The increasing adoption of technologies and the exponential growth of networks has made the area of information technology an integral part of our lives, where network security plays a vital role. One of the most serious threats in the current Internet is posed by distributed denial of service (DDoS) attacks, which target the availability of the victim system. Such an attack is designed to exha...

The increasing number of security incidents against computer networks has made insufficient network management and intrusion detection approaches to maintain and to protect these complex systems. Even distributed intrusion detection seems to be not enough if it is used isolated from other disciplines. My research will focus in how network and security agents can learn to detect and to categoris...

This paper proposes a fully asynchronous scheme for the policy evaluation problem of distributed reinforcement learning (DisRL) over directed peer-to-peer networks. Without waiting any other node network, each can locally update its value function at time using (possibly delayed) information from neighbors. is in sharp contrast to gossip-based where pair nodes concurrently update. Even though s...

We propose a fully distributed actor-critic architecture, named Diff-DAC, with application to multitask reinforcement learning (MRL). During the process, agents communicate their value and policy parameters neighbours, diffusing information across network of no need for central station. Each agent can only access data from its local task, but aims learn common that performs well whole set tasks...

Traditional Recommender Systems (RS) use central servers to collect user data, compute profiles and train global recommendation models. Central computation of RS models has great results in performance because the are trained using all available information full profiles. However, centralised require users share their whole interaction history with server general not scalable as number interact...

Distributed robotic systems can benefit from automatic controller design and online adaptation by reinforcement learning (RL), but often suffer from the limitations of partial observability. In this paper, we address the twin problems of limited local experience and locally observed but not necessarily telling reward signals encountered in such systems. We combine direct search in policy space ...