Sophisticated Samples Research Articles

LISF: A Security Framework for Internet of Things (IoT) Integrated Distributed Applications

Distributed applications where Internet of Things (IoT) technology integrated are vulnerable to different kinds of attacks. Machine learning algorithms are widely used to detect intrusions in such applications. However, there is need for an effective unsupervised learning approach which can detect known and also unknown attacks. Towards this end, in this paper, we proposed a framework to protect security of IoT integrated architectures that are distributed in nature. Our framework is named Learning based IoT Security Framework (LISF). The framework is designed to have machine learning based security to IoT integrated use cases. Since IoT networks cause network traffic that is to be monitored and protected from external attacks, the proposed system uses deep learning technique for automatic detection of cyber-attacks. Particularly, the system exploits deep autoencoder which comprises of encoder and decoder for automatic detection of different kinds of intrusions. It is based on unsupervised learning which is crucial for distributed environments where network flows cannot have sophisticated training samples. We proposed an algorithm named Deep Autoencoder based Cyber Attack Detection (DAE-CAD). Experiments are made using IoT use case dataset known as UNSW-NB15. Our empirical results revealed that DAE-CAD outperforms existing methods with highest accuracy 91.36%.

A ratiometric fluorescent probe for simultaneous detection of L-ascorbic acid and alkaline phosphatase activity based on red carbon dots/polydopamine nanocomposite

Herein, efficient red carbon dots (R-CDs) were synthesized by one-step hydrothermal treatment of N-(4-amino phenyl) acetamide and (2,3-difluoro phenyl) boronic acid. The optimal emission peak of R-CDs was at 602 nm (under 520 nm excitation) and the absolute fluorescence quantum yield of R-CDs was 12.9%. Polydopamine, which was formed by the self-polymerization and cyclization of dopamine in alkaline condition, emitted characteristic fluorescence with peak position of 517 nm (under 420 nm excitation) and affected the fluorescence intensity of R-CDs through inner filter effect. L-Ascorbic acid (AA), which was the hydrolysis product of L-ascorbic acid-2-phosphate trisodium salt under the catalytic reaction of alkaline phosphatase (ALP), effectively prevented the polymerization of dopamine. Combined with the ALP-mediated AA production and the AA-mediated polydopamine generation, the ratiometric fluorescence signal of polydopamine with R-CDs was correlated closely with the concentration of both AA and ALP. Under optimal conditions, the detection limits of AA and ALP were 0.28 μM during linear range of 0.5–30 μM and 0.044 U/L with linear range of 0.05–8 U/L, respectively. This ratiometric fluorescence detection platform can efficiently shield the background interference of sophisticated samples by introducing a self-calibration as reference signal in a multi-excitation mode, which can detect AA and ALP in human serum samples with satisfactory results. Such R-CDs/polydopamine nanocomposite provides a steadfast quantitative information and makes R-CDs be excellent candidate for biosensors via combining target recognition strategy.

Improved estimation of the ratio of detection efficiencies of excited acceptors and donors for FRET measurements.

Förster resonance energy transfer (FRET) is a radiationless interaction between a donor and an acceptor whose distance dependence makes it a sensitive tool for studying the oligomerization and the structure of proteins. When FRET is determined by measuring the sensitized emission of the acceptor, a parameter characterizing the ratio of detection efficiencies of an excited acceptor versus an excited donor is invariably involved in the formalism. For FRET measurements involving fluorescent antibodies or other external labels, this parameter, designated by α, is usually determined by comparing the intensity of a known number of donors and acceptors in two independent samples leading to a large statistical variability if the sample size is small. Here, we present a method that improves precision by applying microbeads with a calibrated number of antibody binding sites and a donor-acceptor mixture in which donors and acceptors are present in a certain, experimentally determined ratio. A formalism is developed for determining α and the superior reproducibility of the proposed method compared to the conventional approach is demonstrated. Since the novel methodology does not require sophisticated calibration samples or special instrumentation, it can be widely applied for the quantification of FRET experiments in biological research.

