Characterising anomalous events using change point correlation on unsolicited network traffic

Monitoring unused or dark IP addresses offers opportunities to extract useful information about both on-going and new attack patterns. In recent years, different techniques have been used to analyze such traffic including sequential analysis where a change in traffic behavior, for example change in mean, is used as an indication of malicious activity. Change points themselves say little about detected change; further data processing is necessary for the extraction of useful information and to identify the exact cause of the detected change which is limited due to the size and nature of observed traffic. In this paper, we address the problem of analyzing a large volume of such traffic by correlating change points identified in different traffic parameters. The significance of the proposed technique is two-fold. Firstly, automatic extraction of information related to change points by correlating change points detected across multiple traffic parameters. Secondly, validation of the detected change point by the simultaneous presence of another change point in a different parameter. Using a real network trace collected from unused IP addresses, we demonstrate that the proposed technique enables us to not only validate the change point but also extract useful information about the causes of change points.

Change Point Estimation in Monitoring Survival Time

Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change in the mean survival time of patients who underwent cardiac surgery. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. Markov Chain Monte Carlo is used to obtain posterior distributions of the change point parameters including location and magnitude of changes and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time CUSUM control charts for different magnitude scenarios. The proposed estimator shows a better performance where a longer follow-up period, censoring time, is applied. In comparison with the alternative built-in CUSUM estimator, more accurate and precise estimates are obtained by the Bayesian estimator. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.

Estimation of the time of a linear trend in monitoring survival time

Change point estimation is recognized as an essential tool of root cause analyses within quality control programs as it enables clinical experts to search for potential causes of change in hospital outcomes more effectively. In this paper, we consider estimation of the time when a linear trend disturbance has occurred in survival time following an in-control clinical intervention in the presence of variable patient mix. To model the process and change point, a linear trend in the survival time of patients who underwent cardiac surgery is formulated using hierarchical models in a Bayesian framework. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. We use Markov Chain Monte Carlo to obtain posterior distributions of the change point parameters including the location and the slope size of the trend and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time cumulative sum control chart (CUSUM) control charts for different trend scenarios. In comparison with the alternatives, step change point model and built-in CUSUM estimator, more accurate and precise estimates are obtained by the proposed Bayesian estimator over linear trends. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.

Use of IP addresses for high rate flooding attack detection

High-rate flooding attacks (aka Distributed Denial of Service or DDoS attacks) continue to constitute a pernicious threat within the Internet domain. In this work we demonstrate how using packet source IP addresses coupled with a change-point analysis of the rate of arrival of new IP addresses may be sufficient to detect the onset of a high-rate flooding attack. Importantly, minimizing the number of features to be examined, directly addresses the issue of scalability of the detection process to higher network speeds. Using a proof of concept implementation we have shown how pre-onset IP addresses can be efficiently represented using a bit vector and used to modify a “white list” filter in a firewall as part of the mitigation strategy.

Monitoring and analysis of internet traffic targeting unused address spaces

Today’s evolving networks are experiencing a large number of different attacks ranging from system break-ins, infection from automatic attack tools such as worms, viruses, trojan horses and denial of service (DoS). One important aspect of such attacks is that they are often indiscriminate and target Internet addresses without regard to whether they are bona fide allocated or not. Due to the absence of any advertised host services the traffic observed on unused IP addresses is by definition unsolicited and likely to be either opportunistic or malicious. The analysis of large repositories of such traffic can be used to extract useful information about both ongoing and new attack patterns and unearth unusual attack behaviors. However, such an analysis is difficult due to the size and nature of the collected traffic on unused address spaces. In this dissertation, we present a network traffic analysis technique which uses traffic collected from unused address spaces and relies on the statistical properties of the collected traffic, in order to accurately and quickly detect new and ongoing network anomalies. Detection of network anomalies is based on the concept that an anomalous activity usually transforms the network parameters in such a way that their statistical properties no longer remain constant, resulting in abrupt changes. In this dissertation, we use sequential analysis techniques to identify changes in the behavior of network traffic targeting unused address spaces to unveil both ongoing and new attack patterns. Specifically, we have developed a dynamic sliding window based non-parametric cumulative sum change detection techniques for identification of changes in network traffic. Furthermore we have introduced dynamic thresholds to detect changes in network traffic behavior and also detect when a particular change has ended. Experimental results are presented that demonstrate the operational effectiveness and efficiency of the proposed approach, using both synthetically generated datasets and real network traces collected from a dedicated block of unused IP addresses.

