Text-based cybersecurity attacks classification H/F

Engineering science

Contract

Internship

Job title

Text-based cybersecurity attacks classification H/F

Subject

The emergence of AI-generated cybersecurity attacks has paved the way for a new era of digital threats. AI-generated text-based cyber attacks represent a new breed of cyber threats where AI is used to create and execute different malicious activities (phishing, spear phishing, fake news, disinformation, social manipulation, etc). These attacks leverage text generation models to create convincing and contextually relevant textual content. The primary goal of these attacks is to deceive individuals, systems and even nations, leading to various harmful consequences. In this context, it becomes imperative to understand the threats brought by such attacks and develop innovative strategies to mitigate them. The aim of this internship consists in developing AI techniques to detect different types of text-based cyber attacks in general and AI-generated attacks in particular in order to equip network experts with precise tools for identifying patterns of misuse and malicious behaviors.1a

Contract duration (months)

6

Job description

Technically, the internship involves the fields of machine learning (ML) and natural language processing (NLP), and more specifically natural language generation (NLG) and classification techniques. In collaboration with CEA research engineers, the aim will be to train classification models capable of recognizing different types of text-based cyber attacks and distinguishing text-based attacks authored by humans from those generated by AI or by a specific generative model. This internship is meant to be an introduction to research, with the goal of publishing a scientific article if the obtained results are conclusive. The implemented models may  also be used to participate in a shared task like AuTexTification (https://sites.google.com/view/autextification/home) and CLIN33 (https://sites.google.com/view/shared-task-clin33/home) or in a challenge like MLMAC (https://mlmac.io/).

This work may be followed by a PhD in a broader context.

Applicant Profile

Engineering degree and/or Master 2 (M2) degree in computer science with a strong interest in artificial intelligence and natural language processing.

Required skills :

working environment : linux
knowledge of text classification techniques
background in natural language generation and language modeling
familiarity with pre-trained language models and large language models
Basic knowledge of the cybersecurity field
programming : Python + PyTorch/TensorFlow