A survey of intrusion detection system technologies

This paper provides an overview of IDS types and how they work as well as configuration considerations and issues that affect them. Advanced methods of increasing the performance of an IDS are explored such as specification based IDS for protecting Supervisory Control And Data Acquisition (SCADA) and Cloud networks. Also by providing a review of varied studies ranging from issues in configuration and specific problems to custom techniques and cutting edge studies a reference can be provided to others interested in learning about and developing IDS solutions. Intrusion Detection is an area of much required study to provide solutions to satisfy evolving services and networks and systems that support them. This paper aims to be a reference for IDS technologies other researchers and developers interested in the field of intrusion detection

Experience in using a typed functional language for the development of a security application

In this paper we present our experience in developing a security application using a typed functional language. We describe how the formal grounding of its semantic and compiler have allowed for a trustworthy development and have facilitated the fulfillment of the security specification.Comment: In Proceedings F-IDE 2014, arXiv:1404.578

Enabling quantitative data analysis through e-infrastructures

This paper discusses how quantitative data analysis in the social sciences can engage with and exploit an e-Infrastructure. We highlight how a number of activities which are central to quantitative data analysis, referred to as ‘data management’, can benefit from e-infrastructure support. We conclude by discussing how these issues are relevant to the DAMES (Data Management through e-Social Science) research Node, an ongoing project that aims to develop e-Infrastructural resources for quantitative data analysis in the social sciences

Utilising semantic technologies for decision support in dementia care

The main objective of this work is to discuss our experience in utilising semantic technologies for building decision support in Dementia care systems that are based on the non-intrusive on the non-intrusive monitoring of the patient’s behaviour. Our approach adopts context-aware modelling of the patient’s condition to facilitate the analysis of the patient’s behaviour within the inhabited environment (movement and room occupancy patterns, use of equipment, etc.) with reference to the semantic knowledge about the patient’s condition (history of present of illness, dependable behaviour patterns, etc.). The reported work especially focuses on the critical role of the semantic reasoning engine in inferring medical advice, and by means of practical experimentation and critical analysis suggests important findings related to the methodology of deploying the appropriate semantic rules systems, and the dynamics of the efficient utilisation of complex event processing technology in order to the meet the requirements of decision support for remote healthcare systems

Evaluation of Rumble Stripes on Low-Volume Rural Roads in Iowa—Phase II Final Report, November 2011

Single-vehicle run-off-road crashes are the most common crash type on rural two-lane Iowa roads. Rumble strips have proven effective in mitigating these crashes, but the strips are commonly installed in paved shoulders on higher-volume roads that are owned by the State of Iowa. Lower-volume paved rural roads owned by local agencies do not commonly feature paved shoulders but frequently experience run-off-road crashes. This project involved installing rumble stripes, which are a combination of conventional rumble strips with a painted edge line placed on the surface of the milled area, along the edge of the travel lanes, but at a narrow width to avoid possible intrusion into the normal vehicle travel paths. The research described in this report was part of a project funded by the Federal Highway Administration, Iowa Highway Research Board, and Iowa Department of Transportation to evaluate the effectiveness of edge-line rumble strips in Iowa. The project evaluated the effectiveness of rumble stripes in reducing run-off-road crashes and in improving the longevity and wet-weather visibility of edge-line markings. This project consisted of two phases. The first phase was to select pilot study locations, select a set of test sites, install rumble stripes, summarize lessons learned during installation, and provide a preliminary assessment of the rumble stripes’ performance. The purpose of this report was to document results from Phase II. A before and after crash analysis was conducted to assess whether use of the treatment had resulted in fewer crashes. However, due to low sample size, results of the analysis were inconclusive. Lateral position was also evaluated before and after installation of the treatment to determine whether vehicles engaged in better lane keeping. Pavement marking wear was also assessed

Quantum key distribution with an efficient countermeasure against correlated intensity fluctuations in optical pulses

Quantum key distribution (QKD) allows two distant parties to share secret keys with the proven security even in the presence of an eavesdropper with unbounded computational power. Recently, GHz-clock decoy QKD systems have been realized by employing ultrafast optical communication devices. However, security loopholes of high-speed systems have not been fully explored yet. Here we point out a security loophole at the transmitter of the GHz-clock QKD, which is a common problem in high-speed QKD systems using practical band-width limited devices. We experimentally observe the inter-pulse intensity correlation and modulation-pattern dependent intensity deviation in a practical high-speed QKD system. Such correlation violates the assumption of most security theories. We also provide its countermeasure which does not require significant changes of hardware and can generate keys secure over 100 km fiber transmission. Our countermeasure is simple, effective and applicable to wide range of high-speed QKD systems, and thus paves the way to realize ultrafast and security-certified commercial QKD systems

Tiresias: Predicting Security Events Through Deep Learning

With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (e.g., whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task