Bayesian models for longitudinal data

Longitudinal data, where data are repeatedly observed or measured on a temporal basis of time or age provides the foundation of the analysis of processes which evolve over time, and these can be referred to as growth or trajectory models. One of the traditional ways of looking at growth models is to employ either linear or polynomial functional forms to model trajectory shape, and account for variation around an overall mean trend with the inclusion of random eects or individual variation on the functional shape parameters. The identification of distinct subgroups or sub-classes (latent classes) within these trajectory models which are not based on some pre-existing individual classification provides an important methodology with substantive implications. The identification of subgroups or classes has a wide application in the medical arena where responder/non-responder identification based on distinctly diering trajectories delivers further information for clinical processes. This thesis develops Bayesian statistical models and techniques for the identification of subgroups in the analysis of longitudinal data where the number of time intervals is limited. These models are then applied to a single case study which investigates the neuropsychological cognition for early stage breast cancer patients undergoing adjuvant chemotherapy treatment from the Cognition in Breast Cancer Study undertaken by the Wesley Research Institute of Brisbane, Queensland. Alternative formulations to the linear or polynomial approach are taken which use piecewise linear models with a single turning point, change-point or knot at a known time point and latent basis models for the non-linear trajectories found for the verbal memory domain of cognitive function before and after chemotherapy treatment. Hierarchical Bayesian random eects models are used as a starting point for the latent class modelling process and are extended with the incorporation of covariates in the trajectory profiles and as predictors of class membership. The Bayesian latent basis models enable the degree of recovery post-chemotherapy to be estimated for short and long-term followup occasions, and the distinct class trajectories assist in the identification of breast cancer patients who maybe at risk of long-term verbal memory impairment.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

The Brain Research Institute (BRI) uses various types of indirect measurements, including EEG and fMRI, to understand and assess brain activity and function. As well as the recovery of generic information about brain function, research also focuses on the utilisation of such data and understanding to study the initiation, dynamics, spread and suppression of epileptic seizures. To assist with the future focussing of this aspect of their research, the BRI asked the MISG 2010 participants to examine how the available EEG and fMRI data and current knowledge about epilepsy should be analysed and interpreted to yield an enhanced understanding about brain activity occurring before, at commencement of, during, and after a seizure. Though the deliberations of the study group were wide ranging in terms of the related matters considered and discussed, considerable progress was made with the following three aspects. (1) The science behind brain activity investigations depends crucially on the quality of the analysis and interpretation of, as well as the recovery of information from, EEG and fMRI measurements. A number of specific methodologies were discussed and formalised, including independent component analysis, principal component analysis, profile monitoring and change point analysis (hidden Markov modelling, time series analysis, discontinuity identification). (2) Even though EEG measurements accurately and very sensitively record the onset of an epileptic event or seizure, they are, from the perspective of understanding the internal initiation and localisation, of limited utility. They only record neuronal activity in the cortical (surface layer) neurons of the brain, which is a direct reflection of the type of electrical activity they have been designed to record. Because fMRI records, through the monitoring of blood flow activity, the location of localised brain activity within the brain, the possibility of combining fMRI measurements with EEG, as a joint inversion activity, was discussed and examined in detail. (3) A major goal for the BRI is to improve understanding about ``when'' (at what time) an epileptic seizure actually commenced before it is identified on an eeg recording, ``where'' the source of this initiation is located in the brain, and ``what'' is the initiator. Because of the general agreement in the literature that, in one way or another, epileptic events and seizures represent abnormal synchronisations of localised and/or global brain activity the modelling of synchronisations was examined in some detail. References C. M. Michel, G. Thut, S. Morand, A. Khateb, A. J. Pegna, R. Grave de Peralta, S. Gonzalez, M. Seeck and T. Landis, Electric source imaging of human brain functions, Brain Res. 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Quality issues for the National Bowel Cancer Screening Program (NBCSP)

