Malware detection using assembly and API call sequences

One of the major problems concerning information assurance is malicious code. To evade detection, malware has also been encrypted or obfuscated to produce variants that continue to plague properly defended and patched networks with zero day exploits. With malware and malware authors using obfuscation techniques to generate automated polymorphic and metamorphic versions, anti-virus software must always keep up with their samples and create a signature that can recognize the new variants. Creating a signature for each variant in a timely fashion is a problem that anti-virus companies face all the time. In this paper we present detection algorithms that can help the anti-virus community to ensure a variant of a known malware can still be detected without the need of creating a signature; a similarity analysis (based on specific quantitative measures) is performed to produce a matrix of similarity scores that can be utilized to determine the likelihood that a piece of code under inspection contains a particular malware. Two general malware detection methods presented in this paper are: Static Analyzer for Vicious Executables (SAVE) and Malware Examiner using Disassembled Code (MEDiC). MEDiC uses assembly calls for analysis and SAVE uses API calls (Static API call sequence and Static API call set) for analysis. We show where Assembly can be superior to API calls in that it allows a more detailed comparison of executables. API calls, on the other hand, can be superior to Assembly for its speed and its smaller signature. Our two proposed techniques are implemented in SAVE) and MEDiC. We present experimental results that indicate that both of our proposed techniques can provide a better detection performance against obfuscated malware. We also found a few false positives, such as those programs that use network functions (e.g. PuTTY) and encrypted programs (no API calls or assembly functions are found in the source code) when the thresholds are set 50% similarity measure. However, these false positives can be minimized, for example by changing the threshold value to 70% that determines whether a program falls in the malicious category or not.     

Production and characterization of phenolic compounds by flash pyrolysis of palmyra palm

New and renewable fuels are the major alternatives to conventional fossil fuels. Biomass in the form of agricultural residues is becoming popular among new renewable energy sources, especially given its wide potential and abundant usage. This study deals with the characterization of the pyrolysis oil obtained from palmyra palm fruit bunch (Borassus flabellifer) produced by flash pyrolysis in the maximum yield. The pyrolysis oil was analyzed to determine its elemental composition and heating value. The chemical composition of the pyrolysis oil and fractions was investigated using various chromatographic techniques such as Fourier transform infra-red (FTIR) spectroscopy, gas chromatography–mass spectroscopy (GC-MS), and ¹H NMR spectroscopy. The bio-oil product was presented as an environmentally friendly green biofuel candidate. The analytical results showed that the pyrolysis bio-oils were very complex mixtures of organic compounds and contained a lot of nitrogenated and oxygenated compounds such as, phenols, aliphatic hydrocarbons, pyridines, amines, ketones, and so on.     

One‐Pot Lipase Entrapment Within Silica Particles to Prepare a Stable and Reusable Biocatalyst for Transesterification

In order to enhance the reusability, Rhizomucor miehei lipase was entrapped in a single step within silica particles having an oleic acid core (RML@SiO₂). Characterization of RML@SiO₂ by scanning and transmission electron microscopy and Fourier transform infrared studies supported the lipase immobilization within silica particles. The immobilized enzyme was employed for transesterification of cottonseed oil with methanol and ethanol. Under the optimum reaction conditions of a methanol‐to‐oil molar ratio of 12:1 or ethanol‐to‐oil molar ratio of 15:1, stirring speed of 250 revolutions/min (flask radius = 3 cm), reaction temperature of 40 °C, and biocatalyst concentration of 5 wt% (with respect to oil), more than 98 % alkyl ester yield was achieved in 16 and 24 h of reaction duration in case of methanolysis and ethanolysis, respectively. The immobilized enzyme did not require any buffer solution or organic solvent for optimum activity; hence, the produced biodiesel and glycerol were free from metal ion or organic molecule contamination. The activation energies for the immobilized enzyme‐catalyzed ethanolysis and methanolysis were found to be 34.9 ± 1.6 and 19.7 ± 1.8 kJ mol^?1, respectively. The immobilized enzyme was recovered from the reaction mixture and reused in 12 successive runs without significant loss of activity. Additionally, RML@SiO₂ demonstrated better reusability as well as stability in comparison to the native enzyme as the former did not lose the activity even upon storage at room temperature (25–30 °C) over an 8‐month period.     

