Post-doctoral Researcher in Cyberattack Data Generation

il y a 7 jours

Palaiseau, Île-de-France Institut Mines-Télécom Temps plein
About Institut Mines-Télécom

Institut Mines-Télécom is a leading graduate school for engineering, renowned for its excellence in digital technology. Our institution is committed to providing high-quality education, emphasizing project management, innovation, and intercultural understanding.

Our Mission

We aim to deploy AI-based attack detection and classification to facilitate the work of security analysts in Security Operation Centers (SOC) and Cyber Security Incident Response Teams (CSIRT). Our goal is to identify and prioritize malicious activities quickly, keeping pace with the rapidly evolving threat landscape.

Challenges in AI-Based Threat Detection

Despite the advancements in AI-based threat detection, several bottlenecks remain. These include insufficient coverage of attack behaviors, limited training data, and the inability to guarantee comprehensive coverage over different attack behaviors. The lack of coverage over different attack behaviors makes it challenging to accurately categorize threats.

Our Approach

We will leverage the attack knowledge and causal relationships between security incidents from a cybersecurity knowledge graph (CSKG) to create an AI-driven attack data generation method. Our approach involves developing a reinforcement learning-based attack behavior prediction policy model. This model will mimic cyberattack strategies used by human attackers/analysts and explore new attack methods based on contextual knowledge of target assets.

Human-AI Cooperative Process

We will establish a human-AI cooperative process to address the bottleneck of attack behavior exploration and prediction. This process will involve:

  • Exploring subgraph structures of CSKGs to unveil possible attack paths
  • Promoting human verification of suspicious behaviors unveiled by AI systems and concatenating human analysts' knowledge to guide attack exploration with active learning
  • Recovering missing entities in CSKG by adopting LLMs to estimate missed attack data or produce synthetic attack behaviors
Key Activities

The proposed work focuses on generating attack data by combining the CSKG produced in CKRISP and human analysts' knowledge from two perspectives:

  • Using pre-trained LLMs with the help of CSKG to produce attack data
  • Developing a language mapping between the actions supported by the reinforcement learning agent trained to explore the knowledge graphs and a penetration testing framework to generate attack payloads
Expected Outcomes

The contribution aims to build an AI agent to query an attack/malware knowledge graph, achieving attack behavior prediction/categorization. The agent may be trained using Reinforcement Learning or combined with an LLM for querying the attack knowledge graph. A GNN model can also be considered to generate soft prompt input to a LLM to achieve understanding/querying an attack knowledge graph.

Requirements

We are seeking a highly skilled Post-doctoral Researcher with expertise in machine-learning based cybersecurity, particularly intrusion detection. The ideal candidate will have knowledge in large language models, graph neural networks, or reinforcement learning, as well as experience in knowledge graphs. English written and spoken proficiency is required.

Application Procedure

To apply, please send us a CV, a cover letter, and a summary of your doctoral thesis. The positions offered for recruitment are open to all, with accommodations for candidates with disabilities available upon request.

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