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Internet of Things is one of the most significant latest developments in computer science. It is common for modern computing infrastructures to partially consist of numerous low power devices that are characterized by high diversity in both hardware and software. Existing security models, approaches and solutions are not able to sufficiently protect such systems. In this paper we propose the use of lightweight agents installed at multiple internet of things (IoT) installations (e.g., smart-homes), in order to collaboratively detect distributed denial of service (DDoS) attacks conducted by the use of IoT devices botnets. Specifically, agents exchange outbound traffic information in order to identify possible victims of DDoS attacks. This information exchange is governed by a blockchain smart contract, that ensures the integrity of both the procedure and the information. A simulation of the operation of the proposed methodology has been conducted in order to evaluate both its detection efficiency and its resilience against malicious agents that aim to falsify results.

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Late-onset sepsis (LOS) and necrotizing enterocolitis (NEC) are severe life-threatening conditions for neonates. Accurate, early diagnosis and timely initiation of treatment are crucial. Non-specific overlapping clinical signs along with the non-sensitive/specific diagnostic tools set obstacles to speedy, trustful diagnosis including differential diagnosis. The objective of this study was to evaluate the potential of targeted LC-MS/MS proteomics in identifying diagnostic biomarkers of NEC or LOS. We conducted a prospective case-control study evaluating serum proteomics profiles of 25 NEC, 18 LOS, and an equal number of matched control neonates, over three sampling points. Eighty-three concatemers and synthetic peptides belonging to 47 protein markers of the two diseases were selected after thorough literature search. A novel selected reaction monitoring (SRM), LC-MS/MS method was developed for their analysis and evaluation as potential biomarkers. Multivariate and univariate statistical analyses highlighted significant proteins in differentiating LOS and NEC neonates and diseased from controls. Moreover, panels of proteins were tested for their ability to distinguish LOS from NEC and controls. We suggest two panels of three proteins each, exhibiting very high diagnostic value for LOS and excellent diagnostic performance at the critical LOS-NEC differentiation, reaching an AUC ROC value close to 1 (0.999). These panels constitute a valuable starting point for further validation with broader cohorts of neonates, aiming to improve the clinical practice. Graphical abstract ᅟ.

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