7,784 research outputs found
Anomaly Detection in BACnet/IP managed Building Automation Systems
Building Automation Systems (BAS) are a collection of devices and software which manage the operation of building services. The BAS market is expected to be a $19.25 billion USD industry by 2023, as a core feature of both the Internet of Things and Smart City technologies. However, securing these systems from cyber security threats is an emerging research area. Since initial deployment, BAS have evolved from isolated standalone networks to heterogeneous, interconnected networks allowing external connectivity through the Internet. The most prominent BAS protocol is BACnet/IP, which is estimated to hold 54.6% of world market share. BACnet/IP security features are often not implemented in BAS deployments, leaving systems unprotected against known network threats. This research investigated methods of detecting anomalous network traffic in BACnet/IP managed BAS in an effort to combat threats posed to these systems.
This research explored the threats facing BACnet/IP devices, through analysis of Internet accessible BACnet devices, vendor-defined device specifications, investigation of the BACnet specification, and known network attacks identified in the surrounding literature. The collected data were used to construct a threat matrix, which was applied to models of BACnet devices to evaluate potential exposure. Further, two potential unknown vulnerabilities were identified and explored using state modelling and device simulation.
A simulation environment and attack framework were constructed to generate both normal and malicious network traffic to explore the application of machine learning algorithms to identify both known and unknown network anomalies. To identify network patterns between the generated normal and malicious network traffic, unsupervised clustering, graph analysis with an unsupervised community detection algorithm, and time series analysis were used. The explored methods identified distinguishable network patterns for frequency-based known network attacks when compared to normal network traffic. However, as stand-alone methods for anomaly detection, these methods were found insufficient. Subsequently, Artificial Neural Networks and Hidden Markov Models were explored and found capable of detecting known network attacks. Further, Hidden Markov Models were also capable of detecting unknown network attacks in the generated datasets.
The classification accuracy of the Hidden Markov Models was evaluated using the Matthews Correlation Coefficient which accounts for imbalanced class sizes and assess both positive and negative classification ability for deriving its metric. The Hidden Markov Models were found capable of repeatedly detecting both known and unknown BACnet/IP attacks with True Positive Rates greater than 0.99 and Matthews Correlation Coefficients greater than 0.8 for five of six evaluated hosts.
This research identified and evaluated a range of methods capable of identifying anomalies in simulated BACnet/IP network traffic. Further, this research found that Hidden Markov Models were accurate at classifying both known and unknown attacks in the evaluated BACnet/IP managed BAS network
Mapping the Evolution of "Clusters": A Meta-analysis
This paper presents a meta-analysis of the âcluster literatureâ contained in scientific journals from 1969 to 2007. Thanks to an original database we study the evolution of a stream of literature which focuses on a research object which is both a theoretical puzzle and an empirical widespread evidence. We identify different growth stages, from take-off to development and maturity. We test the existence of a life-cycle within the authorships and we discover the existence of a substitutability relation between different collaborative behaviours. We study the relationships between a âspatialâ and an âindustrialâ approach within the textual corpus of cluster literature and we show the existence of a âpredatoryâ interaction. We detect the relevance of clustering behaviours in the location of authors working on clusters and in measuring the influence of geographical distance in co-authorship. We measure the extent of a convergence process of the vocabulary of scientists working on clusters.Cluster, Life-Cycle, Cluster Literature, Textual Analysis, Agglomeration, Co-Authorship
Using Reputation Systems and Non-Deterministic Routing to Secure Wireless Sensor Networks
Security in wireless sensor networks is difficult to achieve because of the resource limitations of the sensor nodes. We propose a trust-based decision framework for wireless sensor networks coupled with a non-deterministic routing protocol. Both provide a mechanism to effectively detect and confine common attacks, and, unlike previous approaches, allow bad reputation feedback to the network. This approach has been extensively simulated, obtaining good results, even for unrealistically complex attack scenarios
Lung Cancer Screening Using Adaptive Memory-Augmented Recurrent Networks
In this paper, we investigate the effectiveness of deep learning techniques
for lung nodule classification in computed tomography scans. Using less than
10,000 training examples, our deep networks perform two times better than a
standard radiology software. Visualization of the networks' neurons reveals
semantically meaningful features that are consistent with the clinical
knowledge and radiologists' perception. Our paper also proposes a novel
framework for rapidly adapting deep networks to the radiologists' feedback, or
change in the data due to the shift in sensor's resolution or patient
population. The classification accuracy of our approach remains above 80% while
popular deep networks' accuracy is around chance. Finally, we provide in-depth
analysis of our framework by asking a radiologist to examine important
networks' features and perform blind re-labeling of networks' mistakes
Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010
This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb.
