8 citations · 21 across the 19 of their papers we have counts for
6 papers · 1 filter
Stack-based Buffer Overflow Detection using Recurrent Neural Networks
William Arild Dahl, Laszlo Erdodi, Fabio Massimo Zennaro
Detecting vulnerabilities in software is a critical challenge in the development and deployment of applications. One of the most known and dangerous vulnerabilities is stack-based…
Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions
Alexander Egiazarov, Fabio Massimo Zennaro, Vasileios Mavroeidis
Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents such as terrorism, general crimin…
The Agent Web Model -- Modelling web hacking for reinforcement learning
Laszlo Erdodi, Fabio Massimo Zennaro
Website hacking is a frequent attack type used by malicious actors to obtain confidential information, modify the integrity of web pages or make websites unavailable. The tools use…
Using Subjective Logic to Estimate Uncertainty in Multi-Armed Bandit Problems
Fabio Massimo Zennaro, Audun Jøsang
The multi-armed bandit problem is a classical decision-making problem where an agent has to learn an optimal action balancing exploration and exploitation. Properly managing this t…
Modeling Penetration Testing with Reinforcement Learning Using Capture-the-Flag Challenges: Trade-offs between Model-free Learning and A Priori Knowledge
Fabio Massimo Zennaro, Laszlo Erdodi
Penetration testing is a security exercise aimed at assessing the security of a system by simulating attacks against it. So far, penetration testing has been carried out mainly by…
Firearm Detection and Segmentation Using an Ensemble of Semantic Neural Networks
Alexander Egiazarov, Vasileios Mavroeidis, Fabio Massimo Zennaro +1
In recent years we have seen an upsurge in terror attacks around the world. Such attacks usually happen in public places with large crowds to cause the most damage possible and get…