Diagnosing bot infections using Bayesian inference

Prior research in botnet detection has used the bot lifecycle to build detection systems. These systems, however, use rule-based decision engines which lack automated adaptability and learning, accuracy tunability, the ability to cope with gaps in training data, and the ability to incorporate local security policies. To counter these limitations, we propose to replace the rigid decision engines in contemporary bot detectors with a more formal Bayesian inference engine. Bottleneck, our prototype implementation, builds confidence in bot infections based on the causal bot lifecycle encoded in a Bayesian network. We evaluate Bottleneck by applying it as a post-processing decision engine on lifecycle events generated by two existing bot detectors (BotHunter and BotFlex) on two independently-collected datasets. Our experimental results show that Bottleneck consistently achieves comparable or better accuracy than the existing rule-based detectors when the test data is similar to the training data. For differing training and test data, Bottleneck, due to its automated learning and inference models, easily surpasses the accuracies of rule-based systems. Moreover, Bottleneck’s stochastic nature allows its accuracy to be tuned with respect to organizational needs. Extending Bottleneck’s Bayesian network into an influence diagram allows for local security policies to be defined within our framework. Lastly, we show that Bottleneck can also be extended to incorporate evidence trustscore for false alarm reduction.     

Cloud authorization: exploring techniques and approach towards effective access control framework

Despite the various attractive features that Cloud has to offer, the rate of Cloud migration is rather slow, primarily due to the serious security and privacy issues that exist in the paradigm. One of the main problems in this regard is that of authorization in the Cloud environment, which is the focus of our research. In this paper, we present a systematic analysis of the existing authorization solutions in Cloud and evaluate their effectiveness against well-established industrial standards that conform to the unique access control requirements in the domain. Our analysis can benefit organizations by helping them decide the best authorization technique for deployment in Cloud; a case study along with simulation results is also presented to illustrate the procedure of using our qualitative analysis for the selection of an appropriate technique, as per Cloud consumer requirements. From the results of this evaluation, we derive the general shortcomings of the extant access control techniques that are keeping them from providing successful authorization and, therefore, widely adopted by the Cloud community. To that end, we enumerate the features an ideal access control mechanisms for the Cloud should have, and combine them to suggest the ultimate solution to this major security challenge – access control as a service (ACaaS) for the software as a service (SaaS) layer. We conclude that a meticulous research is needed to incorporate the identified authorization features into a generic AcaaS framework that should be adequate for providing high level of extensibility and security by integrating multiple access control models.     

Multi-objective feature subset selection using mRMR based enhanced ant colony optimization algorithm (mRMR-EACO)

In this research, we propose a novel method to find the relevant feature subset by using ant colony optimisation minimum-redundancy–maximum-relevance. The proposed approach considers the significance of each feature while reducing the dimensionality. The performance of proposed algorithm has been compared with existing biologically inspired feature subset selection algorithms. Eight datasets have been selected from UCI machine learning repository for experimentation. The experimental results indicate that the presented algorithm out performs the other algorithms in terms of the classification accuracy and feature reduction.     

Membrane Bioreactor for Cometabolism of Trichloroethene Air Emissions

Biofilters have been of limited use for cometabolism of chlorinated organic compounds, such as trichloroethene (TCE), due to enzyme inhibition or toxicity effects. A hollow fiber membrane bioreactor was investigated that involves a bundle of polypropylene fibers through which volatile organic compound contaminated air passes. The fibers are immersed in a recirculating nutrient/cosubstrate solution. Batch culture experiments were performed with a mixed culture that could cometabolize TCE with toluene as a primary substrate. No inhibition or inquiry to the toluene degrading ability was observed at up to 15 mg L?1 toluene or up to 1.5 mg L?1 TCE. The culture was inoculated into the hollow-fiber membrane bioreactor. Initially toluene was supplied to the reactor to build a sufficient biomass density on the fibers. After steady-state toluene removal was achieved, TCE was added to the gas phase of the reactor. Toluene was added in three different configurations: (1) As a mixture with TCE in the gas phase; (2) by pulsing into the gas phase; or (3) to the liquid phase. This paper investigates which reactor configuration is most favorable for cometabolism of toluene and TCE.     

Narrow emission linewidths of positioned InAs quantum dots grown on pre-patterned GaAs(100) substrates

We report photoluminescence measurements on a single layer of site-controlled InAs quantum dots (QDs) grown by molecular beam epitaxy (MBE) on pre-patterned GaAs(100) substrates with a 15 nm re-growth buffer separating the dots from the re-growth interface. A process for cleaning the re-growth interface allows us to measure single dot emission linewidths of 80 μeV under non-resonant optical excitation, similar to that observed for self-assembled QDs. The dots reveal excitonic transitions confirmed by power dependence and fine structure splitting measurements. The emission wavelengths are stable, which indicates the absence of a fluctuating charge background in the sample and confirms the cleanliness of the re-growth interface.     

