Ciberseguridad à United States

Organisation / Company: UNIVERSIDAD POLITECNICA DE MADRID

Department: HRS4R

Researcher Profile: First Stage Researcher (R1)

Positions: Bachelor Positions

Country: Spain

Application Deadline: 30 Jan 2025 - 20:00 (Europe / Madrid)

Type of Contract: Temporary

Job Status: Full-time

Offer Starting Date: 15 Feb 2025

Is the job funded through the EU Research Framework Programme? Not funded by a EU programme

Is the Job related to staff position within a Research Infrastructure? No

Offer Description

• Development of artificial intelligence models based on user activities, taking into account log collection agents from systems (Windows / UNIX / MacOS).
• Parameterization of INSIDER cybersecurity attacks based on the MITRE ATT&CK matrix, where Techniques, Tactics, and Procedures must be identified.
• Modeling user activity to identify potential INSIDER threats.
• Development of user session modeling, using clustering algorithms based on unsupervised learning.
• Integration of the developed models into a complex system that allows characterizing INSIDER threats through real-time learning techniques (online learning) and assessing the system's risk based on the previously defined characteristics.

Where to apply

E-mail: Research Field

Minimum Qualifications

Bachelor Degree or equivalent

Skills / Qualifications

• Expertise in developing models using both supervised and unsupervised learning, particularly with clustering algorithms for user activity modeling.
• Strong understanding of INSIDER threats, with experience in using the MITRE ATT&CK matrix to identify Techniques, Tactics, and Procedures (TTPs).
• Ability to work with system logs from platforms like Windows and UNIX / MacOS, extracting meaningful insights to track user behavior.
• Proficiency in languages such as Python, Java, and scripting tools (e.g., Bash, PowerShell) for system interaction and model development.
• Skill in applying anomaly detection algorithms to recognize unusual patterns that may signal insider threats.
• Experience with online learning techniques to enable the integration of models into systems that assess risks and evolve in real-time.

Specific Requirements

• Experience with real-time data processing systems, such as Kafka, Spark Streaming, or Airflow.
• Experience in developing anomaly detection models based on behavioral analysis.
• Familiarity with tools such as ELK Stack (Elasticsearch, Logstash, and Kibana).
• Proficiency with the MITRE ATT&CK framework and the ability to map threats to Techniques, Tactics, and Procedures (TTPs).
• Familiarity with unsupervised learning methods and experience with real-time learning (online learning).
• Experience with Python and popular AI / ML libraries such as TensorFlow, Scikit-learn, and Keras.

Languages

Additional Information

Training courses and attendance at conferences.