A Feasibility Study on the Application of the ScriptGenE Framework as an Anomaly Detection System in Industrial Control Systems

Recent events such as Stuxnet and the Shamoon Aramco have brought to light how vulnerable industrial control systems (ICSs) are to cyber attacks. Modern society relies heavily on critical infrastructure, including the electric power grid, water treatment facilities, and nuclear energy plants. Malicious attempts to disrupt, destroy and disable such systems can have devastating effects on a populations way of life, possibly leading to loss of life. The need to implement security controls in the ICS environment is more vital than ever. ICSs were not originally designed with network security in mind. Today, intrusion detection systems are employed to detect attacks that penetrate the ICS network. This research proposes the use of a novel algorithm known as the ScriptGenE framework as an anomaly-based intrusion detection system. The anomaly detection system (ADS) is implemented between an engineering workstation and programmable logic controller to monitor traffic and alert the operator to anomalous behavior. The ADS achieves true positive rates of 0.9011 and 1.00 with false positive rates of 0 and 0.054. This research demonstrates the viability of using the ScriptGenE framework as an anomaly detection system in a simulated ICS environment

Genetic Classification of Populations using Supervised Learning

There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case--control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed \emph{unsupervised}. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available. In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.Comment: Accepted PLOS On

Genetically predicted complement component 4A expression: effects on memory function and middle temporal lobe activation

Background The longstanding association between the major histocompatibility complex (MHC) locus and schizophrenia (SZ) risk has recently been accounted for, partially, by structural variation at the complement component 4 (C4) gene. This structural variation generates varying levels of C4 RNA expression, and genetic information from the MHC region can now be used to predict C4 RNA expression in the brain. Increased predicted C4A RNA expression is associated with the risk of SZ, and C4 is reported to influence synaptic pruning in animal models. Methods Based on our previous studies associating MHC SZ risk variants with poorer memory performance, we tested whether increased predicted C4A RNA expression was associated with reduced memory function in a large (n = 1238) dataset of psychosis cases and healthy participants, and with altered task-dependent cortical activation in a subset of these samples. Results We observed that increased predicted C4A RNA expression predicted poorer performance on measures of memory recall (p = 0.016, corrected). Furthermore, in healthy participants, we found that increased predicted C4A RNA expression was associated with a pattern of reduced cortical activity in middle temporal cortex during a measure of visual processing (p < 0.05, corrected). Conclusions These data suggest that the effects of C4 on cognition were observable at both a cortical and behavioural level, and may represent one mechanism by which illness risk is mediated. As such, deficits in learning and memory may represent a therapeutic target for new molecular developments aimed at altering C4’s developmental role

ZNF804A risk allele is associated with relatively intact gray matter volume in patients with schizophrenia

ZNF804A rs1344706 is the first genetic risk variant to achieve genome wide significance for psychosis. Following earlier evidence that patients carrying the ZNF804A risk allele had relatively spared memory function compared to patient non-carriers, we investigated whether ZNF804A was also associated with variation in brain volume. In a sample of 70 patients and 38 healthy participants we used voxel based morphometry to compare homozygous (AA) carriers of the ZNF804A risk allele to heterozygous and homozygous (AC/CC) non-carriers for both whole brain volume and specific regions implicated in earlier ZNF804A studies-the dorsolateral pre-frontal cortex, the hippocampus, and the amygdala. For patients, but not for controls, we found that homozygous 'AA' risk carriers had relatively larger gray matter volumes than heterozygous/homozygous non-carriers (AC/CC), particularly for hippocampal volumes. These data are consistent with our earlier behavioral data and suggest that ZNF804A is delineating a schizophrenia subtype characterized by relatively intact brain volume. Establishing if this represents a discrete molecular pathogenesis with consequences for nosology and treatment will be an important next step in understanding ZNF084A's role in illness risk

Disrupted in schizophrenia 1 (DISC1) L100P mutants have impaired activity-dependent plasticity in vivo and in vitro

Major neuropsychiatric disorders are genetically complex but share overlapping etiology. Mice mutant for rare, highly penetrant risk variants can be useful in dissecting the molecular mechanisms involved. The gene disrupted in schizophrenia 1 (DISC1) has been associated with increased risk for neuropsychiatric conditions. Mice mutant for Disc1 display morphological, functional and behavioral deficits that are consistent with impairments observed across these disorders. Here we report that Disc1 L100P mutants are less able to reorganize cortical circuitry in response to stimulation in vivo. Molecular analysis reveals that the mutants have a reduced expression of PSD95 and pCREB in visual cortex and fail to adjust expression of such markers in response to altered stimulation. In vitro analysis shows that mutants have impaired functional reorganization of cortical neurons in response to selected forms of neuronal stimulation, but there is no altered basal expression of synaptic markers. These findings suggest that DISC1 has a critical role in the reorganization of cortical plasticity and that this phenotype becomes evident only under challenge, even at early postnatal stages. This result may represent an important etiological mechanism in the emergence of neuropsychiatric disorders

Genetic modifiers and subtypes in schizophrenia: Investigations of age at onset, severity, sex and family history

Schizophrenia is a genetically and clinically heterogeneous disorder. Genetic risk factors for the disorder may differ between the sexes or between multiply affected families compared to cases with no family history. Additionally, limited data support a genetic basis for variation in onset and severity, but specific loci have not been identified. We performed genome-wide association studies (GWAS) examining genetic influences on age at onset (AAO) and illness severity as well as specific risk by sex or family history status using up to 2762 cases and 3187 controls from the International Schizophrenia Consortium (ISC)