Empty nano and micro‐structured lipid carriers of virgin coconut oil for skin moisturisation

Virgin coconut oil (VCO) is the finest grade of coconut oil, rich in phenolic content, antioxidant activity and contains medium chain triglycerides (MCTs). In this work formulation, characterisation and penetration of VCO‐solid lipid particles (VCO‐SLP) have been studied. VCO‐SLP were prepared using ultrasonication of molten stearic acid and VCO in an aqueous solution. The electron microscopy imaging revealed that VCO‐SLP were solid and spherical in shape. Ultrasonication was performed at several power intensities which resulted in particle sizes of VCO‐SLP ranged from 0.608 ± 0.002 µm to 44.265 ± 1.870 µm. The particle size was directly proportional to the applied power intensity of ultrasonication. The zeta potential values of the particles were from −43.2 ± 0.28 mV to −47.5 ± 0.42 mV showing good stability. The cumulative permeation for the smallest sized VCO‐SLP (0.608 µm) was 3.83 ± 0.01 µg/cm² whereas for larger carriers it was reduced (3.59 ± 0.02 µg/cm²). It is concluded that SLP have the potential to be exploited as a micro/nano scale cosmeceutical carrying vehicle for improved dermal delivery of VCO.Inspec keywords: skin, cosmetics, vegetable oils, ultrasonic applications, electron microscopy, particle sizeOther keywords: empty nanostructured lipid carrier, empty microstructured lipid carrier, virgin coconut oil, skin moisturisation, phenolic content, antioxidant activity, medium chain triglyceride, MCT, VCO‐solid lipid particle, VCO‐SLP, molten stearic acid ultrasonication, electron microscopy imaging, power intensity, particle size, dermal delivery     

Deep Learning Based Face Detection and Identification of Criminal Suspects

Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade. One of the most tedious tasks is to track a suspect once a crime is committed. As most of the crimes are committed by individuals who have a history of felonies, it is essential for a monitoring system that does not just detect the person’s face who has committed the crime, but also their identity. Hence, a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network (DNN) model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been proposed. After detection and extraction of the face in the image by face cropping, the captured face is then compared with the images in the Criminal Database. The comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity metric. After plotting the values in a graph to find the threshold value, we conclude that the confidence rate of the encoder model is 0.75 and above.     

Digital watermarking using Hall property image decomposition method

Most of the existing singular value decomposition-based digital watermarking methods are not robust to geometric rotation, which change the pixels’ locations without maintaining the corresponding changes to the pixel’s intensity values of entire image and yield high computational cost. To answer this, we propose a digital image watermarking algorithm using the Hall property. In the proposed method, a digital watermark image is factorized into lower-triangular, upper-triangular, and permutation matrices. The permutation matrix is used as the valid key matrix for authentication of the rightful ownership of the watermark image. The product of the lower and upper triangular matrices is processed with a few iterations of the Arnold transformation to obtain the scrambled data. The scrambled data are embedded into particular sub-bands of a cover image using Wavelet transform. Our experiments show that the proposed algorithm is highly reliable and computationally efficient compared with state-of-the-art methods that are based on singular value decomposition.     

Magneto-electric characteristics in (Mn,Cu) co-doped BiFeO<Subscript>3</Subscript> multiferroic nanoparticles

Multiferroic nanoparticles having general formula BiFe_0.99-xMn_xCu_0.01O₃ (x = 0, 0.01, 0.02, 0.03 & 0.04) were prepared by a chemically derived method to explore the magneto-electric characteristics of this new class of materials. X-ray diffraction confirmed that all the samples had rhombohedraly distorted cubic perovskite 3D lattice. Lattice constant was increased with increasing concentration of Mn. Micrographs obtained from a field emission scanning electron microscope revealed a fine distribution of well-shaped particles while the particle size was increased with increased contents of Mn. Enhanced hopping mechanism induced by substitution of Mn at the lattice sites of Fe resulted in an increased AC conductivity. Ferroelectricity was observed to increase with increased Mn, attributed mainly to the leakage current due to free charge carriers instigated by multiple oxidation states of Fe and Mn. It has been observed that antiferromagnetic bismuth ferrite begins to show ferromagnetic behavior due to the collapse of antiferromagnetic spin structure with increased Mn contents.