Mustaque Ahamad

About

Dr. Mustaque Ahamad is a professor in the School of Computer Science at the Georgia Institute of Technology, where he has served on the faculty since 1985. He was the director of the Georgia Tech Information Security Center (GTISC) from 2004 to 2012, during which he helped develop major research thrusts in security of converged communication networks, identity and access management, and healthcare information technology security. Currently, he leads Georgia Tech’s educational programs in cybersecurity as associate director of its Institute for Information Security and Privacy. His research interests include distributed systems, computer security, and dependable systems. Dr. Ahamad is also a co-founder and serves as chief scientist of Pindrop Security and FraudScope.

Research topics

• Computer Science
• Computer Security
• Data science
• Environmental health
• Geography
• Environmental science
• Ecology
• Environmental protection
• Medicine
• Biology

Selected publications

• An Inside Look into the Practice of Malware Analysis

  Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security · 2021 · 58 citations

  Senior authorCorresponding

  Malware analysis aims to understand how malicious software carries out actions necessary for a successful attack and identify the possible impacts of the attack. While there has been substantial research focused on malware analysis and it is an important tool for practitioners in industry, the overall malware analysis process used by practitioners has not been studied. As a result, an understanding of common malware analysis workflows and their goals is lacking. A better understanding of these w…

  PublisherDOI

• Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation

  2023-04-26 · 46 citations

  articleOpen access

  The spread of online misinformation threatens public health, democracy, and the broader society. While professional fact-checkers form the first line of defense by fact-checking popular false claims, they do not engage directly in conversations with misinformation spreaders. On the other hand, non-expert ordinary users act as eyes-on-the-ground who proactively counter misinformation – recent research has shown that 96% counter-misinformation responses are made by ordinary users. However, researc…

  PublisherOA PDFDOI

• The Role of the Crowd in Countering Misinformation: A Case Study of the COVID-19 Infodemic

  2020-12-10 · 33 citations

  preprintOpen access

  Fact checking by professionals is viewed as a vital defense in the fight against misinformation. While fact checking is important and its impact has been significant, fact checks could have limited visibility and may not reach the intended audience, such as those deeply embedded in polarized communities. Concerned citizens (i.e., the crowd), who are users of the platforms where misinformation appears, can play a crucial role in disseminating fact-checking information and in countering the spread…

  PublisherOA PDFDOI

• Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation

  arXiv (Cornell University) · 2023-03-11 · 18 citations

  preprintOpen access

  The spread of online misinformation threatens public health, democracy, and the broader society. While professional fact-checkers form the first line of defense by fact-checking popular false claims, they do not engage directly in conversations with misinformation spreaders. On the other hand, non-expert ordinary users act as eyes-on-the-ground who proactively counter misinformation -- recent research has shown that 96% counter-misinformation responses are made by ordinary users. However, resear…

  PublisherOA PDFDOI

• PETGEN

  2021-08-12 · 17 citations

  article

  What should a malicious user write next to fool a detection model? Identifying malicious users is critical to ensure the safety and integrity of internet platforms. Several deep learning based detection models have been created. However, malicious users can evade deep detection models by manipulating their behavior, rendering these models of little use. The vulnerability of such deep detection models against adversarial attacks is unknown. Here we create a novel adversarial attack model against…

  PublisherDOI

Recent grants

• ITR/SI: Guarding the Next Internet Frontier: Countering Denial of Information

  NSF · $2.0M · 2001–2008

• TWC: Medium: Collaborative: Exposing and Mitigating Cross-Channel Attacks that Exploit the Convergence of Telephony and the Internet

  NSF · $900k · 2015–2021

• TWC: Small: Securing the New Converged Telephony Landscape

  NSF · $500k · 2013–2017

Frequent coauthors

• Roberto Perdisci

  Georgia Institute of Technology

  23 shared

• Mostafa Ammar

  Georgia Institute of Technology

  21 shared

• Michel Raynal

  Institut de Recherche en Informatique et Systèmes Aléatoires

  17 shared

• Payas Gupta

  14 shared

• Sumeer Bhola

  Google (United States)

  13 shared

• Francisco J. Torres-Rojas

  Instituto Tecnológico de Costa Rica

  10 shared

• Manos Antonakakis

  Georgia Institute of Technology

  10 shared

• Karsten Schwan

  Delft University of Technology

  10 shared

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