Improving SSH detection model using IPA time and WGAN-GP

In the machine learning-based detection model, the detection accuracy tends to be proportional to the quantity and quality of the training dataset. The machine learning-based SSH detection model’s performance is affected by the size of the training dataset and the ratio of target classes. However, in an actual network environment within a short period, it is inconvenient to collect a sufficient and diverse training dataset. Even though many training data samples are collected, it takes a lot of effort and time to prepare the training dataset through data classification. To overcome these limitations, we generate sophisticated samples using the WGAN-GP algorithm and present how to select samples by comparing generator loss. The synthetic training dataset with generated samples improves the performance of the SSH detection model. Furthermore, we add the new features to include the distinction of inter-packet arrival time. The enhanced SSH detection model decreases false positives and provides a 0.999 F1-score by applying the synthetic dataset and the packet inter-arrival time features.

SMS Observer: A dynamic mechanism to analyze the behavior of SMS-based malware

Nowadays smartphones become an indispensable tool in many people's everyday life that makes themselves attractive targets for attackers. Among various malware targeting at smartphones, SMS-based malware is one of the most notorious ones. Though a number of Android dynamic analysis frameworks have been proposed to analyze SMS-based malware, most of these frameworks or some Android tools, such as Google Android Emulator, do not support an app or malware to send SMS messages to a real smartphone; hence, security researchers cannot use them directly to analyze the behavior of SMS-based malware. In our previous work, SMS Helper, we designed an application layer tool to allow an app or malware in an Android emulator to send and receive SMS messages to or from a real smartphone. Based on SMS Helper, this paper proposes an Android dynamic analysis framework, called SMS Observer, to assist security researchers to analyze SMS-based malware. SMS Observer integrates SMS Helper into it as a client agent, meanwhile, and it maintains the integrity of system logs. This paper also figures out a way to detect whether an app is executed in an emulator and describes how to use SMS Observer to prevent such evasion. Experimental results using real-world malware samples show SMS Observer is much more effective in detecting SMS-related behavior of SMS-based malware than existing frameworks, such as Google Android Emulator, Andrubis, CopperDroid, and DroidBox. SMS Observer can analyze sophisticated SMS-based malware samples and provide a comprehensive view of malicious behavior.

Dichroic Plasmonic Films Based on Anisotropic Au Nanoparticles for Enhanced Sensitivity and Figure of Merit Sensing

Localized surface plasmon resonance (LSPR) of metal nanoparticles are widely used to develop plasmonic sensors, which permit to detect minute amounts of molecular compounds. Two methods of measurements are commonly used. The first most common mode, which investigates the shift in wavelength of the LSPR induced by the analyte, requires the use of high-resolution monochromators. The second mode, based on self-reference or perfect absorbing systems, measures the changes of the signal intensity, and requires the use of sophisticated samples elaborated by lithography techniques. In this article, we adapt the very sensitive transmittance anisotropy spectroscopy technique with anisotropic plasmonic gold films, formed of slightly elongated nanoparticles easily elaborated by grazing deposition on microscopy glass slides. These films display two different LSPR, caused by their morphological anisotropy, as a function of the light polarization. Working at a single wavelength, we experimentally demonstrate an ultrahigh sensitivity to bulk refractive index sensing, with a resolution below delta_n=10-4 . The factor of merit (FoM*), defined for intensity-based plasmonic sensors, reaches the outstanding value of 23 000, which is due to the null optical anisotropy signal initially measured, thus granting an enhanced optical contrast when changing the RI. Furthermore, sensitivity down to four avidin molecules per square micron of the sample is theoretically shown, which offers a promising route towards easily made ultra-sensitive miniaturized plasmonic sensors, compatible with microfluidic systems, without the need for a monochromator.