Multi-objective optimization of feature selection using hybrid cat swarm optimization

With the pervasive generation of information from a wide range of sensors and devices, there always exist a large number of input features in databases, thus complicating machine learning problem formulation. However, certain features are relatively impertinent to specific problems, which may degrade the performances of classifiers in terms of prediction accuracy, sensitivity,specificity, and recall rate. The main goal of a multi-objective optimization problem is to identify the subsets of the given features. To this end, a hybrid cat swarm optimization(HCSO) algorithm is proposed in our paper for performance improvement of the basic cat swarm optimization(CSO) that incorporates guided and competitive inherent characteristics into the original CSO. The performance of HCSO has been tested by finding the optimal feature subset for 15 benchmark datasets. The number of class labels for these datasets varies between 2 and 40. The time complexity analysis of both CSO and HCSO has also been evaluated. Moreover, the performance of the proposed algorithm has been compared with that of simple CSO and other state-ofthe-art techniques. The performances obtained by HCSO have an average 2.68% improvement with a standard deviation of 2.91.The maximum performance improvement is up to 10.09% in prediction accuracy. Tested on the same datasets, CSO has yielded improvements within the range of-7.27% to 8.51% with an average improvement 0.9% and standard deviation 3.96. The statistical tests carried out in the experiments prove that HCSO manifests a moderately better feature selection capacity than that of its counterparts.     

Role of Post-translational Modifications in Alzheimer's Disease

The global burden of Alzheimer's disease (AD) is growing. Valiant efforts to develop clinical candidates for treatment have continuously met with failure. Currently available palliative treatments are temporary and there is a constant need to search for reliable disease pathways, biomarkers and drug targets for developing diagnostic and therapeutic tools to address the unmet medical needs of AD. Challenges in drug-discovery efforts raise further questions about the strategies of current conventional diagnosis; drug design; and understanding of disease pathways, biomarkers and targets. In this context, post-translational modifications (PTMs) regulate protein trafficking, function and degradation, and their in-depth study plays a significant role in the identification of novel biomarkers and drug targets. Aberrant PTMs of disease-relevant proteins could trigger pathological pathways, leading to disease progression. Advancements in proteomics enable the generation of patterns or signatures of such modifications, and thus, provide a versatile platform to develop biomarkers based on PTMs. In addition, understanding and targeting the aberrant PTMs of various proteins provide viable avenues for addressing AD drug-discovery challenges. This review highlights numerous PTMs of proteins relevant to AD and provides an overview of their adverse effects on the protein structure, function and aggregation propensity that contribute to the disease pathology. A critical discussion offers suggestions of methods to develop PTM signatures and interfere with aberrant PTMs to develop viable diagnostic and therapeutic interventions in AD.     

Additive noise removal using a novel fuzzy-based filter

The paper proposes a new fuzzy-based two-step filter for restoring images corrupted with additive noise. The goal of the first step is to compute the difference between the central pixel and its neighborhood in a selected window and to compute a fuzzy membership degree for each difference value using a Gaussian membership function. Computed fuzzy membership values are appropriately utilized as weights for each pixel and then computes the weighted average representing the modified value for the current central pixel. The second step is used as an augmented step to the first one and its goal is to improve the result obtained in the first step by reducing the noise in the color component differences without destroying the fine details of the image. The experimental analysis shows that the proposed method gives better results compared to existing advanced filters for additive noise reduction. Both visual, quantitative and qualitative analysis have been done to prove the efficiency and effectiveness of the proposed method.