Wajih Ul Hassan

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whassan3obfuscate@illinois.edu

Wajih obtained his Ph.D. from the Computer Science Department at the University of Illinois at Urbana-Champaign under the advisement of Professor Adam Bates. His research interests are in secure data provenance, systems security, and distributed systems. During his time at Illinois, Wajih’s work was recognized with the Mavis Future Faculty Fellowship, Heidelberg Laureate Forum Young Researcher award, Symantec Graduate Fellowship and the Sohaib and Sara Abbasi Computer Science Fellowship. He also interned at Corelight, Symantec Research Labs, NEC Laboratories America, and the LABOS group at EPFL, Switzerland advised by Willy Zwaenepoel and Florin Dinu.

Wajih defended his dissertation, titled “Investigating System Intrusions with Data Provenance Analytics”, in summer 2021. Beginning in August 2022, Wajih will join the University of Virginia as an Assistant Professor of Computer Science and Data Science. Prior to this appointment, he will be serving as a visiting scientist at Stellar Cyber and visiting professor of computer science at LUMS.

Papers published with the STS Lab

SoK: History is a Vast Early Warning System: Auditing the Provenance of System Intrusions

FAuST: Striking a Bargain between Forensic Auditing's Security and Throughput

Forensic Analysis of Configuration-based Attacks

Validating the Integrity of Audit Logs Against Execution Repartitioning Attacks

This is Why We Can't Cache Nice Things: Lightning-Fast Threat Hunting using Suspicion-Based Hierarchical Storage

On the Forensic Validity of Approximated Audit Logs

Tactical Provenance Analysis for Endpoint Detection and Response Systems

OmegaLog: High-Fidelity Attack Investigation via Transparent Multi-layer Log Analysis

CUSTOS: Practical Tamper-Evident Auditing of Operating Systems Using Trusted Execution

Can Data Provenance Put an End to the Data Breach?

NoDoze: Combatting Threat Alert Fatigue with Automated Provenance Triage

Analysis of Privacy Protections in Fitness Tracking Social Networks -or- You can run, but can you hide?

Towards Scalable Cluster Auditing through Grammatical Inference over Provenance Graphs

Fear and Logging in the Internet of Things

Transparent Web Service Auditing via Network Provenance Functions