Phd Position F/m Constructing Cyber Security
il y a 1 semaine
Le descriptif de l’offre ci-dessous est en Anglais_
**Type de contrat **:CDD
**Niveau de diplôme exigé **:Bac + 5 ou équivalent
**Fonction **:Doctorant
**A propos du centre ou de la direction fonctionnelle**:
The Inria Centre at Rennes University is one of Inria's eight centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.
**Contexte et atouts du poste**:
Within the framework of the ANR project "CKRISP".
**Mission confiée**:
**Background**
AI-based cyber-attack detection models face several bottlenecks that need to be addressed. First, AI models require large amounts of training data to cover as many as possible attack behaviours. However, in the practice of cybersecurity, the deployed probes cannot guarantee to provide comprehensive coverage over different attack behaviours, especially emerging new attacks. The lack of coverage over different attack behaviours, i.e. the issue of out-of-distribution samples in attack detection [Yang21], makes it challenging to accurately categorise the threats that are being faced. Second, AI models may generate a large number of false positives that are difficult to distinguish from real intrusion. The origin of this issue stems from the lack of interpretability of AI models with complex architectures, e.g., Deep Neural Networks. The difficulty of comprehending the detection logic of AI models leads to mistrust and unaccountability of AI-based detection technologies. In light of the aforementioned limitations of AI-based attack detection, we believe representing cyber security knowledge, e.g. encoding and inferring attack behaviours with AI models is the key to deliver verifiable and accountable AI-based cyberattack prediction system.
**The objective of this thesis**
Over the constructed knowledge graphs, we propose to perform the causal-reasoning-based attack prediction via reinforcement learning (RL)-based exploration in cyber security knowledge graphs. We further integrate human analysts' feedback to refine the reasoning capability of the RL-based exploration models. To achieve this goal, we plan to develop few-shot learning methods [Hejna22], such as few-shot preference learning with human feedback to tailor the exploration policy learnt using knowledge graphs. Furthermore, we will focus on continual learning [Zhangxk22] to update dynamically the contents of the cyber security knowledge graphs. Similar as active learning, this algorithm also allows human experts to encode the verified and corrected causal relations as additional supervision to update links/entities in the knowledge graphs.
**Expectations**
**References**
[Yang21] Yang, L.M, Guo, W.B., Hao, Q.Y., et al., CADE: Detecting and Explaining Concept Drift Samples for Security Applications, In 30th Usenix Security, pp.2327-2344, 2021.
[Rastogi20] Rastogi, N., Dutta, S., Zaki, M.J., MalOnt: An ontology for malware threat intelligence, International Workshop on Deployable Machine Learning for Security Defense, pp. 28-44, 2020.
[Das22] Das, S.S., Dutta, A., Purohit, S., Serra, E., Halappanavar, M., Pothen, A, Towards Automatic Mapping of Vulnerabilities to Attack Patterns using Large Language Models. In IEEE International Symposium on Technologies for Homeland Security, pp. 1-7, 2022.
[Hejna22] Hejna, J. and Sadigh, D., Few-Shot Preference Learning for Human-in-the-loop RL, In Annual Conference on Robot Learning, 2022.
[Zhangxk22] Zhang, X.K. et al., CGLB: Benchmark Tasks for Continual Graph Learning, In NeurIPS 2022, 2022.
**Principales activités**:
**Compétences**:
Technical skills and level required : Machine Learning, Statistics, Information theory, Pytorch, Intrusion Detection
Languages : English
**Avantages**:
- Subsidized meals
- Partial reimbursement of public transport costs
- Possibility of teleworking (90 days per year) and flexible organization of working hours
- Partial payment of insurance costs
**Rémunération**:
Monthly gross salary amounting to 2100 euros for the first and second years and 2200 euros for the third year
**Informations générales**:
- **Thème/Domaine**: Représentation et traitement des données et des connaissances
Statistiques (Big data) (BAP E)
- **Ville**: Rennes
- **Centre Inria**: Centre Inria de l'Université de Rennes
- **Date de prise de fonction souhaitée**: 2024-06-01
- **Durée de contrat**: 3 ans
- **Date limite pour postuler**: 2024-04-01
**Consignes pour postuler**:
Please submit online : your resume, cover letter and letters of recommendation eventually
**Sécurité défense**:
Ce poste est susceptible d’être affecté dans une zone à régime restrictif (ZRR), telle que définie dans le décret n°2011-1425 relatif
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