Computer Science

Threat hunting: proactively hunting for cybersecurity threats

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In a constantly changing digital universe, where cyber threats evolve in complexity and stealth, threat hunting emerges as an essential strategy. Beyond traditional defensive mechanisms, often reactive, this proactive practice allows for anticipating, identifying, and neutralizing sophisticated attacks before they inflict irreversible damage. By analyzing abnormal behaviors and leveraging subtle indicators, cybersecurity teams are reinventing … Read more

Security Operations Center (SOC): ensure real-time monitoring

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In a context where cyber threats are constantly becoming more sophisticated and where the volume of data to be protected is increasing exponentially, the Security Operations Center (SOC) emerges as an indispensable component for ensuring the security of digital infrastructures. The SOC is much more than just a monitoring center: it is the very heart … Read more

Penetration testing: effectively assess the security of your systems

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Faced with the exponential increase of cyber threats in 2025, ensuring IT security becomes an absolute priority for businesses. The penetration test, or penetration testing, represents a major proactive approach to detect vulnerabilities before they are exploited by attackers. This method involves simulating a targeted attack simulation, reproducing techniques used by hackers to assess the … Read more

Zero Trust architecture: rethinking security without a perimeter

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In a world where networks are no longer confined to a single geographical location and where digital technology extends across a multitude of devices and services, traditional perimeter security shows its limits. The rise of cloud computing, widespread telecommuting, and the increase in external access points necessitate a complete overhaul of data protection methods and … Read more

Time Series Forecasting with deep learning

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In a world where data is accumulating at an exponential rate, time series forecasting has become an essential practice for anticipating future events from past data. Whether in finance, meteorology, or even energy management, the ability to model and predict temporal developments is crucial for making essential strategic decisions. Deep learning, particularly through neural networks … Read more

AI ethics: developing responsible and fair AI

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As artificial intelligence establishes itself across all sectors, from healthcare to finance, the question of its responsible development now occupies a central place. The benefits of AI are undeniable: increased efficiency, enhanced innovation, reduced costs. Yet, this technological power comes with major risks, particularly regarding respect for human rights, data protection, and bias prevention. The … Read more

MLOps: industrializing the deployment of AI models

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In a context where artificial intelligence is increasingly infiltrating the core of business strategies, the multiplication of initiatives in machine learning seems promising. Yet, the majority of the models developed never go beyond the experimental stage. They stagnate in notebooks, isolated, never integrated into business processes. This major gap is less explained by the intrinsic … Read more

Explainable AI: making artificial intelligence transparent

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Artificial intelligence has established itself as an indispensable driver of digital transformation across all sectors, from healthcare to finance. However, its widespread adoption faces a major challenge: understanding the decisions produced by algorithms often perceived as “black boxes.” Explainability is now at the heart of the discussions, embodying the need to make these systems more … Read more

Edge AI : deploying artificial intelligence on mobile devices

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At the dawn of a new technological era, Edge AI is emerging as a major revolution in the field of artificial intelligence. This approach, which consists of executing algorithms directly on mobile devices and other connected devices, profoundly changes data processing and user experience. By bringing artificial intelligence closer to data sources, it significantly reduces … Read more

Federated learning: training AI without centralizing data

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In a context where the massive collection of personal data raises numerous concerns, the rise of artificial intelligence encounters limits imposed by the need to protect privacy. Federated learning changes the game by proposing a distributed model that respects data confidentiality while allowing collaborative and effective AI training. This revolutionary approach rethinks the classical paradigm … Read more