Konstantinos Railis | National Technical University of Athens (original) (raw)

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Papers by Konstantinos Railis

Signals, Sep 2, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Electrocardiogram (ECG) analysis has been established as a key element regarding the evaluation o... more Electrocardiogram (ECG) analysis has been established as a key element regarding the evaluation of the human health status. The computational complexity along with the strict constraints of real-time assessment of a heart beat, has made the ECG analysis flow a very challenging application for embedded medical devices. Recent advancements in cyber-physical and IoT systems are transforming medical processing towards embedded and wearable devices, thus making energy consumption a first class design objective. In this work, we focus on analysing the power, performance and energy profiles of an ECG analysis and arrhythmia detection software pipeline during its execution on a ZYNQ-based SoC. We evaluate a large set of design alternatives spanning from a pure software-only implementation to HW/SW oriented designs, in which High-Level Synthesis capabilities are utilized. Using the medically validated MIT-BIH ECG database, we examine the efficiency and the sensitivity of the design solutions...

Intrusion detection plays a critical role in cyber-security domain since malicious attacks cause ... more Intrusion detection plays a critical role in cyber-security domain since malicious attacks cause irreparable damages to cyber-systems. In this work, we propose the I2SP prototype, which is a novel Information Sharing Platform, able to gather, pre-process, model, and distribute network-traffic information. Within the I2SP prototype we build several challenging deep feature learning models for network-traffic intrusion detection. The learnt representations will be utilized for classifying each new network measurement into its corresponding threat level. We evaluate our prototype’s performance by conducting case studies using cyber-security data extracted from the Malware Information Sharing Platform (MISP)-API. To the best of our knowledge, we are the first that combine the MISP-API in order to construct an information sharing mechanism that supports multiple novel deep feature learning architectures for intrusion detection. Experimental results justify that the proposed deep feature ...

2018 21st Euromicro Conference on Digital System Design (DSD)

Current partially or even no automated in-hospital processes often allow unacceptable errors to o... more Current partially or even no automated in-hospital processes often allow unacceptable errors to occur, the effects of which may span from simple injury to mortality. Recent advances in the Internet of Things, smart tags and cloud technologies may decisively alter this fact and minimize such "never events". In this paper, MATISSE is presented as a smart hospital ecosystem, aiming to decisively decrease "never events" in the hospital value chain. Real-time drugs/pills lifetime/aptness verification and medication administration, patients' identification and association with drugs and medical exams, as well as real-time quality assessment are supported in the proposed ecosystem. The ecosystem leverages the exploitation of information lying in (dynamic) smart tags attached to physical and virtual entities of the ecosystem, as well as the integration of a smart medication cart. The paper presents the supported use cases and the architectural design of the proposed solution, while it provides insights in both the hardware and software assembly of the smart medication cart.

2016 26th International Workshop on Power and Timing Modeling, Optimization and Simulation (PATMOS)

IEEE Open Journal of the Communications Society

Intrusion detection plays a critical role in cyber-security domain since malicious attacks cause ... more Intrusion detection plays a critical role in cyber-security domain since malicious attacks cause irreparable damages to cybersystems. In this work, we propose the I2SP prototype, which is a novel Information Sharing Platform, able to gather, preprocess, model, and distribute network-traffic information. Within the I2SP prototype we build several challenging deep feature learning models for network-traffic intrusion detection. The learnt representations will be utilized for classifying each new network measurement into its corresponding threat level. We evaluate our prototype's performance by conducting case studies using cybersecurity data extracted from the Malware Information Sharing Platform (MISP)-API. To the best of our knowledge, we are the first that combine the MISP-API in order to construct an information sharing mechanism that supports multiple novel deep feature learning architectures for intrusion detection. Experimental results justify that the proposed deep feature learning techniques are able to predict accurately MISP threat-levels.

Signals, Sep 2, 2022

2018 21st Euromicro Conference on Digital System Design (DSD)

2016 26th International Workshop on Power and Timing Modeling, Optimization and Simulation (PATMOS)

IEEE Open Journal of the Communications Society

Intrusion detection plays a critical role in cyber-security domain since malicious attacks cause ... more Intrusion detection plays a critical role in cyber-security domain since malicious attacks cause irreparable damages to cybersystems. In this work, we propose the I2SP prototype, which is a novel Information Sharing Platform, able to gather, preprocess, model, and distribute network-traffic information. Within the I2SP prototype we build several challenging deep feature learning models for network-traffic intrusion detection. The learnt representations will be utilized for classifying each new network measurement into its corresponding threat level. We evaluate our prototype's performance by conducting case studies using cybersecurity data extracted from the Malware Information Sharing Platform (MISP)-API. To the best of our knowledge, we are the first that combine the MISP-API in order to construct an information sharing mechanism that supports multiple novel deep feature learning architectures for intrusion detection. Experimental results justify that the proposed deep feature learning techniques are able to predict accurately MISP threat-levels.