AI-based hyperspectral and VOCs assessment approach to identify adulterated extra virgin olive oil

Spectral and VOCs data are important to determine the genuineness of the EVOO. The aim of this work is to apply an open-source device for VIS–NIR spectrophotometric measurements and a VOCs analyzer to identify some types of sophistication. 96 samples were analyzed. Samples included pure EVOO compound olive oil pure seed oils and olive oil samples adulterated with 7 different seed oils at different ratios. An artificial intelligence model was applied to identify adulterations from the spectral and VOCs data. The models built on both spectral and VOCs data showed perfect classification of pure EVOO samples with respect to sophisticated ones in both model and test sets. The most important spectral values and VOCs were extracted. Artificial intelligence applicative models aimed not only to identify sophisticated samples but also to understand the most informative spectra and VOCs to prototype specific devices for anti-fraud control.

Investigating Anti-Evasion Malware Triggers Using Automated Sandbox Reconfiguration Techniques

Malware analysis is fundamental for defending against prevalent cyber security threats and requires a means to deploy and study behavioural software traits as more sophisticated malware is developed. Traditionally, virtual machines are used to provide an environment that is isolated from production systems so as to not cause any adverse impact on existing infrastructure. Malware developers are fully aware of this and so will often develop evasion techniques to avoid detection within sandbox environments. In this paper, we conduct an investigation of anti-evasion malware triggers for uncovering malware that may attempt to conceal itself when deployed in a traditional sandbox environment. To facilitate our investigation, we developed a tool called MORRIGU that couples together both automated and human-driven analysis for systematic testing of anti-evasion methods using dynamic sandbox reconfiguration techniques. This is further supported by visualisation methods for performing comparative analysis of system activity when malware is deployed under different sandbox configurations. Our study reveals a variety of anti-evasion traits that are shared amongst different malware families, such as sandbox “wear-and-tear”, and Reverse Turing Tests (RTT), as well as more sophisticated malware samples that require multiple anti-evasion checks to be deployed. We also perform a comparative study using Cuckoo sandbox to demonstrate the limitations of adopting only automated analysis tools, to justify the exploratory analysis provided by MORRIGU. By adopting a clearer systematic process for uncovering anti-evasion malware triggers, as supported by tools like MORRIGU, this study helps to further the research of evasive malware analysis so that we can better defend against such future attacks.

Hiding Process Memory Via Anti-Forensic Techniques

Nowadays, security practitioners typically use memory acquisition or live forensics to detect and analyze sophisticated malware samples. Subsequently, malware authors began to incorporate anti-forensic techniques that subvert the analysis process by hiding malicious memory areas. Those techniques typically modify characteristics, such as access permissions, or place malicious data near legitimate one, in order to prevent the memory from being identified by analysis tools while still remaining accessible. With this paper, we present three novel methods that prevent malicious user space memory from appearing in analysis tools and additionally making the memory inaccessible from a security analysts perspective. Two of these techniques manipulate kernel structures, namely Page Table Entries and the structures responsible for managing user space memory regions, while the third one utilizes shared memory and hence does not require elevated privileges. As a proof of concept, we implemented all techniques for the Windows and Linux operating systems, and subsequently evaluated these with both, memory forensics and live analysis techniques. Furthermore, we discuss and evaluate several approaches to detect our subversion techniques and introduce two Rekall plugins that automate the detection of hidden memory for the shared memory scenario.

Usefulness of bee bread and capped brood for the assessment of monocyclic aromatic hydrocarbon levels in the environment

Monitoring airborne pollutants, like aromatic hydrocarbons, are raising more and more concerns recently. Various sampling techniques and methods are known to collect, measure, and analyse environmental pollution levels based on honey bee bodies or bee product samples. Although honey bees are studied in detail and sampling methods are becoming more and more sophisticated biological samples may significantly differ in pollutant accumulation, showing a wide range of pollution levels even in the same site and environment. We have compared the pollution levels of honey bee capped brood and bee bread (pollen collected by honey bees and deposited in the hive) originating from four sites during two years of study and twelve honey bee families near various pollution sources emitting monocyclic aromatic hydrocarbons (BTEX) to the environment. Our result showed, that the environmental monitoring of BTEX can be based on sampling honey bees, and bee bread in particular. However, we found a significant difference in the uptake of these pollutants regarding sample type. Pollen collected as a food source revealed consistently higher levels of BTEX than bee brood, as well as some other differences in pollution levels between samples and between seasons, as opposed to capped brood. Based on our results, we suggest that for measuring and monitoring of BTEX pollution in the environment the use of bee bread is a valuable source of information.

