Towards Distributed Accommodation of Covert Attacks in Interconnected Systems

The problem of mitigating maliciously injected signals in interconnected systems is dealt with in this paper. We consider the class of covert attacks, as they are stealthy and cannot be detected by conventional means in centralized settings. Distributed architectures can be leveraged for revealing such stealthy attacks by exploiting communication and local model knowledge. We show how such detection schemes can be improved to estimate the action of an attacker and we propose an accommodation scheme in order to mitigate or neutralize abnormal behavior of a system under attack

Tendon Healing Response Is Dependent on Epithelial–Mesenchymal–Tendon Transition State of Amniotic Epithelial Stem Cells

Tendinopathies are at the frontier of advanced responses to health challenges and sectoral policy targets. Cell‐based therapy holds great promise for tendon disorder resolution. To verify the role of stepwise trans‐differentiation of amniotic epithelial stem cells (AECs) in tendon regeneration, in the present research three different AEC subsets displaying an epithelial (eAECs), mesenchymal (mAECs), and tendon‐like (tdAECs) phenotype were allotransplanted in a validated experimental sheep Achilles tendon injury model. Tissue healing was analyzed adopting a comparative approach at two early healing endpoints (14 and 28 days). All three subsets of transplanted cells were able to accelerate regeneration: mAECs with a lesser extent than eAECs and tdAECs as indicated in the summary of the total histological scores (TSH), where at day 28 eAECs and tdAECs had better significant scores with respect to mAEC‐treated tendons (p < 0.0001). In addition, the immunomodulatory response at day 14 showed in eAEC‐transplanted tendons an upregulation of pro‐regenerative M2 macrophages with respect to mAECs and tdAECs (p < 0.0001). In addition, in all allotransplanted tendons there was a favorable IL10/IL12 compared to CTR (p < 0.001). The eAECs and tdAECs displayed two different underlying regenerative mechanisms in the tendon. The eAECs positively influenced regeneration mainly through their greater ability to convey in the host tissue the shift from pro‐inflammatory to pro‐regenerative responses, leading to an ordered extracellular matrix (ECM) deposition and blood vessel remodeling. On the other hand, the transplantation of tdAECs acted mainly on the proliferative phase by impacting the density of ECM and by supporting a prompt recovery, inducing a low cellularity and angle alignment of the host cell compartment. These results support the idea that AECs lay the groundwork for production of different cell phenotypes that can orient tendon regeneration through a crosstalk with the host tissue. In particular, the obtained evidence suggests that eAECs are a practicable and efficient strategy for the treatment of acute tendinopathies, thus reinforcing the grounds to move their use towards clinical practice

Intelligenza artificiale e sicurezza: opportunità, rischi e raccomandazioni

L'IA (o intelligenza artificiale) è una disciplina in forte espansione negli ultimi anni e lo sarà sempre più nel prossimo futuro: tuttavia è dal 1956 che l’IA studia l’emulazione dell’intelligenza da parte delle macchine, intese come software e in certi casi hardware. L’IA è nata dall’idea di costruire macchine che - ispirandosi ai processi legati all’intelligenza umana - siano in grado di risolvere problemi complessi, per i quali solitamente si ritiene che sia necessario un qualche tipo di ragionamento intelligente. La principale area di ricerca e applicazione attuale dell’IA è il machine learning (algoritmi che imparano e si adattano in base ai dati che ricevono), che negli ultimi anni ha trovato ampie applicazioni grazie alle reti neurali (modelli matematici composti da neuroni artificiali) che a loro volta hanno consentito la nascita del deep learning (reti neurali di maggiore complessità). Appartengono al mondo dell’IA anche i sistemi esperti, la visione artificiale, il riconoscimento vocale, l’elaborazione del linguaggio naturale, la robotica avanzata e alcune soluzioni di cybersecurity. Quando si parla di IA c'è chi ne è entusiasta pensando alle opportunità, altri sono preoccupati poiché temono tecnologie futuristiche di un mondo in cui i robot sostituiranno l'uomo, gli toglieranno il lavoro e decideranno al suo posto. In realtà l'IA è ampiamente utilizzata già oggi in molti campi, ad esempio nei cellulari, negli oggetti smart (IoT), nelle industry 4.0, per le smart city, nei sistemi di sicurezza informatica, nei sistemi di guida autonoma (drive o parking assistant), nei chat bot di vari siti web; questi sono solo alcuni esempi basati tutti su algoritmi tipici dell’intelligenza artificiale. Grazie all'IA le aziende possono avere svariati vantaggi nel fornire servizi avanzati, personalizzati, prevedere trend, anticipare le scelte degli utenti, ecc. Ma non è tutto oro quel che luccica: ci sono talvolta problemi tecnici, interrogativi etici, rischi di sicurezza, norme e legislazioni non del tutto chiare. Le organizzazioni che già adottano soluzioni basate sull’IA, o quelle che intendono farlo, potrebbero beneficiare di questa pubblicazione per approfondirne le opportunità, i rischi e le relative contromisure. La Community for Security del Clusit si augura che questa pubblicazione possa fornire ai lettori un utile quadro d’insieme di una realtà, come l’intelligenza artificiale, che ci accompagnerà sempre più nella vita personale, sociale e lavorativa.AI (or artificial intelligence) is a booming discipline in recent years and will be increasingly so in the near future.However, it is since 1956 that AI has been studying the emulation of intelligence by machines, understood as software and in some cases hardware. AI arose from the idea of building machines that-inspired by processes related to human intelligence-are able to solve complex problems, for which it is usually believed that some kind of intelligent reasoning is required. The main current area of AI research and application is machine learning (algorithms that learn and adapt based on the data they receive), which has found wide applications in recent years thanks to neural networks (mathematical models composed of artificial neurons), which in turn have enabled the emergence of deep learning (neural networks of greater complexity). Also belonging to the AI world are expert systems, computer vision, speech recognition, natural language processing, advanced robotics and some cybersecurity solutions. When it comes to AI there are those who are enthusiastic about it thinking of the opportunities, others are concerned as they fear futuristic technologies of a world where robots will replace humans, take away their jobs and make decisions for them. In reality, AI is already widely used in many fields, for example, in cell phones, smart objects (IoT), industries 4.0, for smart cities, cybersecurity systems, autonomous driving systems (drive or parking assistant), chat bots on various websites; these are just a few examples all based on typical artificial intelligence algorithms. Thanks to AI, companies can have a variety of advantages in providing advanced, personalized services, predicting trends, anticipating user choices, etc. But not all that glitters is gold: there are sometimes technical problems, ethical questions, security risks, and standards and legislation that are not entirely clear. Organizations already adopting AI-based solutions, or those planning to do so, could benefit from this publication to learn more about the opportunities, risks, and related countermeasures. Clusit's Community for Security hopes that this publication will provide readers with a useful overview of a reality, such as artificial intelligence, that will increasingly accompany us in our personal, social and working lives