Purpose: This two-part research project was undertaken as part of the planning process by Queensland Health (QH), Cancer Screening Services Unit (CSSU), Queensland Bowel Cancer Screening Program (QBCSP), in partnership with the National Bowel Cancer Screening Program (NBCSP), to prepare for the implementation of the NBCSP in public sector colonoscopy services in QLD in late 2006. There was no prior information available on the quality of colonoscopy services in Queensland (QLD) and no prior studies that assessed the quality of colonoscopy training in Australia. Furthermore, the NBCSP was introduced without extra funding for colonoscopy service improvement or provision for increases in colonoscopic capacity resulting from the introduction of the NBCSP. The main purpose of the research was to record baseline data on colonoscopy referral and practice in QLD and current training in colonoscopy Australia-wide. It was undertaken from a quality improvement perspective. Implementation of the NBCSP requires that all aspects of the screening pathway, in particular colonoscopy services for the assessment of positive Faecal Occult Blood Tests (FOBTs), will be effective, efficient, equitable and evidence-based. This study examined two important aspects of the continuous quality improvement framework for the NBCSP as they relate to colonoscopy services: (1) evidence-based practice, and (2) quality of colonoscopy training. The Principal Investigator was employed as Senior Project Officer (Training) in the QBCSP during the conduct of this research project. Recommendations from this research have been used to inform the development and implementation of quality improvement initiatives for provision of colonoscopy in the NBCSP, its QLD counterpart the QBCSP and colonoscopy services in QLD, in general. Methods – Part 1 Chart audit of evidence-based practice: The research was undertaken in two parts from 2005-2007. The first part of this research comprised a retrospective chart audit of 1484 colonoscopy records (some 13% of all colonoscopies conducted in public sector facilities in the year 2005) in three QLD colonoscopy services. Whilst some 70% of colonoscopies are currently conducted in the private sector, only public sector colonoscopy facilities provided colonoscopies under the NBCSP. The aim of this study was to compare colonoscopy referral and practice with explicit criteria derived from the National Health & Medical Research Council (NHMRC) (1999) Clinical Practice Guidelines for the Prevention, Early Detection and Management of Colorectal Cancer, and describe the nature of variance with the guidelines. Symptomatic presentations were the most common indication for colonoscopy (60.9%). These comprised per rectal bleeding (31.0%), change of bowel habit (22.1%), abdominal pain (19.6%), iron deficiency anaemia (16.2%), inflammatory bowel disease (8.9%) and other symptoms (11.4%). Surveillance and follow-up colonoscopies accounted for approximately one-third of the remaining colonoscopy workload across sites. Gastroenterologists (GEs) performed relatively more colonoscopies per annum (59.9%) compared to general surgeons (GS) (24.1%), colorectal surgeons (CRS) (9.4%) and general physicians (GPs) (6.5%). Guideline compliance varied with the designation of the colonoscopist. Compliance was lower for CRS (62.9%) compared to GPs (76.0%), GEs (75.0%), GSs (70.9%, p<0.05). Compliance with guideline recommendations for colonoscopic surveillance for family history of colorectal cancer (23.9%), polyps (37.0%) and a past history of bowel cancer (42.7%), was by comparison significantly lower than for symptomatic presentations (94.4%), (p<0.001). Variation with guideline recommendations occurred more frequently for polyp surveillance (earlier than guidelines recommend, 47.9%) and follow-up for past history of bowel cancer (later than recommended, 61.7%, p<0.001). Bowel cancer cases detected at colonoscopy comprised 3.6% of all audited colonoscopies. Incomplete colonoscopies occurred in 4.3% of audited colonoscopies and were more common among women (76.6%). For all colonoscopies audited, the rate of incomplete colonoscopies for GEs was 1.6% (CI 0.9-2.6), GPs 2.0% (CI 0.6-7.2), GS 7.0% (CI 4.8-10.1) and CRS 16.4% (CI 11.2-23.5). 