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

Association of Alpha B-Crystallin Genotypes with Oral Cancer Susceptibility, Survival, and Recurrence in Taiwan

BACKGROUND: Alpha B-crystallin (CRYAB) is a protein that functions as "molecular chaperone" in preserving intracellular architecture and cell membrane. Also, CRYAB is highly antiapoptotic. Abnormal CRYAB expression is a prognostic biomarker for oral cancer, while its genomic variations and the association with carcinogenesis have never been studied. METHODOLOGY/FINDING: Therefore, we hypothesized that CRYAB single nucleotide polymorphisms may be associated with oral cancer risk. In this hospital-based study, the association of CRYAB A-1215G (rs2228387), C-802G (rs14133) and intron2 (rs2070894) polymorphisms with oral cancer in a Taiwan population was investigated. In total, 496 oral cancer patients and 992 age- and gender-matched healthy controls were genotyped and analyzed. A significantly different frequency distribution was found in CRYAB C-802G genotypes, but not in A-1215G and intron2 genotypes, between the oral cancer and control groups. The CRYAB C-802G G allele conferred an increased risk of oral cancer (P = 1.49×10(-5)). Patients carrying CG/GG at CRYAB C-802G were of lower 5-year survival and higher recurrence rate than those of CC (P<0.05). CONCLUSION/SIGNIFICANCE: Our results provide the first evidence that the G allele of CRYAB C-802G is correlated with oral cancer risk and this polymorphism may be a useful marker for oral cancer recurrence and survival prediction for clinical reference

Pro-angiogenic effects of chalcone derivatives in zebrafish embryos in vivo

[[incitationindex]]SCI[[booktype]]電子

Genetic polymorphisms of DNA double strand break gene Ku70 and gastric cancer in Taiwan

<p>Abstract</p> <p>Background and aim</p> <p>The DNA repair gene <it>Ku70</it>, an important member of non-homologous end-joining repair system, is thought to play an important role in the repairing of DNA double strand breaks. It is known that defects in double strand break repair capacity can lead to irreversible genomic instability. However, the polymorphic variants of <it>Ku70</it>, have never been reported about their association with gastric cancer susceptibility.</p> <p>Methods</p> <p>In this hospital-based case-control study, the associations of <it>Ku70 </it>promoter T-991C (rs5751129), promoter G-57C (rs2267437), promoter A-31G (rs132770), and intron 3 (rs132774) polymorphisms with gastric cancer risk in a Taiwanese population were investigated. In total, 136 patients with gastric cancer and 560 age- and gender-matched healthy controls recruited from the China Medical Hospital in Taiwan were genotyped.</p> <p>Results</p> <p>As for <it>Ku70 </it>promoter T-991C, the ORs after adjusted by age and gender of the people carrying TC and CC genotypes were 2.41 (95% CI = 1.53-3.88) and 3.21 (95% CI = 0.96-9.41) respectively, compared to those carrying TT wild-type genotype. The <it>P </it>for trend was significant (<it>P </it>< 0.0001). In the dominant model (TC plus CC versus TT), the association between <it>Ku70 </it>promoter T-991C polymorphism and the risk for gastric cancer was also significant (adjusted OR = 2.48, 95% CI = 1.74-3.92). When stratified by age and gender, the association was restricted to those at the age of 55 or elder of age (TC vs TT: adjusted OR = 2.52, 95% CI = 1.37-4.68, <it>P </it>= 0.0139) and male (TC vs TT: adjusted OR = 2.58, 95% CI = 1.33-4.47, <it>P </it>= 0.0085). As for the other three polymorphisms, there was no difference between both groups in the distributions of their genotype frequencies.</p> <p>Conclusion</p> <p>In conclusion, the <it>Ku70 </it>promoter T-991C (rs5751129), but not the <it>Ku70 </it>promoter C-57G (rs2267437), promoter A-31G (rs132770) or intron 3 (rs132774), is associated with gastric cancer susceptibility. This polymorphism may be a novel useful marker for gastric carcinogenesis.</p

Detection of Anomalous Traffic Patterns and Insight Analysis from Bus Trajectory Data

