In this paper we explore the problem of data coun-terfeiting in the context of data trading platforms for industrial data. We focus on the methods and mechanisms that allow data trading platforms to determine whether a dataset is original and/or authentic and whether it conflicts with the licence terms of existing data. We present the state of the art methods for data similarity suited for the various types of data and we present the functionalities and architecture of a data trading platform module that safeguards against data counterfeiting for newly introduced datasets within the platform.

Over the past few years, there has been a noticeable transition from centralized Industrial Control Systems (ICS) to distributed systems. However, the challenges of distributed systems (e.g., communication delays and packet loss) can give rise to undesired situations like deadlocks, which malicious actors may target. To address such conditions, implementing run-time security monitoring can ensure these systems’ reliable and secure operation. In this paper, we introduce a novel approach for run-time security monitoring for distributed ICS, extending previous works that focus on autonomous and centralized systems. Our approach makes it possible to specify physical and cyber resources and their changing limitations within a distributed environment. By doing so, our approach offers a valuable contribution to ICS security by tackling limitations introduced by distributed ICS. Furthermore, it provides an efficient mechanism for monitoring the security of these systems in real time.

We are currently witnessing an increased occurrence of extreme weather events, causing a great deal of disruption and distress across the globe. In this setting, the importance and utility of Early Warning Systems is becoming increasingly obvious. In this work, we present the design of an early warning system called TransCPEarlyWarning, aimed at seven countries in the Adriatic-Ionian area in Europe. The overall objective is to increase the level of cooperation among national civil protection institutions in these countries, addressing natural and man-made risks from the early warning stage and improving the intervention capabilities of civil protection mechanisms. The system utilizes an innovative approach with a lever effect, while also aiming to support the whole system of Civil Protection.

Abstract The publication and (re)utilization of Open Data is still facing multiple barriers on technical, organizational and legal levels. This includes limitations in interfaces, search capabilities, provision of quality information and the lack of definite standards and implementation guidelines. Many Semantic Web specifications and technologies are specifically designed to address the publication of data on the web. In addition, many official publication bodies encourage and foster the development of Open Data standards based on Semantic Web principles. However, no existing solution for managing Open Data takes full advantage of these possibilities and benefits. In this paper, we present our solution “Piveau”, a fully-fledged Open Data management solution, based on Semantic Web technologies. It harnesses a variety of standards, like RDF, DCAT, DQV, and SKOS, to overcome the barriers in Open Data publication. The solution puts a strong focus on assuring data quality and scalability. We give a detailed description of the underlying, highly scalable, service-oriented architecture, how we integrated the aforementioned standards, and used a triplestore as our primary database. We have evaluated our work in a comprehensive feature comparison to established solutions and through a practical application in a production environment, the European Data Portal. Our solution is available as Open Source.

The detection of malicious hardware logic (hardware Trojan) requires test patterns that succeed in exciting the malicious logic part. Testing of all possible input patterns is often prohibitively expensive. As an alternative, we explored previously the applicability of the combinatorial testing principles. In this paper, we turn our focus on the efficiency of this approach for triggering the hidden malicious logic. We present a series of experiments with Trojan designs of various activation patterns and lengths that target a cryptographic module performing AES cryptography. Our findings indicate that the available test suites succeed in triggering the malicious logic in all cases requiring only a very small number of test vectors. Thus, it is an efficient means for detecting malicious hardware logic.

A Transient Effect Ring Oscillator (TERO) is a special case of a Ring Oscillator (RO) design that exhibits increased sensitivity to intrinsic noise. It can serve as a basis for implementing a True Random Number Generator (TRNG) or a Physically Uncloneable Function (PUF). Also, as a digital sensor for detecting insertion of malicious hardware logic (Trojans) in digital circuits. Here, we explore the application of TERO for detecting hardware Trojans injected in FPGA implementations of the AES cryptographic algorithm. Experiments and comparisons are reported in terms of the frequency as a function of the TERO length. Our findings indicate that TERO-based digital sensors can be used to efficiently detect the presence of the Trojan.

Logistics or supply-chain services provide enterprises and organizations with the necessary level of flexibility and efficiency in order to retain competitiveness under the increasingly turbulent e-business area.Web-Services are utilized by organizations in order to integrate high and low level applications, thus providing a collaborative environment without affecting inter-and intra-enterprise processes.Nevertheless, the above context should be enhanced in order to comply with the Web-of-Things concept.This paper describes a sustainable approach towards the above requirement by employing ONS based services able to provide targeted information regarding RFID-enabled physical objects that are handled in an organization agnostic collaborative environment.

A spammer is making profit and stays in business even when a tiny fraction of recipients replies to spam messages. Despite enormous effort put in spam identification and elimination, spam is still dominating our inbox. We seek a novel and complementary approach to force spammers stop sending unsolicited messages. Our approach is to artificially inflate the number of available recipients by contaminating spammers' databases with addresses not monitored by human beings. Thus, we aim to drastically reduce the number of messages delivered to human beings as to reduce the response rate.