A point of care platform based on microfluidic chip for nucleic acid extraction in less than 1 minute.

In view of the complex procedure of nucleic acid extraction, there exists a huge challenge for the widespread use of point-of-care diagnostics for nucleic acid testing. To achieve point-of-care applications in a more rapid and cost-efficient manner, we designed a snake pipe-shaped microfluidic chip so as to accomplish reagents-prestored, time-saving, operation-simple nucleic acid extraction. All reagents needed for this process, including lysis buffer, wash buffer, elution buffer, and so on, were preloaded in the snake pipe and securely isolated by membrane valves, without the need for using any specialized equipment. By an integrated chip and a powerful ultrasonic, this device could complete virus nucleic acid extraction from sophisticated serum samples in less than 1 min. We used hepatitis B virus (HBV) and human immunodeficiency virus (HIV) mixed with different sources of serum as samples to be extracted. The coefficient of variation of HBV and HIV extraction on-chip was 1.32% and 2.74%, respectively, and there were no significant differences between on-chip and commercial instrument extraction (P > 0.05, α = 0.05) in different dilution ratios, which showed that the extraction device we established had excellent stability and sensitivity.

HPTLC-FLD-SERS as a facile and reliable screening tool: Exemplarily shown with tyramine in cheese

The serious cytotoxicity of tyramine attracted marked attention as it induced necrosis of human intestinal cells. This paper presented a novel and facile high performance thin-layer chromatography (HPTLC) method tailored for screening tyramine in cheese. Separation was performed on glass backed silica gel plates, using methanol/ethyl acetate/ammonia (6/4/1 v/v/v) as the mobile phase. Special efforts were focused on optimizing conditions (substrate preparation, laser wavelength, salt types and concentrations) of surface enhanced Raman spectroscopy (SERS) measurements directly on plates after derivatization, which enabled molecule-specific identification of targeted bands. In parallel, fluorescent densitometry (FLD) scanning at 380</400 nm offered satisfactory quantitative performances (LOD 9 ng/zone, LOQ 17 ng/zone, linearity 0.9996 and %RSD 6.7). Including a quick extraction/cleanup step, the established method was successfully validated with different cheese samples, both qualitatively (straightforward confirmation) and quantitatively (recovery rates from 83.7 to 108.5%). Beyond this application, HPTLC-FLD-SERS provided a new horizon in fast and reliable screening of sophisticated samples like food and herb drugs, striking an excellent balance between specificity, sensitivity and simplicity.

Revisiting the dimensional structure of the emotion domain

Recent research has claimed that a novelty dimension is needed to represent the cognitive emotion structure over and above valence, power and arousal. Novelty emerged when student samples evaluated the meaning of 24 emotion terms on 142 emotion features. This claim is debatable, however, because to date novelty has never been found in similarity sorting studies. It is possible that novelty emerged because sophisticated student samples evaluated emotion terms on emotion features. The current research identified a large, representative set of emotion terms using a free-listing task in a middle childhood up to early adulthood sample (N = 5071). Children, adolescents, students and adults (N = 1184) then evaluated the similarity between these emotion terms using a similarity rating task without priming any emotion feature. Novelty robustly emerged as the fourth dimension. The existence of novelty is thus confirmed with a different method across a wide variety of participants.

Template based classification of multi-touch gestures

We propose a probabilistic classifier for multi-touch gestures specified by users themselves. The template-based gesture classifier allows selecting gesture types more freely without constraints regarding implementation issues and considers multi-finger or bi-manual operations. The statistical approaches to the classification scheme are presented. The basic concepts of separating input into tokens, retrieving local features and applying a new method of sensor fusion under uncertainty are adaptive to broader application ranges. Results from testing against a set of sophisticated samples show that this approach performs well and, while recognition benefits from more complex gestures, it also distinguishes subtly different gestures.

