SPARQ: A QoS-aware Framework for Mitigating Cyber Risk in Self-Protecting IoT Systems
FULL
This program is tentative and subject to change.
Today’s smart spaces deploy various IoT devices to offer services for occupants. Such devices are exposed to security risks that may pose serious threats to network services and users’ privacy. To avoid the disruption of normal operations, self-protecting solutions have been developed to allow IoT networks to autonomously respond to cyber threats in real-time. However, existing self-protecting systems focus solely on architectural adaptations to respond to cyber threats, overlooking the mitigation actions described in cybersecurity standards –which represent the correct cybersecurity posture– as well as the impact of the adaptation strategies on the Quality-of-Service (QoS) performance. To overcome these existing limitations, this paper presents SPARQ, a novel framework for designing self-protecting IoT systems that considers both the security exposure to cyber attacks and the QoS performance. We leverage Attack Graph as a threat model for analyzing the cyber exposure of the system and Queuing Network Models to analyze QoS in IoT systems. Based on the analysis outcomes, SPARQ provides mitigation plans to reduce the cyber risk while also minimizing the impact on QoS. We evaluate the proposed approach on two use cases from real-world scenarios including a critical infrastructure and a smart home. The experimental evaluation shows that SPARQ is capable of reducing the cyber risk significantly while also improving the QoS performance by 35% compared to existing approaches.
This program is tentative and subject to change.
Tue 29 AprDisplayed time zone: Eastern Time (US & Canada) change
14:00 - 15:30 | |||
14:00 25mTalk | FLEXICO: Sustainable Machine Translation via Self-AdaptationFULL Research Track Maria Casimiro Instituto Superior Técnico, Universidade de Lisboa & S3D, Carnegie Mellon University, Paolo Romano IST/INESC-ID, José Sousa Unbabel, Amin M Khan INESC-ID. Universidade de Lisboa, David Garlan Carnegie Mellon University | ||
14:25 25mTalk | SPARQ: A QoS-aware Framework for Mitigating Cyber Risk in Self-Protecting IoT SystemsFULL Research Track Alessandro Palma Università di Roma Sapienza, Houssam Hajj Hassan SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris, Georgios Bouloukakis Télécom SudParis, Institut Polytechnique de Paris | ||
14:50 15mTalk | Adapting Aggregation Rule for Robust Federated Learning under Dynamic AttacksSHORT Research Track Chenyu Hu Southwest University, Mingyue Zhang Southwest University, NIANYU LI ZGC Lab, China, Jialong Li Waseda University, Japan, Zheng Yang Southwest University, Muneeb Ul Hassan Deakin University, Kenji Tei Institute of Science Tokyo | ||
15:05 15mTalk | Adaptive and Interoperable Federated Data Spaces: An Implementation ExperienceARTIFACT Artifact Track Nikolaos Papadakis , Niemat Khoder Télécom SudParis, Institut Polytechnique de Paris, France, Daphne Tuncer Ecole nationale des ponts et chaussees, Institut Polytechnique de Paris, France, Kostas Magoutis University of Crete and FORTH-ICS, Georgios Bouloukakis Télécom SudParis, Institut Polytechnique de Paris | ||
15:20 10mOther | Discussion Session 7 Research Track |