2015
[10]
P. Cooper, T. Crick, T. Tryfonas, G. Oikonomou, "Whole-Life Environmental Impacts of ICT Use", in Proc. 2015 IEEE Globecom Workshops (GC Wkshps), 2015
In this paper we apply a whole-life assessment approach to estimate the environmental impact of the use of ICT of an individual within the UK over a one-year period. By estimating the energy and data consumption of an average user's use of a typical device, and estimating the associated energy usage (and thus CO2 produced) of each stage in the data chain, we are able to calculate the summed CO2 value for embodied carbon of an average device. Overall, device energy is seen to dominate; within device, desktops dominate, both due to their high energy use for a given task, but also their high standby power, which is the most significant point of behaviour-driven waste. Geographical, behavioural and chronological factors are all evaluated to be highly significant to the impact of a user's ICT use, along with a number of secondary factors. Finally, we present policy recommendations to further the understanding of the factors affecting the environmental impact of ICT, particularly focusing on sustainability, resource efficiency and the social implications of ICT in a low-carbon transformation.
[9]
M. Haghighi, K. Maraslis, T. Tryfonas, G. Oikonomou, A. Burrows, P. Woznowski, "Game Theoretic Approach Towards Optimal Multi-tasking and Data-distribution in IoT", in Proc. IEEE World Forum on Internet of Things (WF-IoT), pp. 406-411, 2015
Current applications of Internet of Things (IoT) often require nodes to implement logical decision-making on aggregated data, which involves more processing and wider interactions amongst network peers, resulting in higher energy consumption and shorter node lifetime. This paper presents a game theoretic approach used in Sensomax, an agent-based WSN middleware that facilitates seamless integration of mathematical functions in large-scale wireless sensor networks. In this context, we investigate game theoretic and auction-based techniques to optimise task distribution and energy consumption in IoT networks of multiple concurrent WSNs. We also demonstrate how our proposed game theoretic approach affects the performance of WSN applications with different operational paradigms.
[8]
L. Suzuki, P. Cooper, T. Tryfonas, G. Oikonomou, "Hidden Presence: Sensing Occupancy and Extracting Value from Occupancy Data", in Design, User Experience, and Usability: Interactive Experience Design, ser. Lecture Notes in Computer Science, 9188, pp. 412-424, 2015
In this paper we review various technical architectures for sensing occupancy in commercial real estate spaces and discuss the potential benefits of applications that could be built upon the collected data. The technical capabilities reviewed range from simple presence detection to identifying individual workers and relating those semantically to jobs, teams, processes or other elements of the business. The volume and richness of accumulated data varies accordingly allowing the development of a range of occupancy monitoring applications that could bring multiple benefits to an organization. We find that overall occupancy-based applications are underappreciated in the Smart Buildings mantra due to occupancy’s inability to align to traditional building engineering silos, a lack of common view between stakeholders with respect to what is ‘value’ and the current client assessment tendencies which use predominantly demonstrator-based logic rather than a combination of practical demonstrators and theoretical value. We demonstrate that in commercial office buildings, occupancy-based Smart Building concepts have the potential to deliver benefits that can be orders of magnitude greater than current practice associated with silos such as energy and lighting. The directness of value in these is far more variable however, and the barriers and enablers to its realization are non-trivial. We identify and discuss these factors (including privacy, perceived additional capital expenditure, retrofitting requirements etc.) in more detail and relate them to stages of design and delivery of the built environment. We conclude that, on the presumption costs of development and implementation are relatively similar, the value streams of occupancy-based systems, while requiring more careful and bespoke design in the short term, could produce greater lifetime value in commercial office scenarios than leading smart building technologies.
[7]
T. Spyridopoulos, K. Maraslis, A. Mylonas, T. Tryfonas, G. Oikonomou, "A Game Theoretical Method for Cost-Benefit Analysis of Malware Dissemination Prevention", Information Security Journal: A Global Perspective, Taylor & Francis, 24(4-6), pp. 164-176, 2015
Literature in malware proliferation focuses on modeling and analyzing its spread dynamics. Epidemiology models, which are inspired by the characteristics of biological disease spread in human populations, have been used against this threat to analyze the way malware spreads in a network. This work presents a modified version of the commonly used epidemiology models Susceptible Infected Recovered (SIR) and Susceptible Infected Susceptible (SIS), which incorporates the ability to capture the relationships between nodes within a network, along with their effect on malware dissemination process. Drawing upon a model that illustrates the network’s behavior based on the attacker’s and the defender’s choices, we use game theory to compute optimal strategies for the defender to minimize the effect of malware spread, at the same time minimizing the security cost. We consider three defense mechanisms: patch, removal, and patch and removal, which correspond to the defender’s strategy and use probabilistically with a certain rate. The attacker chooses the type of attack according to its effectiveness and cost. Through the interaction between the two opponents we infer the optimal strategy for both players, known as Nash Equilibrium, evaluating the related payoffs. Hence, our model provides a cost-benefit risk management framework for managing malware spread in computer networks.
