Mohamed Nabeel
Research Scientist email:
mnabeel [at] hbku [dot] edu [dot] qa
office:
Room RC-B1-1191
HBKU Reserch Complex
Research
Machine Learning for Cybersecurity
Detecting malicious domains (phishing, spam, command and control, etc.) by crunching large amounts of network graph data.
Adversarial Machine Learning for Cybersecurity
Adversarial attacks against the ML models trained in the cybersecurity domains and defenses.
Encrypted Query Processing
Processing queries over encrypted data in different encrypted database models: relational, time-series, graph and NoSQL in general.
Privacy Preserving Machine Learning
Making maching learning models privacy preserving (differential privacy, homomorphic encryption, SMC, federated learning)
Recent News
Feb, 2021
Our paper accepted at USENIX Security 2021
Our paper titled "Compromised or Attacker-Owned: A Large Scale Classification and Study of Hosting Domains of Malicious URLs" is to appear in USENIX 2021.
June, 2020
Our paper accepted at ACM TOPS 2020
Our paper titled "Following Passive DNS Traces to Detect Stealthy Malicious Domains Via Graph Inference" is to appear in ACM TOPS 2020.
May, 2020
Real-time COVID-19 squatting domain detection was selected as the best national product
Our web based system for investigating covid-19 squatting domains powered a real-time detection backend was published in April 2020 and it was selected as the best national product in May 2020.
June, 2020
Our paper accepted at ACM TOPS 2020
Our paper titled "Following Passive DNS Traces to Detect Stealthy Malicious Domains Via Graph Inference" is to appear in ACM TOPS 2020.
April, 2018
Our paper accepted at ACM SACMAT 2018
Our paper titled "Securing Named Data Networks: Challenges and the Way Forward" is to appear in ACM SACMAT 2018. It will be held in June in Indianapolis, USA.
April, 2018
QNRF-TUBITAK grant awarded
The joint proposal of QCRI and UITSEC (Turkey) was awarded the QNRF-TUBITAK grant worth over USD 1.5. The proposal is about building a cyber threat intelligence platform.
March, 2018
Himaya-Domains prototype v0.1
Presented our initial prototype of Himaya-Domains, a domain threat visualization portal.
March, 2018
Best paper award at ACM CODASPY 2018
Our paper titled "A Domain is only as Good as its Buddies: Detecting Stealthy Malicious Domains via Graph Inference" received the best paper award at ACM CODASPY 2018. We propose new techniques to detect malicious domains via graph inference.