Publication Library
Optimal In-Network Distribution of Learning Functions for a Secure-by-Design Programmable Data Plane of Next-Generation Networks
Description: The rise of programmable data plane (PDP) and in-network computing (INC) paradigms paves the way for the development of network devices (switches, network interface cards, etc.) capable of performing advanced computing tasks. This allows to execute algorithms of various nature, including machine learning ones, within the network itself to support user and network services. In particular, this paper delves into the issue of implementing in-network learning models to support distributed intrusion detection systems (IDS). It proposes a model that optimally distributes the IDS workload, resulting from the subdivision of a "Strong Learner" (SL) model into lighter distributed "Weak Learner" (WL) models, among data plane devices; the objective is to ensure complete network security without excessively burdening their normal operations. Furthermore, a meta-heuristic approach is proposed to reduce the long computational time required by the exact solution provided by the mathematical model, and its performance is evaluated. The analysis conducted and the results obtained demonstrate the enormous potential of the proposed new approach to the creation of intelligent data planes that effectively act as a first line of defense against cyber attacks, with minimal additional workload on network devices.
Created At: 04 December 2024
Updated At: 04 December 2024
Towards Type Agnostic Cyber Defense Agents
Description: With computing now ubiquitous across government, industry, and education, cybersecurity has become a critical component for every organization on the planet. Due to this ubiquity of computing, cyber threats have continued to grow year over year, leading to labor shortages and a skills gap in cybersecurity. As a result, many cybersecurity product vendors and security organizations have looked to artificial intelligence to shore up their defenses. This work considers how to characterize attackers and defenders in one approach to the automation of cyber defense -- the application of reinforcement learning. Specifically, we characterize the types of attackers and defenders in the sense of Bayesian games and, using reinforcement learning, derive empirical findings about how to best train agents that defend against multiple types of attackers.
Created At: 04 December 2024
Updated At: 04 December 2024
Defending against Intrusion of Malicious UAVs with Networked UAV Defense Swarms
Description: Nowadays, companies such as Amazon, Alibaba, and even pizza chains are pushing forward to use drones, also called UAVs (Unmanned Aerial Vehicles), for service provision, such as package and food delivery. As governments intend to use these immense economic benefits that UAVs have to offer, urban planners are moving forward to incorporate so-called UAV flight zones and UAV highways in their smart city designs. However, the high-speed mobility and behavior dynamics of UAVs need to be monitored to detect and, subsequently, to deal with intruders, rogue drones, and UAVs with a malicious intent. This paper proposes a UAV defense system for the purpose of intercepting and escorting a malicious UAV outside the flight zone. The proposed UAV defense system consists of a defense UAV swarm, which is capable to self-organize its defense formation in the event of intruder detection, and chase the malicious UAV as a networked swarm. Modular design principles have been used for our fully localized approach. We developed an innovative auto-balanced clustering process to realize the intercept- and capture-formation. As it turned out, the resulting networked defense UAV swarm is resilient against communication losses. Finally, a prototype UAV simulator has been implemented. Through extensive simulations, we show the feasibility and performance of our approach.
Created At: 04 December 2024
Updated At: 04 December 2024
Embodied Artificial Intelligence Trends and Challenges
Description: The field of Artificial Intelligence, which started roughly half a century ago, has a turbulent history. In the 1980s there has been a major paradigm shift towards embodiment. While embodied artificial intelligence is still highly diverse, changing, and far from “theoretically stable”, a certain consensus about the important issues and methods has been achieved or is rapidly emerging. In this non-technical paper we briefly characterize the field, summarize its achievements, and identify important issues for future research. One of the fundamental unresolved problems has been and still is how thinking emerges from an embodied system. Provocatively speaking, the central issue could be captured by the question “How does walking relate to thinking?”
Created At: 04 December 2024
Updated At: 04 December 2024
All Robots in One A New Standard and Unified Dataset for Versatile, General-Purpose Embodied Agents
Description: Embodied AI is transforming how AI systems interact with the physical world, yet existing datasets are inadequate for developing versatile, general-purpose agents. These limitations include a lack of standardized formats, insufficient data diversity, and inadequate data volume. To address these issues, we introduce ARIO (All Robots In One), a new data standard that enhances existing datasets by offering a unified data format, comprehensive sensory modalities, and a combination of real-world and simulated data. ARIO aims to improve the training of embodied AI agents, increasing their robustness and adaptability across various tasks and environments. Building upon the proposed new standard, we present a large-scale unified ARIO dataset, comprising approximately 3 million episodes collected from 258 series and 321,064 tasks. The ARIO standard and dataset represent a significant step towards bridging the gaps of existing data resources. By providing a cohesive framework for data collection and representation, ARIO paves the way for the development of more powerful and versatile embodied AI agents, capable of navigating and interacting with the physical world in increasingly complex and diverse ways. The project is available on https: //imaei.github.io/project_pages/ario/.
Created At: 04 December 2024
Updated At: 04 December 2024