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

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

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

First passage behaviour of fractional Brownian motion in two-dimensional wedge domains

We study the survival probability and the corresponding first passage time density of fractional Brownian motion confined to a two-dimensional open wedge domain with absorbing boundaries. By analytical arguments and numerical simulation we show that in the long time limit the first passage time density scales as t**{-1+pi*(2H-2)/(2*Theta)} in terms of the Hurst exponent H and the wedge angle Theta. We discuss this scaling behaviour in connection with the reaction kinetics of FBM particles in a one-dimensional domain.Comment: 6 pages, 4 figure

Stochastic Opinion Formation in Scale-Free Networks

The dynamics of opinion formation in large groups of people is a complex non-linear phenomenon whose investigation is just at the beginning. Both collective behaviour and personal view play an important role in this mechanism. In the present work we mimic the dynamics of opinion formation of a group of agents, represented by two state $\pm 1$, as a stochastic response of each of them to the opinion of his/her neighbours in the social network and to feedback from the average opinion of the whole. In the light of recent studies, a scale-free Barab\'asi-Albert network has been selected to simulate the topology of the interactions. A turbulent-like dynamics, characterized by an intermittent behaviour, is observed for a certain range of the model parameters. The problem of uncertainty in decision taking is also addressed both from a topological point of view, using random and targeted removal of agents from the network, and by implementing a three state model, where the third state, zero, is related to the information available to each agent. Finally, the results of the model are tested against the best known network of social interactions: the stock market. A time series of daily closures of the Dow Jones index has been used as an indicator of the possible applicability of our model in the financial context. Good qualitative agreement is found.Comment: 24 pages and 13 figures, Physical Review E, in pres

Extreme value statistics and return intervals in long-range correlated uniform deviates

We study extremal statistics and return intervals in stationary long-range correlated sequences for which the underlying probability density function is bounded and uniform. The extremal statistics we consider e.g., maximum relative to minimum are such that the reference point from which the maximum is measured is itself a random quantity. We analytically calculate the limiting distributions for independent and identically distributed random variables, and use these as a reference point for correlated cases. The distributions are different from that of the maximum itself i.e., a Weibull distribution, reflecting the fact that the distribution of the reference point either dominates over or convolves with the distribution of the maximum. The functional form of the limiting distributions is unaffected by correlations, although the convergence is slower. We show that our findings can be directly generalized to a wide class of stochastic processes. We also analyze return interval distributions, and compare them to recent conjectures of their functional form