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

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

A Deep Learning Approach to Network Intrusion Detection

Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow-based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL-KDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the forty-one features of NSL-KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow-based anomaly detection in SDN environments

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

Secure Proximity-Based Identity Pairing using an Untrusted Signalling Service

New protocols such as WebRTC promise seamless in-browser peer-to-peer communications that in theory remove the need for third-party services. In practice, widespread use of Firewalls, NATS and dynamic IP addresses mean that overlay addressing or use of some fixed rendezvous point is still needed. In this paper we describe a proximity-based pairing scheme that uses a signalling service to minimise the trust requirements on the third party, achieving anonymity and avoiding the need for PKI, while still requiring only a simple asymmetric pairing protocol

Statistical analysis driven optimized deep learning system for intrusion detection

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks has made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods (human-in-the-loop), followed by a deep autoencoder for potential threat detection. Specifically, a pre-processing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discard features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018

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