© 2019, Springer Nature Switzerland AG. Detection of anomalous patterns from traffic data is closely related to analysis of traffic accidents, fault detection, flow management, and new infrastructure planning. Existing methods on traffic anomaly detection are modelled on taxi trajectory data and have shortcoming that the data may lose much information about actual road traffic situation, as taxi drivers can select optimal route for themselves to avoid traffic anomalies. We employ bus trajectory data as it reflects real traffic conditions on the road to detect city-wide anomalous traffic patterns and to provide broader range of insights into these anomalies. Taking these considerations, we first propose a feature visualization method by mapping extracted 3-dimensional hidden features to red-green-blue (RGB) color space with a deep sparse autoencoder (DSAE). A color trajectory (CT) is produced by encoding a trajectory with RGB colors. Then, a novel algorithm is devised to detect spatio-temporal outliers with spatial and temporal properties extracted from the CT. We also integrate the CT with the geographic information system (GIS) map to obtain insights for understanding the traffic anomaly locations, and more importantly the road influence affected by the corresponding anomalies. Our proposed method was tested on three real-world bus trajectory data sets to demonstrate the excellent performance of high detection rates and low false alarm rates

Giant Enhancement of Polarization and Strong Improvement of Retention in Epitaxial Ba0.6_{0.6}Sr0.4_{0.4}TiO3_{3}-Based Nanocomposites

In Ba$_{0.6}$Sr$_{0.4}$TiO$_{3}$ (BSTO)-based epitaxial nanocomposite films increased P r values are demonstrated by up to a factor of 3 compared to standard BSTO films. A strongly reduced temperature coefficient of polarization retention is also obtained, i.e., 0.07% °C$^{−1}$ compared to 0.24% °C$^{−1}$ . Piezopoling with only marginal leakage current is also achieved up to 200 °C, the highest temperature studied. The origin of the improved performance is the incorporation of Sm$_{2}$O$_{3}$ nanopillars in the films which acted as stiff vertical nanoscaffolds, inducing a strong tetragonal distortion in the BSTO (up to 1.033(7) in terms of the out-of-plane/in-plane lattice dimensions). The films have comparable performance to industry-standard Pb(Zr,Ti)O$_{3}$ films, at the same time as being Pb-free

Defining Rules for Kinematic Shapes with Variable Spatial Relations

Designing mechanisms can be a challenging problem, because the underlying kinematics involved are typically not intuitively incorporated into common techniques for design representation. Kinematic shapes and kinematic grammars build on the shape grammar and making grammar formalisms to enable a visually intuitive approach to model and explore mechanisms. With reference to the lower kinematic pairs this paper introduces kinematic shapes. These are connected shapes with parts which have variable spatial relations that account for the relative motion of the parts. The paper considers how such shapes can be defined, the role of elements shared by connected parts, and the motions that result. It also considers how kinematic shape rules can be employed to generate and explore the motion of mechanisms

A critical review of PASBio's argument structures for biomedical verbs

BACKGROUND: Propositional representations of biomedical knowledge are a critical component of most aspects of semantic mining in biomedicine. However, the proper set of propositions has yet to be determined. Recently, the PASBio project proposed a set of propositions and argument structures for biomedical verbs. This initial set of representations presents an opportunity for evaluating the suitability of predicate-argument structures as a scheme for representing verbal semantics in the biomedical domain. Here, we quantitatively evaluate several dimensions of the initial PASBio propositional structure repository. RESULTS: We propose a number of metrics and heuristics related to arity, role labelling, argument realization, and corpus coverage for evaluating large-scale predicate-argument structure proposals. We evaluate the metrics and heuristics by applying them to PASBio 1.0. CONCLUSION: PASBio demonstrates the suitability of predicate-argument structures for representing aspects of the semantics of biomedical verbs. Metrics related to theta-criterion violations and to the distribution of arguments are able to detect flaws in semantic representations, given a set of predicate-argument structures and a relatively small corpus annotated with them

Small but crucial : the novel small heat shock protein Hsp21 mediates stress adaptation and virulence in Candida albicans

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