UCLâs research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010.
The overarching theme this year was âGlobal Challengesâ, with specific focus on the following themes:
* Crime and Place
* Environmental Change
* Intelligent Transport
* Public Health and Epidemiology
* Simulation and Modelling
* London as a global city
* The geoweb and neo-geography
* Open GIS and Volunteered Geographic Information
* Human-Computer Interaction and GIS
Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond
Caroline: An Autonomously Driving Vehicle for Urban Environments
The 2007 DARPA Urban Challenge afforded the golden opportunity for the
Technische Universit\"at Braunschweig to demonstrate its abilities to develop
an autonomously driving vehicle to compete with the world's best competitors.
After several stages of qualification, our team CarOLO qualified early for the
DARPA Urban Challenge Final Event and was among only eleven teams from
initially 89 competitors to compete in the final. We had the ability to work
together in a large group of experts, each contributing his expertise in his
discipline, and significant organisational, financial and technical support by
local sponsors who helped us to become the best non-US team. In this report, we
describe the 2007 DARPA Urban Challenge, our contribution "Caroline", the
technology and algorithms along with her performance in the DARPA Urban
Challenge Final Event on November 3, 2007.Comment: 68 pages, 7 figure
Fuzzy Logic
The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems
A Structural Analysis of Social Representations of "Reconciliation" in Cyprus: An Empirical Contribution
In the Cyprus peace process, the efforts of Civil Society Organisations (CSOs) facilitate the practices of co-existence, or intergroup contact between the Turkish Cypriot (TC) and Greek Cypriot (GC) communities. Considering this context, the original contribution of this study is to employ the theoretical framework of social representations (SRs), to understand the internal structure SR of reconciliation in Cyprus. Moreover, this study intends to explore the reconciliation SRs of those taking part in CSOs (n=30) and of the laypeople (n=40). This study analyses power relations in both competing and convergent SRs between different groups of actors. It presents insights drawing upon fieldwork that was carried out in Cyprus using a convenient and purposive sampling method. Methodologically we choose the Central Nucleus Theory (Abric, 1976). This oft-used methodological approach is based on the Hierarchised Evocations tool: a task of word association starting from the inductor "Reconciliation", followed by a justification questionnaire (Galli, Fasanelli & Schember, 2018) as well as a classification of the associated terms (Vergès, 1992). Data were analysed using both prototypical and similitude analyses processed by IRaMuTeQ, an interface of R, and through the perspective of iterative factorial cluster analysis for binary data (iFCB). The results indicate that the structure of the social representations of reconciliation demonstrated by all subsamples reflects their social construct. It appears that, due to their activism, the CSO subsample has a higher degree of quality and quantity of collaboration in both the in-group and the intergroup, compared to the laypeople. The CSO subsample engaging in in-group and intergroup collaborations tends to show trust and a more tolerant attitude for engaging in collaborative work. Their representation of reconciliation reflects the line of activism and positive attitudes towards cooperation for a shared future. Meanwhile, the TCC and GCC laypeople subsamples have a shared hope for peace, looking forward to having a more comfortable life. They highlight the coexistence contributed to by emotional efforts (forgiveness, empathy, and the spread of love to reconnect and come to an agreement). Moreover, all subsamples share the common representation that reconciliation corresponding to peace as a common goal
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