Safer AI for a better world

Designing Machine Intelligence for

Cyber Security, Machine Vision, and Data-driven Business Applications


Read more about the next editions

Half-day Tutorial on Adversarial Machine Learning

Attack Prophecy

Rewriting the rules of protection

AI-based Web Services Protection Solution


Trustworthy AI

Pluribus One is a research-intensive startup company that turns basic research results into commercial products and provides innovative AI based solutions and Secure Machine Learning products that are the strongest link in the Cybersecurity chain and not the weakest one.

Adversarial ML Pioneers

Pluribus One is a spin out of the PRA University Lab with more than 20 years of experience in world-class research on Adversarial Machine Learning and in providing solutions based on Pattern Recognition and secure Machine Learning technologies for real-world applications.

Secure your business

Pluribus One develops customized solutions and other data-driven applications to secure your business and your devices. Our customers safely enjoy our products in these fields: Public Administration, Healthcare, Finance, Defence, Education, E-commerce.
Attack Prophecy® is the most advanced system for the detection and protection against web attacks, output of years of research in the field of computer security.
Attack Prophecy® works in three steps:
(1) learning, (2) detection and (3) protection.
It automatically learns the legitimate (normal) traffic profile by observing its live traces.

Artificial Intelligence behind your applications

Machine Learning behind your protection

Discover the effectiveness of our Next Generation
Web Services Protection Solution

Want to see a live demo?

Schedule a Trial

No software agent required.
Simply safe, from any device

Privacy compliant and
tremendously scalable

Want to be involved in the AIsafe DNS Beta-testing?

AIsafe DNS is a comprehensive solution for the prevention and detection of endpoints threats.

It offers coverage against a wide range of threats, from malware to phishing, enabling the mitigation of the risk associated with them.
AIsafe DNS allows to promptly identify machines in a network which contacted malicious hosts on the Internet, so that it is possibile to isolate them immediately. This prevents an attack campaign to result effective on a large scale significantly reducing the damage it may cause.

Additionally, the solution allows to maintain the monitored network healty and clean, enabling the discovery of unwanted or misconfigured services on the monitored endpoints.

Our Partners

Leading Research

The founders of Pluribus One have a large experience in leading R&D projects in computer security, pattern recognition, machine learning and related research areas, funded under the European Research Framework Programmes (FP6 - FP7 - Horizon2020)
Funded by EU under the H2020 Programme.
The main goal of ALOHA is to facilitate implementation of Deep Learning algorithms on low-power embedded systems and heterogeneous low-energy computing platforms, providing automation for optimal algorithm selection, resource allocation and deployment.
Funded by EU under the H2020 Programme.
LETSCROWD overcomes challenges preventing the implementation of the European Security Model with regards to mass gatherings, providing assessment methodologies and advanced automated softwares to Law Enforcement Agencies  and security operators.
Funded by EIT Digital.
Phishsense is a phishing reputation service aimed at providing advanced knowledge regarding phishing attacks. Combining multiple datasets and feeds regarding known incidents, Phishsense can help companies to improve the ongoing defence processes for identification, mitigation and prevention of phishing attacks, regardless from where the threat comes.



Artificial Intelligence Vs. the Hackers
Machine-learning algorithms watch hackers’ behavior and adapt to their evolving tactics. Machine learning security systems don’t work in all instances, particularly when there is insufficient data to train them. And researchers and companies worry constantly that they can be exploited by hackers. For example, they could mimic users’ activity to foil algorithms that screen for typical behavior. Or hackers could tamper with the data used to train the algorithms and warp it for their own ends-so-called poisoning. That’s why it’s so important for companies to keep their algorithmic criteria secret and change the formulas regularly, says Battista Biggio...


Evasion Attacks vs. Machine Learning
This web application demo creates evasion attacks (a.k.a. adversarial examples) against a multiclass Support Vector Machine classifier for handwritten digit recognition (using MNIST digits). It implements the high-confidence evasion attacks firstly defined in [Biggio et al., ECML-PKDD 2013] for two-class classifiers, and then extended to multi-class problems in [Melis et al., ViPAR 2017].


WILD Patterns Tutorial on Adversarial Machine Learning
WILD PATTERNS is our successful free tutorial on the security of Machine Learning and Artificial Intelligence systems, that has seen over the last years the overall participation of more than 1000 attendees around the world. WILD PATTERNS introduces the fundamentals of adversarial machine learning, presenting recently-proposed techniques to assess the vulnerability of machine-learning algorithms to adversarial attacks, and some of the most effective countermeasures. We consider these threats in different application domains (object recognition in images, biometric identity recognition, spam and malware detection). Don't miss the next edition of WILD PATTERNS, it will be announced soon. The last edition was held at ITASEC 2019, February 14, Pisa.


Pluribus One S.r.l.

Via Bellini 9, 09128, Cagliari (CA)


PEC: pluribus-one[at]


Legal entity

Share capital: € 10008

Paid-up share capital: € 4.602

VAT no.: 03621820921

R.E.A.: Cagliari 285352


University of Cagliari

  Pluribus One is a spin-off

  of the Department of

  Electrical and Electronic Engineering

  University of Cagliari, Italy


© 2019 Pluribus One s.r.l. All Rights Reserved.