Diagnosis and management of Cornelia de Lange syndrome:first international consensus statement

Cornelia de Lange syndrome (CdLS) is an archetypical genetic syndrome that is characterized by intellectual disability, well-defined facial features, upper limb anomalies and atypical growth, among numerous other signs and symptoms. It is caused by variants in any one of seven genes, all of which have a structural or regulatory function in the cohesin complex. Although recent advances in next-generation sequencing have improved molecular diagnostics, marked heterogeneity exists in clinical and molecular diagnostic approaches and care practices worldwide. Here, we outline a series of recommendations that document the consensus of a group of international experts on clinical diagnostic criteria, both for classic CdLS and non-classic CdLS phenotypes, molecular investigations, long-term management and care planning

Diagnosis of stealthy local cyber-attacks in large-scale systems

Ubiquity of embedded computers and network connectivity have made control systems vulnerable to potential cyber-attacks. This has become a relevant problem after real-world cyber-security breaches targeting automation equipment, and notable research effort has been made in order to model such attacks and to render these systems more secure. However, particularly resourceful agents can design attacks that cannot be detected by existing monitoring system, and are therefore called stealthy. In this work, we address the case when these attacks are deployed on part of a Large-Scale System (LSS), which can be partitioned into a collection of Linear Time Invariant (LTI) systems. We provide a local characterisation of stealthy attacks for LSSs, including modelling in the state-space domain, analysis of the way physical interconnections affect detectability, and control resilience. For detectable attacks, we develop a general detection method that is distributed and scalable by design, as it relies only on neighbour-to-neighbour communication. The core principle is that, if one system is misbehaving, its neighbours can exploit the physical coupling to reveal such otherwise stealthy anomaly. Of this general idea, we devise two different implementations, depending on the types of disturbances affecting the system, namely disturbances with known bounds and stochastic Gaussian noise. In both cases, we design a residual generator which is sensitive to attacks in neighbouring systems, and provide theoretical analysis of its detection capabilities. Next, we give additional insights on the isolation properties ensuing our proposed methodology, obtaining conditions under which it is feasible. We prove that there are, in fact, some system topologies that do not allow for attack isolation. For those cases, we suitably augment the proposed detection algorithm and show that it is possible to isolate attacks in all cases, at the price of increased communication complexity. Isolation is a necessary step to allow for accommodation of the considered class of attacks. By this, we mean the design of an attack-tolerant control system that can counterbalance an attack’s effects once one is detected. Finally, in the last part of this work, we present early results in the disturbance-free case on how accommodation can be implemented. We provide existence conditions depending on the structure of the interconnections, and propose an algorithm that covers all cases. Each one of the methods proposed in this work is accompanied by a simulation example that demonstrates its effectiveness.Open Acces