USENIX Security 2021)

In Proceedings of the 2020 CHI Workshop on Designing Interactions for the Ageing Populations - Addressing Global Challenges
Older adults are disproportionately affected by scams, many of which target them specifically. We present Fraud Bingo, an intervention designed by WISE \& Healthy Aging Center in Southern California prior to 2012, that has been played by older adults throughout the United States. We also present the Scam Defender Obstacle Course (SDOC), an interactive web application that tests a user's ability to identify scams, and subsequently teaches them how to recognize the scams. SDOC is patterned after existing phishing-recognition training tools for working professionals. We present the results of running a workshop with 17 senior citizens, where we performed a controlled study that and used SDOC to measure the effectiveness of Fraud Bingo. We outline the difficulties several participants had with completing SDOC, which indicate that tools like SDOC should be tailored to the needs of older adults.

In Proceedings of the 41st IEEE Symposium on Security and Privacy (S&P 2020)
Technology increasingly facilitates interpersonal attacks such as stalking, abuse, and other forms of harassment. While prior studies have examined the ecosystem of software designed for stalking, our study uncovers a larger landscape of apps---what we call creepware---used for interpersonal attacks. We discover and report on apps used for harassment, impersonation, fraud, information theft, concealment, hacking, and other attacks, as well as creative defensive apps that victims use to protect themselves.

In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ‘14)
We present AESOP, a scalable algorithm that identifies malicious executable files by leveraging a novel combination of locality-sensitive hashing and belief propagation. AESOP attained early labeling of 99% of benign files and 79% of malicious files with a 0.9961 true positive rate at 0.0001 false positive rate.