Cyber-physical systems constitute a disruptive technology across many industries, with a strong impact on economies and social processes. Their applications in many domains, from manufacturing to ...agriculture and from critical infrastructure to assistive living, brings challenges in technology, business, law and ethics.
Safety and security have traditionally been distinct problems in engineering and computer science. The introduction of computing elements to create cyber-physical systems (CPSs) has opened up a vast ...new range of potential problems that do not always show up on the radar of traditional engineers. Security, in contrast, is traditionally viewed as a data or communications security problem to be handled by computer scientists and/or computer engineers. Advances in CPSs and the Internet-of-Things (IoT) requires us to take a unified view of safety and security. This paper defines a safety/security threat model for CPSs and IoT systems and surveys emerging techniques which improve the safety and security of CPSs and IoT systems.
Industry is a leading application domain for the Internet of Things. The wide availability of industrial control systems (operational technology) combined with the Internet revolutionizes ...infrastructures and services, especially critical ones, providing effective and efficient, dependable, and safe real-time solutions.
Sisyfos is a modular and extensible platform for malware analysis; it addresses multiple operating systems, including critical infrastructure ones. Its purpose is to enable the development and ...evaluation of new tools as well as the evaluation of malware classifiers. Sisyfos has been developed based on open software for feature extraction and is available as a stand-alone tool with a web interface but can be integrated into an operational environment with a continuous sample feed. We present the structure and implementation of Sisyfos, which accommodates analysis for Windows, Linux and Android malware.
Security challenges in embedded systems Serpanos, Dimitrios; Voyiatzis, Artemios
ACM transactions on embedded computing systems,
03/2013, Letnik:
12, Številka:
1S
Journal Article
Recenzirano
Embedded systems security is a significant requirement in emerging environments, considering the increasing deployment of embedded systems in several application domains. The large number of deployed ...embedded systems, their limited resources and their increasing complexity render systems vulnerable to an increasing number of threats. Additionally, the involvement of sensitive, often private, information and the expectation for safe and dependable embedded platforms lead to strong security requirements, even legal ones, which require new technologies for their provision. In this article, we provide an overview of embedded security issues, used methods and technologies, identifying important challenges in this emerging field.
Security and safety have been traditionally considered as different disciplines addressed by different professionals. However, a close look at their characteristics and their analogies leads to the ...conclusion that they are actually interdependent. Specifically, we argue that safety requires system security and dependability.
Detection and identification of misinformation and fake news is a complex problem that intersects several disciplines, ranging from sociology to computer science and mathematics. In this work, we ...focus on social media analyzing characteristics that are independent of the text language (language-independent) and social context (location-independent) and common to most social media, not only Twitter as mostly analyzed in the literature. Specifically, we analyze temporal and structural characteristics of information flow in the social networks and we evaluate the importance and effect of two different types of features in the detection process of fake rumors. Specifically, we extract epidemiological features exploiting epidemiological models for spreading false rumors; furthermore, we extract graph-based features from the graph structure of the information cascade of the social graph. Using these features, we evaluate them for fake rumor detection with 3 configurations: (i) using only epidemiological features, (ii) using only graph-based features, and (iii) using the combination of epidemiological and graph-based features. Evaluation is performed with a Gradient Boosting classifier on two benchmark fake rumor detection datasets. Our results demonstrate that epidemiological models fit rumor propagation well, while graph-based features lead to more effective classification of rumors; the combination of epidemiological and graph-based features leads to improved performance.
Bacillus simplex is an environmental microorganism found in soil. Herein, we present the case of a 69-year-old Greek male patient who attended the Emergency Department at our hospital. The patient ...complained of lower right extremity swelling and pain, after suffering penetrating trauma whilst doing farm work in a rural area. Swab aerobic cultures revealed Bacillus simplex as identified by MALDI-TOF Mass Spectrometry. The strain was susceptible to vancomycin, imipenem, clindamycin, and ciprofloxacin. Our patient refused hospitalization; therefore, both ciprofloxacin and clindamycin were registered for a total of 19 days. No complications were experienced, and he recovered fully. In our case, the thorough cleaning of the ulcer bed prior to sample collection, the fact that it was the only microorganism isolated, and the wound’s aggravating mechanism led the authors of the present study to the conclusion that B. simplex was the most probable culprit of the infection. To our knowledge, this is the second probable case of B. simplex infection described worldwide, and the first in Greece.
Safe artificial intelligence/machine learning (AI/ML)-enabled systems require design methodologies that accommodate for failures in AI/ML decisions. Certification of AI/ML systems and components will ...increase trust and accelerate adoption, deployment, and use in critical domains.
Connected and automated vehicles (CAVs) are getting a lot of attention these days as their technology becomes more mature and they benefit from the Internet-of-Vehicles (IoV) ecosystem. CAVs attract ...malicious activities that jeopardize security and safety dimensions. The cybersecurity systems of CAVs detect such activities, collect and analyze related information during and after the activity, and use cyber threat intelligence (CTI) to organize this information. Considering that CTI collected from various malicious activities may share common characteristics, it is critical to provide the cybersecurity stakeholders with quick and automatic ways of analysis and interrelation. This aims to help them perform more accurate and effective forensic investigations. To this end, we present CAVeCTIR, a novel approach that finds similarities between CTI reports that describe malicious activities detected on CAVs. CAVeCTIR uses advanced machine learning techniques and provides a quick, automated, and effective solution for clustering similar malicious activities. We applied CAVeCTIR in a series of experiments investigating almost 3000 malicious activities in simulation, real-world, and hybrid CAV environments, covering seven critical cyber-attack scenarios. The results showed that the DBSCAN algorithm identified seven no-overlapping core clusters characterized by high density. The results indicated that cybersecurity stakeholders could take advantage of CAVeCTIR by adopting the same or similar methods to analyze newly detected malicious activity, speed up the attack attribution process, and perform a more accurate forensics investigation.
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