[6]
H. Read, K. Xynos, I. Sutherland, F. Roarson, P. Andriotis, G. Oikonomou, "An Extensible Platform for the Forensic Analysis of Social Media Data", in Human Aspects of Information Security, Privacy, and Trust - HAS 2015, ser. Lecture Notes in Computer Science, 9190, pp. 404-414, 2015
Visualising data is an important part of the forensic analysis process. Many cell phone forensic tools have specialised visualisation components, but are as of yet able to tackle questions concerning the broad spectrum of social media communication sources. Visualisation tools tend to be stove-piped, it is difficult to take information seen in one visualisation tool and obtain a different perspective in another tool. If an interesting relationship is observed, needing to be explored in more depth, the process has to be reiterated by manually generating a subset of the data, converting it into the correct format, and invoking the new application. This paper describes a cloud-based data storage architecture and a set of interactive visualisation tools developed to allow for a more straightforward exploratory analysis. This approach developed in this tool suite is demonstrated using a case study consisting of social media data extracted from two mobile devices.
[5]
K. Maraslis, T. Spyridopoulos, G. Oikonomou, T. Tryfonas, M. Haghighi, "Application of a Game Theoretic Approach in Smart Sensor Data Trustworthiness Problems", in Proc. 30th IFIP TC 11 International Conference (SEC), ser. IFIP Advances in Information and Communication Technology, 455, pp. 601-615, 2015
In this work we present an Intrusion Detection (ID) and an Intrusion Prevention (IP) model for Wireless Sensor Networks (WSNs). The attacker’s goal is to compromise the deployment by causing nodes to report faulty sensory information. The defender, who is the WSN’s operator, aims to detect the presence of faulty sensor measurements (ID) and to subsequently recover compromised nodes (IP). In order to address the conflicting interests involved, we adopt a Game Theoretic approach that takes into consideration the strategies of both players and we attempt to identify the presence of Nash Equilibria in the two games. The results are then verified in two simulation contexts: Firstly, we evaluate the model in a middleware-based WSN which uses clustering over a bespoke network stack. Subsequently, we test the model in a simulated IPv6-based sensor deployment. According to the findings, the results of both simulation models confirm the results of the theoretic one.
[4]
M. Haghighi, K. Maraslis, G. Oikonomou, T. Tryfonas, "Game Theoretic Approach Towards Energy - Efficient Task Distribution in Multitasking Wireless Sensor Networks", in Proc. IEEE Sensors 2015, 2015
WSNs have a wide variety of applications, and their usability for remote monitoring of various parameters of interest is growing dramatically. Conventional applications mostly involved a single WSN for collecting raw parameters with limited aggregation on the node side, whereby more sophisticated data mining was implemented by the end-users. Recent applications however, often require more intelligent functions, in which nodes are expected to implement logical decision-makings on the aggregated data. Implementing such functions often involves more processing, and wider interactions amongst network peers, hence resulting in higher energy consumption and shorter node lifetime. Sensomax is an agent-based WSN middleware, which facilitates seamless integration of mathematical functions in large-scale wireless sensor networks. In this paper, we will investigate game theoretic and auction-based techniques in order to optimise task distribution and energy consumption in WSNs.
[3]
B. Chen, Z. Fan, F. Cao, G. Oikonomou, T. Tryfonas, "Class Based Overall Priority Scheduling for M2M Communications over LTE Networks", in Proc. 81st Vehicular Technology Conference (VTC2015-Spring), 2015
The rapidly increasing demand of M2M (Machine to Machine) communications poses great challenges to the capacity of cellular networks. This paper proposes a new M2M scheduling algorithm, namely, Class Based Overall Priority (CBOP) scheduling, which is designed particularly to improve uplink scheduling for a massive number of MTCDs (Machine Type Communication Devices) in LTE networks. We compare the proposed algorithm with several existing scheduling algorithms via simulations and discuss its advantages and limitations.
[2]
P. Andriotis, G. Oikonomou, "Messaging Activity Reconstruction with Sentiment Polarity Identification", in Human Aspects of Information Security, Privacy, and Trust - HAS 2015, ser. Lecture Notes in Computer Science, 9190, pp. 475-486, 2015
Sentiment Analysis aims to extract information related to the emotional state of the person that produced a text document and also describe the sentiment polarity of the short or long message. This kind of information might be useful to a forensic analyst because it provides indications about the psychological state of the person under investigation at a given time. In this paper we use machine-learning algorithms to classify short texts (SMS), which could be found in the internal memory of a smartphone and extract the mood of the person that sent them. The basic goal of our method is to achieve low False Positive Rates. Moreover, we present two visualization schemes with the intention to provide the ability to digital forensic analysts to see graphical representations of the messaging activity of their suspects and therefore focus on specific areas of interest reducing their workload.
[1]
P. Andriotis, T. Tryfonas, G. Oikonomou, I. King, "A framework to describe multimedia circulation in the smartphone ecosystem", in Advances in Digital Forensics XI, ser. IFIP Advances in Information and Communication Technology, 462, pp. 251-267, 2015
Contemporary mobile devices allow almost unrestricted sharing of multimedia and other types of files. But as smartphones and tablets can easily access the Internet or exchange files wirelessly, they've also transformed to useful tools for criminals, aiming at performing illegal activities such as sharing contraband or distributing child abuse images. Thus, the need to investigate the source and destination of a multimedia file that resides in the internal memory of a smartphone becomes apparent. In this paper we present a framework that illustrates and visualizes the flow of digital images as evidence obtained from the artefacts retrieved from Android smartphones during a forensic investigation. Our approach uses `big data' concepts to facilitate the processing of diverse (semi-structured) evidence derived from mobile devices and extends the idea of Digital Evidence Bags (DEB). We obtained our data after running an experiment that included image exchanging through numerous channels such as Bluetooth, Internet and cloud services. Our study presents information about the locations where evidence resides and uses graph databases to store metadata and therefore, visualize the relationships that connect images with apps and events.
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