1,721,042 research outputs found
A Novel Approach for Securing Federated Learning: Detection and Defense Against Model Poisoning Attacks
No full textFederated Learning (FL) holds great promise for collaborative model training across distributed devices. However, it faces a significant threat: model poisoning attacks. In particular, Byzantine attacks can severely compromise the accuracy of FL systems. Through experimental analysis, we demonstrate a significant degradation in network accuracy as the percentage of malicious participants increases, underscoring the critical need for robust defense mechanisms. Our proposed detection strategy, based on clustering algorithms, exhibits promising results in identifying outliers and potential attackers, offering a proactive approach to safeguarding FL systems against adversarial manipulation. This work underscores the critical necessity of implementing robust detection and mitigation strategies to improve the resilience of Federated Learning against increasingly sophisticated and pervasive attacks
Clinical treatment of polycystic kidney disease (APKD): do we need further suggestions from rodents?
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Enhancing Healthcare Data Confidentiality through Decentralized TEE Attestation
No full textThe integration of digital technologies like the Internet of Medical Things (IoMT) and Electronic Health Records (EHRs) in healthcare has significantly improved patient care. However, this digital transformation brings challenges, particularly regarding the secure computation, privacy and security of sensitive health information. This paper presents a potential solution to these challenges by combining Trusted Execution Environments (TEEs) with a decentralized attestation mechanism. Unlike traditional centralized attestation, the proposed approach leverages blockchain technology to ensure transparency, immutability, and enhanced security. This research outlines the benefits, discusses potential threats to validity, and suggests future work to further secure healthcare data processing, ultimately aiming to create a more robust and resilient system for protecting sensitive patient information
Modificazioni del volume plasmatico e del fattore natriuretico atriale in corso di trattamento cronico con ciclosporina A
No full textAn Innovative Approach to Real-Time Concept Drift Detection in Network Security
No full textIn the realm of cybersecurity, the detection of Concept Drift holds the potential to improve the adaptability and effectiveness of security systems. In particular, Security Information and Event Management (SIEM) frameworks can benefit from real-time Drift Detection, enabling prompt detection of changing attack patterns, and consequent update of the detection criteria. To explore such an opportunity, the proposed approach extends a previously introduced SIEM solution with Concept Drift Detectors. An experimental evaluation is presented using two well-known unsupervised detectors on a merged dataset featuring Concept Drift, taking into consideration metrics such as Error Rate, Precision, Recall, and Window Average Error Rate. The results demonstrate that the integrated mechanism successfully identifies Concept Drift, triggering SIEM alerts and prompting timely updates to correlation rules. The experiment’s implications, limitations, and future directions are discussed, emphasizing the importance of continuous improvement in cybersecurity measures
A Tamper-Resistant Storage Framework for Smart Grid security
No full textIn the past few years, the energy sector has been among the most targeted by cyber-criminals. Due to the strong reliance of Critical Infrastructures on energy distribution, and the strategic value of such systems, the impact of intrusions and data breaches cannot be underestimated. In this scenario, data constitutes a critical asset to protect, especially as the latest technological development has led to interconnected intelligent systems, named smart grids. The consequences of data tampering, exposure or loss can range from disruption of essential services, to serious risks for environment, economy and people safety. Data provenance, as the documentation of the origin of data and the processes and methodology that led to it, can bring support when facing the aforementioned attacks. The present work aims to address security issues in the energy domain, by proposing the Advanced Tamper-Resistant Storage (ATRS), a novel framework for data provenance based on blockchain technology. The ATRS allows for the creation and storage of provenance records, whose reliability is ensured by the tamper-resistance feature enabled through the combination of blockchain and TLS-based communication. The framework, tailored and tested for the smart grid domain, can easily be customized for different critical use cases
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