Theory of Mid-infrared Absorption Microspectroscopy: II. Heterogeneous Samples

Fourier transform infrared (FT-IR) spectroscopic imaging combines the specificity of optical microscopy with the spectral selectivity of vibrational spectroscopy. There is increasing recognition that the recorded data may be dependent on the optical configuration and sample morphology in addition to its local material spectral response, but a quantitative framework for predicting such dependence is lacking. Here, a theory is developed to relate recorded data to the spectral and physical properties of heterogeneous samples. The modeling approach combines optical theory through rigorous coupled wave analysis with modeling of sampling geometry and sample structure. The interplay of morphology and dispersion are systematically explored using increasingly sophisticated samples to illustrate the dependence of the detected optical intensity on the spatial sample structure. Predictions of spectral distortions arising from the sample structure are quantified, and experimental validation of the developed theory is performed using a microfabricated standard from a commercial photoresist polymer. The developed framework forms a basis for understanding sample induced distortions in spectroscopic IR microscopy and imaging.

Investigation of polymer thin films by use of Bi-cluster-ion-supported time of flight secondary ion mass spectrometry

The investigation and analysis of polymer thin films with Bi(n)(+), n = 1-7 cluster ions has been demonstrated by means of static secondary ion mass spectrometry (SIMS). The highly specific signal enhancement of these primary ions combined with the individual fragmentation pattern of poly(4-vinylphenol) and poly(methyl methacrylate) is the basic principle for a modified approach of data reduction derived from the well-established g-SIMS procedure. Based on mass spectra, which correspond to different cluster ion sizes, not only a clear distinction between the two polymers is feasible but also a further simplification of the data can be demonstrated. It has been successfully proven that characteristic polymer-relevant species can be refined out of the large amount of unspecific and highly fragmented secondary ions, which are usually present in SIMS spectra. Therefore, a more precise and direct interpretation of complex organic fragments becomes feasible, which consequently enables the investigation of even more sophisticated samples.

LCG MCDB—a knowledgebase of Monte-Carlo simulated events

In this paper we report on LCG Monte-Carlo Data Base (MCDB) and software which has been developed to operate MCDB. The main purpose of the LCG MCDB project is to provide a storage and documentation system for sophisticated event samples simulated for the LHC Collaborations by experts. In many cases, the modern Monte-Carlo simulation of physical processes requires expert knowledge in Monte-Carlo generators or significant amount of CPU time to produce the events. MCDB is a knowledgebase mainly dedicated to accumulate simulated events of this type. The main motivation behind LCG MCDB is to make the sophisticated MC event samples available for various physical groups. All the data from MCDB is accessible in several convenient ways. LCG MCDB is being developed within the CERN LCG Application Area Simulation project. Program summary Program title: LCG Monte-Carlo Data Base Catalogue identifier: ADZX_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADZX_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU General Public Licence No. of lines in distributed program, including test data, etc.: 30 129 No. of bytes in distributed program, including test data, etc.: 216 943 Distribution format: tar.gz Programming language: Perl Computer: CPU: Intel Pentium 4, RAM: 1 Gb, HDD: 100 Gb Operating system: Scientific Linux CERN 3/4 RAM: 1 073 741 824 bytes (1 Gb) Classification: 9 External routines: • perl >= 5.8.5; • Perl modules ○ DBD-mysql >= 2.9004, ○ File::Basename, ○ GD::SecurityImage, ○ GD::SecurityImage::AC, ○ Linux::Statistics, ○ XML::LibXML > 1.6, ○ XML::SAX, ○ XML::NamespaceSupport; • Apache HTTP Server >= 2.0.59; • mod auth external >= 2.2.9; • edg-utils-system RPM package; • gd >= 2.0.28; • rpm package CASTOR-client >= 2.1.2-4; • arc-server (optional) Nature of problem: Often, different groups of experimentalists prepare similar samples of particle collision events or turn to the same group of authors of Monte-Carlo (MC) generators to prepare the events. For example, the same MC samples of Standard Model (SM) processes can be employed for the investigations either in the SM analyses (as a signal) or in searches for new phenomena in Beyond Standard Model analyses (as a background). If the samples are made available publicly and equipped with corresponding and comprehensive documentation, it can speed up cross checks of the samples themselves and physical models applied. Some event samples require a lot of computing resources for preparation. So, a central storage of the samples prevents possible waste of researcher time and computing resources, which can be used to prepare the same events many times. Solution method: Creation of a special knowledgebase (MCDB) designed to keep event samples for the LHC experimental and phenomenological community. The knowledgebase is realized as a separate web-server ( http://mcdb.cern.ch). All event samples are kept on types at CERN. Documentation describing the events is the main contents of MCDB. Users can browse the knowledgebase, read and comment articles (documentation), and download event samples. Authors can upload new event samples, create new articles, and edit own articles. Restrictions: The software is adopted to solve the problems, described in the article and there are no any additional restrictions. Unusual features: The software provides a framework to store and document large files with flexible authentication and authorization system. Different external storages with large capacity can be used to keep the files. The WEB Content Management System provides all of the necessary interfaces for the authors of the files, end-users and administrators. Running time: Real time operations.

