Paul Irofti


About me: 
 Resume (RO)
 Publications
 Education
 Security Seminar
 ORCID
 Scholar
 LinkedIn
 GitHub

Grants: 
 DDNET
 Graphomaly
 NetAlert

Teaching: 
 Sisteme de Operare
 Utilizarea SO
 OS Security
 Vedere Artificială
 Static Analysis
 Procesarea Semnalelor
 Calcul Numeric

Contact: 
 [E-mail address]

 [PhotoID]

Associate Professor
The Research Center for Logic, Optimization and Security
Department of Computer Science
Faculty of Mathematics and Computer Science
University of Bucharest

I am interested in dictionary learning, signal processing, operating systems and security.

Every second Thursday I am organizing the Cyber-security seminar. The schedule and event details are here.

Currently teaching the OS courses Operating System Security, Operating Systems, Using Operating Systems, and also the Signal Processing and Static Analysis courses.


Researcher and Founding Member of
The Research Center for Logic, Optimization and Security (LOS)
University of Bucharest

NETALERT -- Automatic tools for detecting abnormal behavior in computer networks
SOL4 2021 project, September 2021 - May 2023

Goal: NetAlert aims to create a hardware-software sensor solution for detecting anomalies in computer networks based on the monitoring and analysis of data packets. The network-mounted sensor will provide real-time alerts on abnormal traffic behaviors using two complementary approaches:

(i) static analysis based on rules and behavioral patterns
(ii) machine learning (ML) analysis without prior expert knowledge

I am hiring, check the project page here!

DDNET -- Data Driven Fault Accommodation for Distribution Networks
PD 2019 project, September 2020 - August 2022

Adapt and propose new dictionary learning methods for solving untractable fault detection and isolation problems found in distribution networks. Given a large dataset of sensor measurements from the distribution network, the dictionary learning algorithms should be able to produce the subset of network nodes where faults exist.

Graphomaly -- software package for anomaly detection in graphs modeling financial transactions
PED 2019 project, September 2020 - April 2022

Create a Python software package for anomaly detection in graphs that model financial transactions, with the purpose of discovering fraudulent behavior like money laundering, illegal networks, tax evasion, scams, etc. Such a toolbox is necessary in banks, where fraud detection departments still use mostly human experts.

Vice Dean
Faculty of Mathematics and Computer Science
University of Bucharest

Vice Dean in charge of Research, IT, and Industry Relations.