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

The impact of body vigilance on help-seeking for cancer 'alarm' symptoms: a community-based survey.

BACKGROUND: The act of detecting bodily changes is a pre-requisite for subsequent responses to symptoms, such as seeking medical help. This is the first study to explore associations between self-reported body vigilance and help-seeking in a community sample currently experiencing cancer 'alarm' symptoms. METHODS: Using a cross-sectional study design, a 'health survey' was mailed through primary care practices to 4913 UK adults (age ≥50 years, no cancer diagnosis), asking about symptom experiences and medical help-seeking over the previous three months. Body vigilance, cancer worry and current illness were assessed with a small number of self-report items derived from existing measures. RESULTS: The response rate was 42% (N = 2042). Almost half the respondents (936/2042; 46%) experienced at least one cancer alarm symptom. Results from logistic regression analysis revealed that paying more attention to bodily changes was significantly associated with help-seeking for cancer symptoms (OR = 1.44; 1.06-1.97), after controlling for socio-demographics, current illness and cancer worry. Being more sensitive to bodily changes was not significantly associated with help-seeking. CONCLUSIONS: Respondents who paid attention to their bodily changes were more likely to seek help for their symptoms. Although the use of a cross-sectional study design and the limited assessment of key variables preclude any firm conclusions, encouraging people to be body vigilant may contribute towards earlier cancer diagnosis. More needs to be understood about the impact this might have on cancer-related anxiety

Elevated expression of artemis in human fibroblast cells is associated with cellular radiosensitivity and increased apoptosis

Copyright @ 2012 Nature Publishing GroupThis article has been made available through the Brunel Open Access Publishing Fund.Background: The objective of this study was to determine the molecular mechanism(s) responsible for cellular radiosensitivity in two human fibroblast cell lines 84BR and 175BR derived from two cancer patients. Methods: Clonogenic assays were performed following exposure to increasing doses of gamma radiation to confirm radiosensitivity. γ-H2AX foci assays were used to determine the efficiency of DNA double strand break (DSB) repair in cells. Quantitative-PCR (Q-PCR) established the expression levels of key DNA DSB repair proteins. Imaging flow cytometry using Annexin V-FITC was used to compare artemis expression and apoptosis in cells. Results: Clonogenic cellular hypersensitivity in the 84BR and 175BR cell lines was associated with a defect in DNA DSB repair measured by the γ-H2AX foci assay. Q-PCR analysis and imaging flow cytometry revealed a two-fold overexpression of the artemis DNA repair gene which was associated with an increased level of apoptosis in the cells before and after radiation exposure. Over-expression of normal artemis protein in a normal immortalised fibroblast cell line NB1-Tert resulted in increased radiosensitivity and apoptosis. Conclusion: We conclude elevated expression of artemis is associated with higher levels of DNA DSB, radiosensitivity and elevated apoptosis in two radio-hypersensitive cell lines. These data reveal a potentially novel mechanism responsible for radiosensitivity and show that increased artemis expression in cells can result in either radiation resistance or enhanced sensitivity.This work was supported in part by The Vidal Sassoon Foundation USA. This article is made available through the Brunel Open Access Publishing Fund

Quantifying habitat selection and variability in habitat suitability for juvenile white sharks

While adult white sharks (Carcharodon carcharias) are apex predators with a circumglobal distribution, juvenile white sharks (JWS) feed primarily on bottom dwelling fishes and tend to be coastally associated. Despite the assumedly easier access to juveniles compared to large, migratory adults, limited information is available on the movements, environments, and distributions of individuals during this life stage. To quantify movement and understand their distribution in the southern California Bight, JWS were captured and fitted with dorsal fin-mounted satellite transmitters (SPOT tags; n = 18). Nine individuals crossed the U.S. border into Baja California, Mexico. Individuals used shallow habitats (134.96 +/- 191.1 m) close to shore (7.16 +/- 5.65 km). A generalized linear model with a binomial distribution was used to predict the presence of individuals based on several environmental predictors from these areas. Juveniles were found to select shallow habitats (\u3c 1000 m deep) close to land (\u3c 30 km of the shoreline) in waters ranging from 14 to 24 degrees C. Southern California was found to be suitable eight months of the year, while coastal habitats in Baja California were suitable year-round. The model predicted seasonal movement with sharks moving from southern California to Baja California during winter. Additionally, habitat distribution changed inter annually with sharks having a more northerly distribution during years with a higher Pacific Decadal Oscillation index, suggesting sharks may forego their annual fall migrations to Baja California, Mexico, during El Nino years. Model predictions aligned with fishery-dependent catch data, with a greater number of sharks being captured during periods and/or areas of increased habitat suitability. Thus, habitat models could be useful for predicting the presence of JWS in other areas, and can be used as a tool for potentially reducing fishery interactions during seasons and locations where there is increased susceptibility of incidental catch

Disentangled Representations for Domain-generalized Cardiac Segmentation

Robust cardiac image segmentation is still an open challenge due to the inability of the existing methods to achieve satisfactory performance on unseen data of different domains. Since the acquisition and annotation of medical data are costly and time-consuming, recent work focuses on domain adaptation and generalization to bridge the gap between data from different populations and scanners. In this paper, we propose two data augmentation methods that focus on improving the domain adaptation and generalization abilities of state-to-the-art cardiac segmentation models. In particular, our "Resolution Augmentation" method generates more diverse data by rescaling images to different resolutions within a range spanning different scanner protocols. Subsequently, our "Factor-based Augmentation" method generates more diverse data by projecting the original samples onto disentangled latent spaces, and combining the learned anatomy and modality factors from different domains. Our extensive experiments demonstrate the importance of efficient adaptation between seen and unseen domains, as well as model generalization ability, to robust cardiac image segmentation.Comment: Accepted by STACOM 202