Further Validation of a Subtle Racism Scale in South Africa

Obvious and transparent measures of traditional racism no longer appear to adequately assess anti-black prejudice in more sophisticated samples. This, together with a growing tendency for traditional measures to elicit antagonistic responses from subjects, led in an earlier study to the development and preliminary validation of a South African subtle racism (SR) scale. The current research used a new sample, in which the use of traditional measures of anti-black racism would have been very difficult, to cross-validate the SR scale. It also extended the validation of the SR scale by investigating its association with self-reports of actual interracial behaviour. The findings from 303 white students at the University of the Witwatersrand showed a high level of internal consistency on the SR scale, and a strong correlation with an interracial behavioural intention scale. These findings as well as significant correlations with self-reported interracial behaviour support the validity of the SR scale. Contrary to earlier thinking, the findings also indicate a very powerful relationship between anti-black prejudice and authoritarianism.

Developmental experiments on large-area silicon solar cells

A comprehensive design/material/technological study was conducted with the aim of obtaining high-efficiency back-surface-field- (BSF-) type bifacial solar cells with reproducible parameters on large-area n- and p-silicon wafers. Practical ways of attenuating the severe limitations imposed by different kinds of areal inhomogeneities on the electro-optical performance of large-area BSF single-crystal silicon solar cells are described and tested experimentally. Various procedures leading to a substantial increase of both the emitter and the base contributions to the generated photocurrent are implemented and discussed in detail. The p+-n-n+ and n+-p-p+ cells were processed as bifacial devices and tested under both front and backside AM1 illumination. The trial devices were fabricated on 2-in and 3-in. commercially available lower-grade silicon and the minimization of the cell cost was one of the sought-after objectives. The combination of simple design/technological approaches described in this work has ultimately led to the development of low-cost, high-efficiency (17%–18%) large-area silicon solar cells with good overall electro-optical performance as bifacial devices. The results of this work show clearly that simple, but adequately designed/processed cells fabricated on an industrial scale on large-area silicon wafers could possess parameters similar to those of sophisticated laboratory samples elaborated on small-area silicon. With the design/technological approaches outlined in this work, the use of even cheaper, i.e., lower-grade single-crystal silicon, could still yield 2- and 3-in. cells with fairly acceptable conversion efficiencies.

High-efficiency, low-cost solar cells on 3-inch silicon wafers

The present work describes practical ways of attenuating the severe limitations imposed by different kinds of areal inhomogeneities on the performance of large-area p+−n−n+ single-crystal silicon solar cells. The test devices were processed on 3-inch, lower-grade n-wafers and the minimization of the cells' cost was one of the desired objectives. A combination of simple design/technological approaches described in this work has ultimately led to the development of low-cost, high-efficiency (> 17%) 3-inch silicon solar cells with improved overall electro-optical performance. These devices possess short-circuit current densities similar to those of sophisticated laboratory samples prepared on high-quality, small-area silicon.