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  1. Article ; Online: Cyber security of robots

    Alessio Botta / Sayna Rotbei / Stefania Zinno / Giorgio Ventre

    Intelligent Systems with Applications, Vol 18, Iss , Pp 200237- (2023)

    A comprehensive survey

    2023  

    Abstract: The use of robots in the modern world is widespread, not only in medicine and automated vehicles, but also in national security, defense, and industry. Together with the growing number of robots there is also an increase of cyber attacks against robots ... ...

    Abstract The use of robots in the modern world is widespread, not only in medicine and automated vehicles, but also in national security, defense, and industry. Together with the growing number of robots there is also an increase of cyber attacks against robots and in general of their security issues. Thus, we consider cyber security and related issues such as robots vulnerabilities from different perspectives that need to be investigated in order to understand strengths and weaknesses of robots. The aim of this paper is to cover the topic of cyber security in robots in a more focused and comprehensive way with respect to what has been done previously in literature. Throughout our comprehensive survey, we discuss also different aspects related to threats, attacks, and available methods for preventing malicious behavior from robots. As a result of our investigation, it has been found that robots' data, software, network, and hardware are the most vulnerable components. During this review, eventually current approaches to protect robots are discussed in order to maintain their integrity, availability, and confidentiality. Furthermore, we demonstrate that the likelihood of cyber security risks on robotic platforms can be significantly reduced through improvements in encryption, authorization/authentication, and physical security. Security level of different robotic systems is analyzed in different fields so as to determine whether the security needs to be upgraded or rectified. We also present and describe open challenges that can arise in the next few years. This paper aims at being a starting point for researchers and practitioners to understand and upgrade the cyber security of robots.
    Keywords Cyber security ; ROS ; Robot forensics ; DoS ; Cybernetics ; Q300-390 ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 629
    Language English
    Publishing date 2023-05-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Predicting the Severity of Lockdown-Induced Psychiatric Symptoms with Machine Learning

    Giordano D’Urso / Alfonso Magliacano / Sayna Rotbei / Felice Iasevoli / Andrea de Bartolomeis / Alessio Botta

    Diagnostics, Vol 12, Iss 957, p

    2022  Volume 957

    Abstract: During the COVID-19 pandemic, an increase in the incidence of psychiatric disorders in the general population and an increase in the severity of symptoms in psychiatric patients have been reported. Anxiety and depression symptoms are the most commonly ... ...

    Abstract During the COVID-19 pandemic, an increase in the incidence of psychiatric disorders in the general population and an increase in the severity of symptoms in psychiatric patients have been reported. Anxiety and depression symptoms are the most commonly observed during large-scale dramatic events such as pandemics and wars, especially when these implicate an extended lockdown. The early detection of higher risk clinical and non-clinical individuals would help prevent the new onset and/or deterioration of these symptoms. This in turn would lead to the implementation of public policies aimed at protecting vulnerable populations during these dramatic contingencies, therefore optimising the effectiveness of interventions and saving the resources of national healthcare systems. We used a supervised machine learning method to identify the predictors of the severity of psychiatric symptoms during the Italian lockdown due to the COVID-19 pandemic. Via a case study, we applied this methodology to a small sample of healthy individuals, obsessive-compulsive disorder patients, and adjustment disorder patients. Our preliminary results show that our models were able to predict depression, anxiety, and obsessive-compulsive symptoms during the lockdown with up to 92% accuracy based on demographic and clinical characteristics collected before the pandemic. The presented methodology may be used to predict the psychiatric prognosis of individuals under a large-scale lockdown and thus supporting the related clinical decisions.
    Keywords machine learning ; COVID-19 ; prediction ; obsessive-compulsive disorder ; depression ; anxiety ; Medicine (General) ; R5-920
    Subject code 310
    Language English
    Publishing date 2022-04-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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