Predicting the Effects of DDoS Attacks on a Network of Critical Infrastructures

Over the last decade, the level of critical infrastructure technology has been steadily transforming in order to keep pace with the growing demand for the services offered. The implementation of the smart grid, which relies on a complex and intelligent level of interconnectivity, is one example of how vital amenity provision is being refined. However, with this change, the risk of threats from the digital domain must be calculated. Superior interconnectivity between infrastructures means that the future cascading impacts of successful cyber-attacks are unknown. One such threat being faced in the digital domain is the Distributed Denial of Service (DDoS) attack. A DDoS has the goal of incapacitating a server, network or service, by barraging a target with external data traffic in the form of communication requests. DDoS have the potential to cause a critical infrastructure outage, and the subsequent impact on a network of such infrastructures is yet unknown. In this paper, an approach for assessing the future impacts of a cyber-attack in a network of critical infrastructures is presented; with a focus on DDoS attacks. A simulation of a critical infrastructure network provides data to represent both normal run-time and an attack scenario. Using this dataset, a technique for assessing the future impact of disruptions on integrated critical infrastructure network, is demonstrated. Index Terms—Critical Infrastructure, Cyber-Attack Distributed Denial of Service, Simulation, Cascading Failur

Time-Pattern Profiling from Smart Meter Data to Detect Outliers in Energy Consumption

Smart meters have become a core part of the Internet of Things, and its sensory network is increasing globally. For example, in the UK there are over 15 million smart meters operating across homes and businesses. One of the main advantages of the smart meter installation is the link to a reduction in carbon emissions. Research shows that, when provided with accurate and real-time energy usage readings, consumers are more likely to turn off unneeded appliances and change other behavioural patterns around the home (e.g., lighting, thermostat adjustments). In addition, the smart meter rollout results in a lessening in the number of vehicle callouts for the collection of consumption readings from analogue meters and a general promotion of renewable sources of energy supply. Capturing and mining the data from this fully maintained (and highly accurate) sensing network, provides a wealth of information for utility companies and data scientists to promote applications that can further support a reduction in energy usage. This research focuses on modelling trends in domestic energy consumption using density-based classifiers. The technique estimates the volume of outliers (e.g., high periods of anomalous energy consumption) within a social class grouping. To achieve this, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points to Identify the Clustering Structure (OPTICS) and Local Outlier Factor (LOF) demonstrate the detection of unusual energy consumption within naturally occurring groups with similar characteristics. Using DBSCAN and OPTICS, 53 and 208 outliers were detected respectively; with 218 using LOF, on a dataset comprised of 1,058,534 readings from 1026 homes

Dinosaur tracks in Triassic Molteno sediments: the earliest evidence of dinosaurs in South Africa?

A fossil tracksite containing well-preserved tridactyl footprints of bipedal theropod dinosaurs is reported from fluvial overbank deposits of Molteno age (Stormberg Group: Triassic) in the northeastern Cape Province, South Africa. They occur stratigraphically below the mudrocks of the Elliot Formation, in which dinosaur remains are comparatively common, and are taken to represent the earliest evidence for dinosaurs in South Africa. They also represent the earliest unequivocal evidence of tetrapods in Molteno deposits.Foundation for Research Development; Trustees of the Port Elizabeth Museu

Digital Memories Based Mobile User Authentication for IoT

The increasing number of devices within the IoT is raising concerns over the efficiency and exploitability of existing authentication methods. The weaknesses of such methods, in particular passwords, are well documented. Although alternative methods have been proposed, they often rely on users being able to accurately recall complex and often unmemorable information. With the profusion of separate online accounts, this can often be a difficult task. The emerging digital memories concept involves the creation of a repository of memories specific to individuals. We believe this abundance of personal data can be utilised as a form of authentication. In this paper, we propose our digital memories based two-factor authentication mechanism, and also present our promising evaluation results. Keywords—Digital memories, authentication, IoT, securit

