Time-based Detection Research Articles

A Hybrid Association Rule-Based Method to Detect and Classify Botnets

Nowadays, botnet has become a threat in the area of cybersecurity, and, worse still, it is difficult to be detected in complex network environments. Thus, traffic analysis is adopted to detect the botnet since this kind of method is practical and effective; however, the false rate is very high. The reason is that normal traffic and botnet traffic are quite close to the border, making it so difficult to be recognized. In this paper, we propose an algorithm based on a hybrid association rule to detect and classify the botnets, which can calculate botnets’ boundary traffic features and receive effects in the identification between normal and botnet traffic ideally. First, after collecting the data of different botnets in a laboratory, we analyze botnets traffic features by processing a data mining on it. The suspicious botnet traffic is filtered through DNS protocol, black and white list, and real-time feature filtering methods. Second, we analyze the correlation between domain names and IP addresses. Combining with the advantages of the existing time-based detection methods, we do a global correlation analysis on the characteristics of botnets, to judge whether the detection objects can be botnets according to these indicators. Then, we calculate these parameters, including the support, trust, and membership functions for association rules, to determine which type of botnet it belongs to. Finally, we process the test by using the public dataset and it turns out that the accuracy of our algorithm is higher.

Algorithms and software solutions for SQL injection vulnerability testing in web applications

Software security gains importance day by day and developers try to secure web applications as much as possible to protect confidentiality, integrity and availability that are described in the fundamental security model so-called CIA triad. SQL injection vulnerability which can violate the confidentiality and integrity principles of the CIA triad is reviewed, and SQL injection attack execution and protection techniques are explained. The common frameworks’ solutions against SQL injection vulnerability were compared, and this comparison shown the most used techniques in this domain. Error-based and time-based detection algorithms for SQL injection’s identification are developed to create a vulnerability scanner that can detect SQL attacks which cause vulnerability in web applications, and these algorithms are represented in form of UML-activity diagrams. In order to discover all possible links and forms to perform SQL injection vulnerability tests in the entire website, a web crawler is needed. Breadth-First Search (BFS) algorithm for developing the web crawler is proposed, and the appropriate pseudo code and activity diagram are provided. Besides, Common Vulnerability Scoring System (CVSS) that is used to measure severity score of attacks that can violate CIA triad principles is reviewed. Qualitative severity score rating scale of CVSS is explained. An example of CVSS calculation is represented. Necessary components of a vulnerability scanner are explained. A vulnerability scanner prototype is developed using explained algorithms. Process results of this vulnerability scanner’s usage for real web applications are represented. Conclusions are made, and goals of future work are defined.

Computational Algorithms Underlying the Time-Based Detection of Sudden Cardiac Arrest via Electrocardiographic Markers

Early detection of sudden cardiac arrest (SCA) is critical to prevent serious repercussion such as irreversible neurological damage and death. Currently, the most effective method involves analyzing electrocardiogram (ECG) features obtained during ventricular fibrillation. In this study, data from 10 normal patients and 10 SCA patients obtained from Physiobank were used to statistically compare features, such as heart rate, R-R interval duration, and heart rate variability (HRV) features from which the HRV features were then selected for classification via linear discriminant analysis (LDA) and linear and fine Gaussian support vector machines (SVM) in order to determine the ideal time-frame in which SCA can be accurately detected. The best accuracy was obtained at 2 and 8 min prior to SCA onset across all three classifiers. However, accuracy rates of 75–80% were also obtained at time-frames as early as 50 and 40 min prior to SCA onset. These results are clinically important in the field of SCA, as early detection improves overall patient survival.

Developmental anatomy and immunocytochemistry reveal the neo-ontogenesis of the leaf tissues of Psidium myrtoides (Myrtaceae) towards the globoid galls of Nothotrioza myrtoidis (Triozidae).

The temporal balance between hyperplasia and hypertrophy, and the new functions of different cell lineages led to cell transformations in a centrifugal gradient that determines the gall globoid shape. Plant galls develop by the redifferentiation of new cell types originated from those of the host plants, with new functional and structural designs related to the composition of cell walls and cell contents. Variations in cell wall composition have just started to be explored with the perspective of gall development, and are herein related to the histochemical gradients previously detected on Psidium myrtoides galls. Young and mature leaves of P. myrtoides and galls of Nothotrioza myrtoidis at different developmental stages were analysed using anatomical, cytometrical and immunocytochemical approaches. The gall parenchyma presents transformations in the size and shape of the cells in distinct tissue layers, and variations of pectin and protein domains in cell walls. The temporal balance between tissue hyperplasia and cell hypertrophy, and the new functions of different cell lineages led to cell transformations in a centrifugal gradient, which determines the globoid shape of the gall. The distribution of cell wall epitopes affected cell wall flexibility and rigidity, towards gall maturation. By senescence, it provided functional stability for the outer cortical parenchyma. The detection of the demethylesterified homogalacturonans (HGAs) denoted the activity of the pectin methylesterases (PMEs) during the senescent phase, and was a novel time-based detection linked to the increased rigidity of the cell walls, and to the gall opening. Current investigation firstly reports the influence of immunocytochemistry of plant cell walls over the development of leaf tissues, determining their neo-ontogenesis towards a new phenotype, i.e., the globoid gall morphotype.

The Research on Modeling and Optimization of Comprehensive Maintenance

This paper conducted a study on the comprehensive maintenance combined by time-based replacement and time-based detection. The sourse and policy of comprehensive maintenance were introduced in details. Based on availability and cost, comprehensive maintenance model were established respectively. For exponential distribution, optimized the modle, and finally through examples compared the advantages and disadvantages of the models.

Assay of chondroitin sulfate using time-based detection in a simple lab-on-chip

Amount of chondroitin sulfate in supplementary tablets is determined using the reaction with methylene blue in a proposed simple lab-on-chip platform. An easy approach of detection by timing the migration of the reaction zone, which depends upon concentration of chondroitin sulfate, is described. This simple assay system does not require any complicated or expensive instrumentation and has a potential of further development for on-site analysis.

Time-based detection of changes to multivariate patterns

Detection of changes to multivariate patterns is an important topic in a number of different domains. Modern data sets often include categorical and numerical data and potentially complex in-control regions. Given a flexible, robust decision rule for this environment that signals based on an individual observation vector, an important issue is how to extend the rule to incorporate time-based information. A decision rule can be learned to detect shifts through artificial data that transforms the problem to one of supervised learning. Then class probability ratios are derived from a relationship to likelihood ratios to form the basis for time-weighted updates of the monitoring scheme.

Simple lab-on-chip approach with time-based detection

A simple lab-on-chip approach with time-based detection is proposed. A platform is made from a piece of acrylic differently shaped channels for introducing sample and reagent(s) using flow manipulation. Time-based changes involving migration of the reaction zone are monitored. The changes can be visually monitored by using a stop-watch with naked eyes observation. Some applications for the determination of ascorbic acid, acetic acid and iron in real samples with different chemistries were demonstrated.

An experiment using time-based detection in flow injection analysis

This experiment can be adopted to work with any number of reactions that result in a product that absorbs light of the LED wavelength significantly.