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Cybersecurity Expert in Threat Research

Il y a 2 mois

Clichy, Île-de-France ENEA Temps plein
About ENEA Qosmos Division

ENEA Qosmos division is a leading provider of Deep Packet Inspection (DPI) technology, enabling real-time identification of data traveling over networks. Our DPI engine recognizes over 3800 protocols and extracts over 5000 metadata, providing a true picture of network traffic.

The Role

We are seeking a highly skilled Cybersecurity Expert in Threat Research & Detection to join our innovative R&D team. As a key member of our team, you will contribute to implementing our company's strategy in cybersecurity, focusing on threat research and detection.

Key Responsibilities:
  • Lead the setup and automation of a cybersecurity lab for malware and TTPs analysis, experimentation, and training purposes.
  • Lead malware analysis, extract malware IOCs and TTPs, and produce high-fidelity detection signatures.
  • Participate in research related to machine learning-based threat and anomaly detection focusing on network traffic.
  • Contribute to the design of new cybersecurity-oriented products and features on top of Qosmos NG DPI.
  • Educate, train, and provide guidance to R&D team members on key cybersecurity subjects.
  • Carry out experiments and prototyping to determine the feasibility of solutions to complex engineering problems.
  • Contribute to conversations on product strategy and direction.
What You Bring to the Team
  • Expertise and passion for threat research and detection, with a deep understanding of the cybersecurity threat landscape.
  • Ability to qualify and develop high-quality detection signatures based on analysis of malicious behavior.
  • Experience with systems such as IDPS, NDR, XDR, SIEM, or SOAR.
  • Familiarity with existing TTP frameworks (MITRE ATT&CK, Cyber Kill Chain).
  • Strong communication and collaboration skills.
  • Willingness to take initiative and challenge existing assumptions and conventions.
Preferred Qualifications
  • Experience in applying machine learning/deep learning techniques to detecting network threats or network anomalies.
  • Experience with one or more of the following programming languages: Python, C, Rust.