Background: Telocytes (TCs) are unique interstitial or stromal cells of mesodermal origin, defined by long cellular extensions called telopodes (Tps) which form a network, connecting them to surrounding cells. TCs were previously found around stem and progenitor cells, and were thought to be most likely involved in local tissue metabolic equilibrium and regeneration. The roles of telocytes are still under scientific scrutiny, with existing studies suggesting they possess various functions depending on their location. Methods: Human myometrium biopsies were collected from pregnant and non-pregnant women, telocytes were then investigated in myometrial interstitial cell cultures based on morphological criteria and later prepared for time-lapse microscopy. Semi-analytical and numerical solutions were developed to highlight the geometric characteristics and the behavior of telocytes. Results: Results were gathered in a database which would further allow efficient telocyte tracking and indexing in a content-based image retrieval (CBIR) of digital medical images. Mathematical analysis revealed pivotal information regarding the homogeneity, hardness and resistance of telocytes’ structure. Cellular activity models were monitored in vitro, therefore supporting the creation of databases of telocyte images. Conclusions: The obtained images were analyzed, using segmentation techniques and mathematical models in conjunction with computer simulation, in order to depict TCs behavior in relation to surrounding cells. This paper brings an important contribution to the development of bioinformatics systems by creating software-based telocyte models that could be used both for diagnostic and educational purposes.
The wide spread of cyber-attacks made the need of gathering as much information as possible about them, a real demand in nowadays global context. The honeypot systems have become a powerful tool on the way to accomplish that. Researchers have already focused on the development of various honeypot systems but the fact that their administration is time consuming made clear the need of self-adaptive honeypot system capable of learning from their interaction with attackers. This paper presents a self-adaptive honeypot system we are developing that tries to overlap some of the disadvantaged that existing systems have. The proposed honeypot is a medium interaction system developed using Python and it emulates a SSH (Secure Shell) server. The system is capable of interacting with the attackers by means of reinforcement learning algorithms.
The latest technological progress in the industrial sector has led to a paradigm shift in manufacturing efficiency and operational cost reduction. More often than not, this cost reduction comes at the price of dismissing information security, especially when multiple stakeholders are involved and the complexity increases. As a further matter, most of the legacy systems and smart factoring processes lack a security by design approach, making them highly vulnerable to cyber-attacks. Taking into consideration the aforementioned issues, we propose an architectural framework for Industrial Internet of Things (IIoT) that provides authentication and guaranteed integrity. Our proposal properly addresses the security by design principle while combining some of the emerging technologies like Secure Multi-Party Computation (SMPC) for grounded policy rules and Distributed Ledger Technology (DLT) for an immutable and transparent registry.
The Internet of Things has become a cutting-edge technology that is continuously evolving in size, connectivity, and applicability. This ecosystem makes its presence felt in every aspect of our lives, along with all other emerging technologies. Unfortunately, despite the significant benefits brought by the IoT, the increased attack surface built upon it has become more critical than ever. Devices have limited resources and are not typically created with security features. Lately, a trend of botnet threats transitioning to the IoT environment has been observed, and an army of infected IoT devices can expand quickly and be used for effective attacks. Therefore, identifying proper solutions for securing IoT systems is currently an important and challenging research topic. Machine learning-based approaches are a promising alternative, allowing the identification of abnormal behaviors and the detection of attacks. This paper proposes an anomaly-based detection solution that uses unsupervised deep learning techniques to identify IoT botnet activities. An empirical evaluation of the proposed method is conducted on both balanced and unbalanced datasets to assess its threat detection capability. False-positive rate reduction and its impact on the detection system are also analyzed. Furthermore, a comparison with other unsupervised learning approaches is included. The experimental results reveal the performance of the proposed detection method.
In an era of fully digitally interconnected people and machines, IoT devices become a real target for attackers. Recent incidents such as the well-known Mirai botnet, have shown that the risks incurred are huge and therefore a risk assessment is mandatory. In this paper we present a novel approach on collecting relevant data about IoT attacks. We detail a SSH/Telnet honeypot system that leverages reinforcement learning algorithms in order to interact with the attackers, and we present the results obtained in view of defining optimal reward functions to be used. One of the key issues regarding the performance of such algorithms is the direct dependence on the reward functions used. The main outcome of our study is a full implementation of an IoT honeypot system that leverages Apprenticeship Learning using Inverse Reinforcement Learning, in order to generate best suited reward functions.
Cyber security exercises are a very effective way of learning the practical aspects of information security. Designing and implementing a cyber security exercise is a complex task because it can have many forms and approaches. The paper presents a step-by-step implementation of a cyber security exercise including design considerations for each of the steps and following a structured approach. The paper can be used as a guide for institutions who want to organize cyber security exercises for training purposes.
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