ECFGM: enriched control flow graph miner for unknown vicious infected code detection

Vicious codes, especially viruses, as a kind of impressive malware have caused many disasters and continue to exploit more vulnerabilities. These codes are injected inside benign programs in order to abuse their hosts and ease their propagation. The offsets of injected virus codes are unknown and their targets usually are latent until they are executed and activated, what in turn makes viruses very hard to detect. In this paper enriched control flow graph miner, ECFGM in short, is presented to detect infected files corrupted by unknown viruses. ECFGM uses enriched control flow graph model to represent the benign and vicious codes. This model has more information than traditional control flow graph (CFG) by utilizing statistical information of dependent assembly instructions and API calls. To the best of our knowledge, the presented approach in this paper, for the first time, can recognize the offset of infected code of unknown viruses in the victim files. The main contributions of this paper are two folds: first, the presented model is able to detect unknown vicious code using ECFG model with reasonable complexity and desirable accuracy. Second, our approach is resistant against metamorphic viruses which utilize dead code insertion, variable renaming and instruction reordering methods.     

A bio-inspired gelatin-based pH- and thermal-sensitive magnetic hydrogel for in vitro chemo/hyperthermia treatment of breast cancer cells

Gelatin (Gel)-based pH- and thermal-responsive magnetic hydrogels (MH-1 and MH-2) were designed and developed as novel drug delivery systems (DDSs) for cancer chemo/hyperthermia therapy. For this goal, Gel was functionalized with methacrylic anhydride (GelMA), and then copolymerized with (2-dimethylaminoethyl) methacrylate (DMAEMA) monomer in the presence of methacrylate-end capped magnetic nanoparticles (MNPs) as well as triethylene glycol dimethacrylate (TEGDMA; as crosslinker). Afterward, a thiol-end capped poly(N-isopropylacrylamide) (PNIPAAm-SH) was synthesized through an atom transfer radical polymerization technique, and then attached onto the hydrogel through “thiol-ene” click grafting. The preliminary performances of developed MHs for chemo/hyperthermia therapy of human breast cancer was investigated through the loading of doxorubicin hydrochloride (Dox) as an anticancer agent followed by cytotoxicity measurement of drug-loaded DDSs using MTT assay by both chemo- and chemo/hyperthermia-therapies. Owing to porous morphologies of the fabricated magnetic hydrogels according to scanning electron microscopy images and strong physicochemical interactions (e.g., hydrogen bonding) the drug loading capacities of the MH-1 and MH-2 were obtained as 72 ± 1.4 and 77 ± 1.8, respectively. The DDSs exhibited acceptable pH- and thermal-triggered drug release behaviors. The MTT assay results revealed that the combination of hyperthermia therapy and chemotherapy has synergic effect on the anticancer activities of the developed DDSs.     

Flexible decision tree for data stream classification in the presence of concept change, noise and missing values

In recent years, classification learning for data streams has become an important and active research topic. A major challenge posed by data streams is that their underlying concepts can change over time, which requires current classifiers to be revised accordingly and timely. To detect concept change, a common methodology is to observe the online classification accuracy. If accuracy drops below some threshold value, a concept change is deemed to have taken place. An implicit assumption behind this methodology is that any drop in classification accuracy can be interpreted as a symptom of concept change. Unfortunately however, this assumption is often violated in the real world where data streams carry noise that can also introduce a significant reduction in classification accuracy. To compound this problem, traditional noise cleansing methods are incompetent for data streams. Those methods normally need to scan data multiple times whereas learning for data streams can only afford one-pass scan because of data’s high speed and huge volume. Another open problem in data stream classification is how to deal with missing values. When new instances containing missing values arrive, how a learning model classifies them and how the learning model updates itself according to them is an issue whose solution is far from being explored. To solve these problems, this paper proposes a novel classification algorithm, flexible decision tree (FlexDT), which extends fuzzy logic to data stream classification. The advantages are three-fold. First, FlexDT offers a flexible structure to effectively and efficiently handle concept change. Second, FlexDT is robust to noise. Hence it can prevent noise from interfering with classification accuracy, and accuracy drop can be safely attributed to concept change. Third, it deals with missing values in an elegant way. Extensive evaluations are conducted to compare FlexDT with representative existing data stream classification algorithms using a large suite of data streams and various statistical tests. Experimental results suggest that FlexDT offers a significant benefit to data stream classification in real-world scenarios where concept change, noise and missing values coexist.     

Free and forced convection boundary layer flow through a porous medium with large suction

An analytical study is made of the free and forced convection boundary layer flow past a porous medium bounded by a semi-infinite vertical porous plate. Locally similar solutions are then obtained by a perturbation method for large suction. Solutions for the velocity and temperature distributions are shown graphically for various suction velocities and values of the driving parameter G_r/R, where G_r is the Grashof number and R_e is the Reynolds number. The corresponding values of the skin friction coefficient and the Nusselt number are finally shown in tabular form.     

