DCDIDP: A distributed, collaborative, and data-driven intrusion detection and prevention framework for cloud computing environments

With the growing popularity of cloud computing, the exploitation of possible vulnerabilities grows at the same pace; the distributed nature of the cloud makes it an attractive target for potential intruders. Despite security issues delaying its adoption, cloud computing has already become an unstoppable force; thus, security mechanisms to ensure its secure adoption are an immediate need. Here, we focus on intrusion detection and prevention systems (IDPSs) to defend against the intruders. In this paper, we propose a Distributed, Collaborative, and Data-driven Intrusion Detection and Prevention system (DCDIDP). Its goal is to make use of the resources in the cloud and provide a holistic IDPS for all cloud service providers which collaborate with other peers in a distributed manner at different architectural levels to respond to attacks. We present the DCDIDP framework, whose infrastructure level is composed of three logical layers: network, host, and global as well as platform and software levels. Then, we review its components and discuss some existing approaches to be used for the modules in our proposed framework. Furthermore, we discuss developing a comprehensive trust management framework to support the establishment and evolution of trust among different cloud service providers. © 2011 ICST

Proceedings of International Workshop "Global Computing: Programming Environments, Languages, Security and Analysis of Systems"

According to the IST/ FET proactive initiative on GLOBAL COMPUTING, the goal is to obtain techniques (models, frameworks, methods, algorithms) for constructing systems that are flexible, dependable, secure, robust and efficient. The dominant concerns are not those of representing and manipulating data efficiently but rather those of handling the co-ordination and interaction, security, reliability, robustness, failure modes, and control of risk of the entities in the system and the overall design, description and performance of the system itself. Completely different paradigms of computer science may have to be developed to tackle these issues effectively. The research should concentrate on systems having the following characteristics: • The systems are composed of autonomous computational entities where activity is not centrally controlled, either because global control is impossible or impractical, or because the entities are created or controlled by different owners. • The computational entities are mobile, due to the movement of the physical platforms or by movement of the entity from one platform to another. • The configuration varies over time. For instance, the system is open to the introduction of new computational entities and likewise their deletion. The behaviour of the entities may vary over time. • The systems operate with incomplete information about the environment. For instance, information becomes rapidly out of date and mobility requires information about the environment to be discovered. The ultimate goal of the research action is to provide a solid scientific foundation for the design of such systems, and to lay the groundwork for achieving effective principles for building and analysing such systems. This workshop covers the aspects related to languages and programming environments as well as analysis of systems and resources involving 9 projects (AGILE , DART, DEGAS , MIKADO, MRG, MYTHS, PEPITO, PROFUNDIS, SECURE) out of the 13 founded under the initiative. After an year from the start of the projects, the goal of the workshop is to fix the state of the art on the topics covered by the two clusters related to programming environments and analysis of systems as well as to devise strategies and new ideas to profitably continue the research effort towards the overall objective of the initiative. We acknowledge the Dipartimento di Informatica and Tlc of the University of Trento, the Comune di Rovereto, the project DEGAS for partially funding the event and the Events and Meetings Office of the University of Trento for the valuable collaboration

Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

Robots that navigate among pedestrians use collision avoidance algorithms to enable safe and efficient operation. Recent works present deep reinforcement learning as a framework to model the complex interactions and cooperation. However, they are implemented using key assumptions about other agents' behavior that deviate from reality as the number of agents in the environment increases. This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules. This work also introduces a strategy using LSTM that enables the algorithm to use observations of an arbitrary number of other agents, instead of previous methods that have a fixed observation size. The proposed algorithm outperforms our previous approach in simulation as the number of agents increases, and the algorithm is demonstrated on a fully autonomous robotic vehicle traveling at human walking speed, without the use of a 3D Lidar

K-Trek: A Peer-to-Peer Approach To Distribute Knowledge In Large Environments

In this paper, we explore an architecture, called K-Trek, that enables mobile users to travel across knowledge distributed over a large geographical area (ranging from large public buildings to a national park). Our aim is providing, dis-tributing, and enriching the environment with location-sensitive information for use by agents on board of mobile and static devices. Local interactions among K-Trek devices and the distribution of information in the larger environment adopt some typical peer-to-peer patterns and techniques. We introduce the architecture, discuss some of its potential knowledge management applications, and present a few experimental results obtained with simulation

JXTA-Overlay: a P2P platform for distributed, collaborative, and ubiquitous computing

With the fast growth of the Internet infrastructure and the use of large-scale complex applications in industries, transport, logistics, government, health, and businesses, there is an increasing need to design and deploy multifeatured networking applications. Important features of such applications include the capability to be self-organized, be decentralized, integrate different types of resources (personal computers, laptops, and mobile and sensor devices), and provide global, transparent, and secure access to resources. Moreover, such applications should support not only traditional forms of reliable distributing computing and optimization of resources but also various forms of collaborative activities, such as business, online learning, and social networks in an intelligent and secure environment. In this paper, we present the Juxtapose (JXTA)-Overlay, which is a JXTA-based peer-to-peer (P2P) platform designed with the aim to leverage capabilities of Java, JXTA, and P2P technologies to support distributed and collaborative systems. The platform can be used not only for efficient and reliable distributed computing but also for collaborative activities and ubiquitous computing by integrating in the platform end devices. The design of a user interface as well as security issues are also tackled. We evaluate the proposed system by experimental study and show its usefulness for massive processing computations and e-learning applications.Peer ReviewedPostprint (author's final draft