On the Numerical Evaluation of Loop Integrals With Mellin-Barnes Representations

An improved method is presented for the numerical evaluation of multi-loop integrals in dimensional regularization. The technique is based on Mellin-Barnes representations, which have been used earlier to develop algorithms for the extraction of ultraviolet and infrared divergencies. The coefficients of these singularities and the non-singular part can be integrated numerically. However, the numerical integration often does not converge for diagrams with massive propagators and physical branch cuts. In this work, several steps are proposed which substantially improve the behavior of the numerical integrals. The efficacy of the method is demonstrated by calculating several two-loop examples, some of which have not been known before.Comment: 13 pp. LaTe

Refinement of metabolite detection in cystic fibrosis sputum reveals heme correlates with lung function decline

The bacterial growth environment within cystic fibrosis (CF) sputum is complex, dynamic, and shaped by both host and microbial processes. Characterization of the chemical parameters within sputum that stimulate the in vivo growth of airway pathogens (e.g. Pseudomonas aeruginosa) and their associated virulence factors may lead to improved CF treatment strategies. Motivated by conflicting reports of the prevalence and abundance of P. aeruginosa-derived metabolites known as phenazines within CF airway secretions, we sought to quantify these metabolites in sputum using quadrupole time-of-flight mass spectrometry. In contrast to our previous work, all phenazines tested (pyocyanin (PYO), phenazine-1-carboxylic acid (PCA), phenazine-1-carboxamide, and 1-hydroxyphenazine) were below detection limits of the instrument (0.1 μM). Instead, we identified a late-eluting compound that shared retention time and absorbance characteristics with PCA, yet generated mass spectra and a fragmentation pattern consistent with ferriprotoporphyrin IX, otherwise known as heme B. These data suggested that UV-vis chromatographic peaks previously attributed to PCA and PYO in sputum were mis-assigned. Indeed, retrospective analysis of raw data from our prior study found that the heme B peak closely matched the peaks assigned to PCA, indicating that the previous study likely uncovered a positive correlation between pulmonary function (percent predicted forced expiratory volume in 1 second, or ppFEV1) and heme B, not PCA or any other phenazine. To independently test this observation, we performed a new tandem mass-spectrometry analysis of 71 additional samples provided by the Mountain West CF Consortium Sputum Biomarker study and revealed a positive correlation (ρ = −0.47, p<0.001) between sputum heme concentrations and ppFEV1. Given that hemoptysis is strongly associated with airway inflammation, pulmonary exacerbations and impaired lung function, these new data suggest that heme B may be a useful biomarker of CF pathophysiology

A Definitive Signal of Multiple Supersymmetry Breaking

If the lightest observable-sector supersymmetric particle (LOSP) is charged and long-lived, then it may be possible to indirectly measure the Planck mass at the LHC and provide a spectacular confirmation of supergravity as a symmetry of nature. Unfortunately, this proposal is only feasible if the gravitino is heavy enough to be measured at colliders, and this condition is in direct conflict with constraints from big bang nucleosynthesis (BBN). In this work, we show that the BBN bound can be naturally evaded in the presence of multiple sectors which independently break supersymmetry, since there is a new decay channel of the LOSP to a goldstino. Certain regions of parameter space allow for a direct measurement of LOSP decays into both the goldstino and the gravitino at the LHC. If the goldstino/gravitino mass ratio is measured to be 2, as suggested by theory, then this would provide dramatic verification of the existence of multiple supersymmetry breaking and sequestering. A variety of consistent cosmological scenarios are obtained within this framework. In particular, if an R symmetry is imposed, then the gauge-gaugino-goldstino interaction vertices can be forbidden. In this case, there is no bound on the reheating temperature from goldstino overproduction, and thermal leptogenesis can be accommodated consistently with gravitino dark matter.Comment: 10 pages, 5 figures, title changed to match the version published in JHE

Improving the sensitivity of Higgs boson searches in the golden channel

Leptonic decays of the Higgs boson in the ZZ* channel yield what is known as the golden channel due to its clean signature and good total invariant mass resolution. In addition, the full kinematic distribution of the decay products can be reconstructed, which, nonetheless, is not taken into account in traditional search strategy relying only on measurements of the total invariant mass. In this work we implement a type of multivariate analysis known as the matrix element method, which exploits differences in the full production and decay matrix elements between the Higgs boson and the dominant irreducible background from q bar{q} -> ZZ*. Analytic expressions of the differential distributions for both the signal and the background are also presented. We perform a study for the Large Hadron Collider at sqrt{s}=7 TeV for Higgs masses between 175 and 350 GeV. We find that, with an integrated luminosity of 2.5 fb^-1 or higher, improvements in the order of 10 - 20 % could be obtained for both discovery significance and exclusion limits in the high mass region, where the differences in the angular correlations between signal and background are most pronounced.Comment: 31 pages, 8 figures. v2: Minus signs in definitions of angles corrected. Typos fixed. Reference added. Cosmetic changes to Figure 4. Additional sentence added for clarificatio