18.6% (n=55) of patients with a documented family history of bowel cancer had colonoscopy performed against guidelines recommendations (for general (category 1) population risk, for reasons of patient request or family history of polyps, rather than for high risk status for colorectal cancer). In general, family history was inadequately documented and subsequently applied to colonoscopy referral and practice. Methods - Part 2 Surveys of quality of colonoscopy training: The second part of the research consisted of Australia-wide anonymous, self-completed surveys of colonoscopy trainers and their trainees to ascertain their opinions on the current apprenticeship model of colonoscopy in Australia and to identify any training needs. Overall, 127 surveys were received from colonoscopy trainers (estimated response rate 30.2%). Approximately 50% of trainers agreed and 27% disagreed that current numbers of training places were adequate to maintain a skilled colonoscopy workforce in preparation for the NBCSP. Approximately 70% of trainers also supported UK-style colonoscopy training within dedicated accredited training centres using a variety of training approaches including simulation. A collaborative approach with the private sector was seen as beneficial by 65% of trainers. Non-gastroenterologists (non-GEs) were more likely than GEs to be of the opinion that simulators are beneficial for colonoscopy training (χ2-test = 5.55, P = 0.026). Approximately 60% of trainers considered that the current requirements for recognition of training in colonoscopy could be insufficient for trainees to gain competence and 80% of those indicated that ≥ 200 colonoscopies were needed. GEs (73.4%) were more likely than non-GEs (36.2%) to be of the opinion that the Conjoint Committee standard is insufficient to gain competence in colonoscopy (χ2-test = 16.97, P = 0.0001). The majority of trainers did not support training either nurses (73%) or GPs in colonoscopy (71%). Only 81 (estimated response rate 17.9%) surveys were received from GS trainees (72.1%), GE trainees (26.3%) and GP trainees (1.2%). The majority were males (75.9%), with a median age 32 years and who had trained in New South Wales (41.0%) or Victoria (30%). Overall, two-thirds (60.8%) of trainees indicated that they deemed the Conjoint Committee standard sufficient to gain competency in colonoscopy. Between specialties, 75.4% of GS trainees indicated that the Conjoint Committee standard for recognition of colonoscopy was sufficient to gain competence in colonoscopy compared to only 38.5% of GE trainees. Measures of competency assessed and recorded by trainees in logbooks centred mainly on caecal intubation (94.7-100%), complications (78.9-100%) and withdrawal time (51-76.2%). Trainees described limited access to colonoscopy training lists due to the time inefficiency of the apprenticeship model and perceived monopolisation of these by GEs and their trainees. Improvements to the current training model suggested by trainees included: more use of simulation, training tools, a United Kingdom (UK)-style training course, concentration on quality indicators, increased access to training lists, accreditation of trainers and interdisciplinary colonoscopy training. Implications for the NBCSP/QBCSP: The introduction of the NBCSP/QBCSP necessitates higher quality colonoscopy services if it is to achieve its ultimate goal of decreasing the incidence of morbidity and mortality associated with bowel cancer in Australia. This will be achieved under a new paradigm for colonoscopy training and implementation of evidence-based practice across the screening pathway and specifically targeting areas highlighted in this thesis. Recommendations for improvement of NBCSP/QBCSP effectiveness and efficiency include the following: 1. Implementation of NBCSP and QBCSP health promotion activities that target men, in particular, to increase FOBT screening uptake. 2. Improved colonoscopy training for trainees and refresher courses or retraining for existing proceduralists to improve completion rates (especially for female NBCSP/QBCSP participants), and polyp and adenoma detection and removal, including newer techniques to detect flat and depressed lesions. 3. Introduction of colonoscopy training initiatives for trainees that are aligned with NBCSP/QBCSP colonoscopy quality indicators, including measurement of training outcomes using objective quality indicators such as caecal intubation, withdrawal time, and adenoma detection rate. 4. Introduction of standardised, interdisciplinary colonoscopy training to reduce apparent differences between specialties with regard to compliance with guideline recommendations, completion rates, and quality of polypectomy. 5. Improved quality of colonoscopy training by adoption of a UK-style training program with centres of excellence, incorporating newer, more objective assessment methods, use of a variety of training tools such as simulation and rotations of trainees between metropolitan, rural, and public and private sector training facilities. 6. Incorporation of NHMRC guidelines into colonoscopy information systems to improve documentation, provide guideline recommendations at the point of care, use of gastroenterology nurse coordinators to facilitate compliance with guidelines and provision of guideline-based colonoscopy referral letters for GPs. 7. Provision of information and education about the NBCSP/QBCSP, bowel cancer risk factors, including family history and polyp surveillance guidelines, for participants, GPs and proceduralists. 8. Improved referral of NBCSP/QBCSP participants found to have a high-risk family history of bowel cancer to appropriate genetics services.