MICRO-CI: A Testbed for Cyber-Security Research

A significant challenge for governments around the globe is the need to improve the level of awareness for citizens and businesses about the threats that exist in cyberspace. The arrival of new information technologies has resulted in different types of criminal activities, which previously did not exist, with the potential to cause extensive damage. Given the fact that the Internet is boundary-less, it makes it difficult to identify where attacks originate from and how to counter them. The only solution is to improve the level of support for security systems and evolve the defences against cyber-attacks. This project supports the development of critical infrastructure security research, in the fight against a growing threat from the digital domain. However, the real-world evaluation of emerging security systems for Supervisory Control and Data Acquisition (SCADA) systems is impractical. The research project furthers the knowledge and understanding of Information Systems; specifically acting as a facilitator for cyber-security research. In this paper, the construction of a testbed and datasets for cyber-security and critical infrastructure research are presented

A Case Study on the Advantages of 3D Walkthroughs over Photo Stitching Techniques

Virtual tours and interactive walkthroughs enable a more in-depth platform for communicating information. Many current techniques employ the use of Photo Stitching to accomplish this. However, over the last decade advancements in computing power and the accessibility of game engines, meant that developing rich 3D content for virtual tours is more possible than ever before. As such, the purpose of this paper is to present a study into the advantages of developing an interactive 3D virtual tour of student facilities, using the Unreal Development 4 Game Engine, for educational establishments. The project aims to demonstrate a comparison between the use of Photo Stitching and 3D Modelled interactive walkthrough for developing rich visual environments. The research reveals that the approach in this paper can improve educational facilities prominence within universities, and contains many advantages over Photo Stitching techniques

Micro-CI: A Model Critical Infrastructure Testbed for Cyber-Security Training and Research

Critical infrastructures encompass various sectors, such as energy resources and manufacturing, which tend to be dispersed over large geographic areas. With recent technological advancements over the last decade, they have developed to be dependent on Information and Communication Technology (ICT); where control systems and the use of sensor equipment facilitate operation. However, the persistently evolving global state of ICT has resulted in the emergence of sophisticated cyber-threats. As dependence upon critical infrastructure systems continues to increase, so too does the urgency with which these systems need to be adequately protected. Modelling and testbed development are now crucial for the study and analysis of security within critical infrastructures; particularly as testing within a live system can have far-reaching impacts, including potential loss of life. Existing testbed approaches are not replicable or involve the use of simulation, which impacts upon the realism of the datasets constructed. As such, the research presented in this paper discusses the novel development of a replicable and affordable critical infrastructure testbed for cyber-security training and research. The testbed can be used to anticipate cyber-security incidents and assist in the development of new and innovative cyber-security methods. The access to real-world data for training, research and testing new design methodologies is a challenge for security researchers; as such, the aim of this project is to provide an original methodology for the construction of accessible data for cyber-security research. The testbed data is evaluated through a comparison with a simulation comprised of the same components

An Ensemble Detection Model using Multinomial Classification of Stochastic Gas Smart Meter Data to Improve Wellbeing Monitoring in Smart Cities

Fuel poverty has a negative impact on the wellbeing of individuals within a household; affecting not only comfort levels but also increased levels of seasonal mortality. Wellbeing solutions within this sector are moving towards identifying how the needs of people in vulnerable situations can be improved or monitored by means of existing supply networks and public institutions. Therefore, the focus of this research is towards wellbeing monitoring solution, through the analysis of gas smart meter data. Gas smart meters replace the traditional analogue electro-mechanical and diaphragm-based meters that required regular reading. They have received widespread popularity over the last 10 years. This is primarily due to the fact that by using this technology, customers are able to adapt their consumption behaviours based on real-time information provided by In-Home Devices. Yet, the granular nature of the datasets generated has also meant that this technology is ideal for further scalable wellbeing monitoring applications. For example, the autonomous detection of households at risk of energy poverty is possible and of growing importance in order to face up to the impacts of fuel poverty, quality of life and wellbeing of low-income housing. However, despite their popularity (smart meters), the analysis of gas smart meter data has been neglected. In this paper, an ensemble model is proposed to achieve autonomous detection, supported by four key measures from gas usage patterns, consisting of i) a tariff detection, ii) a temporally-aware tariff detection, iii) a routine consumption detection and iv) an age-group detection. Using a cloud-based machine learning platform, the proposed approach yielded promising classification results of up to 84.1% Area Under Curve (AUC), when the Synthetic Minority Over-sampling Technique (SMOTE) was utilised