Stability of edible oils and fats to fluorescent light irradiation

Butter, butterfat, and corn, coconut, rapeseed, and soybean oils were exposed to 500 ft-c of fluorescent light at varying time-temperature conditions. Oxidation rates were measured by the peroxide values. Vitamin A and β-carotene content of butterfat were estimated. The effect of wavelength on the relative rates of oxidation was determined. The light transmitting properties of the samples at 15 and 30 C over a spectral range of 380–750 nm were measured. It was observed that there was no increase in oxidation rate when the light was switched off. The stability of the oils as shown by the oxidation rates did not correlate well with the ratios of C18:2 to C18:1 or C18:3 to C18:2 nor with the degree of unsaturation. Increase in temperature alone had minimal effect; however, in the presence of light the rate of oxidation increased considerably with a corresponding decrease in the content of Vitamin A and β-carotene. β-Carotene provided strong protective properties. After the photobleaching of β-carotene in butterfat, there was a rapid increase in peroxide values. With coconut oil, the oxidation rate was greater at 15 C than at 30 C due to greater light absorption at 15 C over the entire spectrum. The rate of oxidation decreased at higher wavelengths, and this effect was more pronounced in the vegetable oils than in butterfat, where the β-carotene was considered to serve as a filter for light of low wavelength. Presented at the AOCS meeting, Dallas, April 1975.     

Attribute weighted Naive Bayes classifier using a local optimization

The Naive Bayes classifier is a popular classification technique for data mining and machine learning. It has been shown to be very effective on a variety of data classification problems. However, the strong assumption that all attributes are conditionally independent given the class is often violated in real-world applications. Numerous methods have been proposed in order to improve the performance of the Naive Bayes classifier by alleviating the attribute independence assumption. However, violation of the independence assumption can increase the expected error. Another alternative is assigning the weights for attributes. In this paper, we propose a novel attribute weighted Naive Bayes classifier by considering weights to the conditional probabilities. An objective function is modeled and taken into account, which is based on the structure of the Naive Bayes classifier and the attribute weights. The optimal weights are determined by a local optimization method using the quasisecant method. In the proposed approach, the Naive Bayes classifier is taken as a starting point. We report the results of numerical experiments on several real-world data sets in binary classification, which show the efficiency of the proposed method.     

Investigation of the impact of global warming on precipitation pattern of Saudi Arabia

This study has been carried out to forecast the impact of global warming on the precipitation pattern of Saudi Arabia by the end of year 2100. Simulation has been done using EdGCM model software (with available 8×10 resolution) developed at Columbia University on which there have been produced global precipitation maps for the seasonal and annual averages for the last 5 years (2096–2100). For each map, EdGCM grid values surrounding Saudi Arabia are used as input to one of the tools of Eagle point software called surface modelling (SM). SM is a new approach for downscaling global climate model results. SM software modelled out isohyets at 0.2 mm/day interval. The results indicate that the present pattern of precipitation (more in winter and less in summer) is going to change by almost equal occurrence of precipitation in all seasons for double_CO₂ (2CO₂) experiment. The 2CO₂ experiment indicates an increase of about 16.05% over the annual average precipitation across the country.     

Verification of Authentication Protocols for Epistemic Goals via SAT Compilation

This paper introduces a new methodology for epistemic logic, to analyze communication protocols that uses knowledge structures, a specific form of Kripke semantics over hostile networks. The paper particularly focuses on automatic verification of authentication protocols. Our approach is based on the actual definitions of a protocol, not on some difficultto-establish justifications. The proposed methodology is different from many previous approaches to automatic verification of security protocols in that it is justification-oriented instead of falsification-oriented, i.e., finding bugs in a protocol. The main idea is based on observations： separating a principal executing a run of protocol from the role in the protocol, and inferring a principal＇s knowledge from the local observations of the principal. And we show analytically and empirically that this model can be easily reduced to Satisfiability （SAT） problem and efficiently implemented by a modern SAT solver.     

Increasing ethylene production as a high value hydrocarbon in Fischer-Tropsch (FT) reactor: A concept reactor for combining FT with oxidative coupling of methane

The paper proposes a concept configuration of reactors for coupling OCM and FTS, and presents systematic simulation results. FTS section is a combination of fixed bed and membrane fluidized bed reactor, and feed of the FT reactor is supplied by OCM. The reactor configuration is compared with the consecutive reactors of OCM and one fixed bed FT reactor. Effects of CH₄/O₂ ratio, percent of N₂ in the feed, contact time, and input temperature on the yield of ethylene and valuable hydrocarbons are studied. The results show that compared with one FTS reactor configuration, the dual FTS reactor configuration is more effective and thus gives much higher product yields. Furthermore, a main decrease is observed in the formation of CO₂ and CH₄.