Two-tier network anomaly detection model: a machine learning approach

Network anomaly detection is one of the most challenging fields in cyber security. Most of the proposed techniques have high computation complexity or based on heuristic approaches. This paper proposes a novel two-tier classification models based on machine learning approaches Naïve Bayes, certainty factor voting version of KNN classifiers and also Linear Discriminant Analysis for dimension reduction. Experimental results show a desirable and promising gain in detection rate and false alarm compared with other existing models. The model also trained by two generated balance training sets using SMOTE method to evaluate the chosen similarity measure for dealing with imbalanced network anomaly data sets. The two-tier model provides low computation time due to optimal dimension reduction and feature selection, as well as good detection rate against rare and complex attack types which are so dangerous because of their close similarity to normal behaviors like User to Root and Remote to Local. All evaluation processes experimented by NSL-KDD data set.     

Towards β-phase formation probability in spin coated PVDF thin films

This work aimed towards the study on variations in the percentage of β-phase in Poly vinylidene fluoride (PVDF) thin films deposited by spin coating technique. PVDF is a semi-crystalline polymer which exhibits α, β, γ and δ polymorphs. Among these polymorphs, generally it crystallizes in a non-polar α-phase, which is of little importance as far as its applications are concerned. However, the β-phase, which exhibits spontaneous polarity created tremendous interest and showed a path towards the devices based on its pyro- and piezoelectric properties. Fourier Transform Infrared (FTIR) spectroscopy and XRD techniques were used to study the percentage of formation of β-phase in spin coated PVDF thin films at different processing conditions viz. spin rotation speed (rpm), solution concentration and annealing temperature. We identified the β-phase percentage in PVDF thin films, which are (i) Deposited with different rotation speeds ranging from 1000 to 9000 rpm, (ii) Annealed at different temperatures viz.; room temperature to 100C, and (iii) Deposited at various solution concentrations. It is identified that percentage of formation of β-phase is high in the films deposited with 15(w/v)% solution concentration which is annealed at 100C. The films deposited at higher rpm is showing low enhancement in the β-phase with annealing temperature.     

Effect of gamma irradiation on thermoluminescence emissions from dust of Asian plant nuts

Experiments were conducted to see the effect of irradiation on the thermoluminescence emission from dust of plant nuts such as almond, peanut, pinenut and walnut. Inorganic dust particulate minerals adhering to the nut's surface were collected from unirradiated and irradiated (0.5, 1.0 and 1.5 kGy) samples, and analysed for their thermoluminescence (TL) intensities in the temperature range of 80–320°C at a temperature rate of 10°C/s. It was observed that peaks of the TL signals appeared at 200°C in each case and generally the magnitude of the peak signals of irradiated samples was manifold that of unirradiated controls. Regression and correlation analysis of the data indicated strong relationship between radiation adsorbed dose and TL values at each temperature (r ⪈ 0.96). It was concluded that TL measurements could serve as a fast and reliable method for identifying as well as determining absorbed dose in irradiated plant nuts.     

基于超混沌Lorenz系统的自适应异步抗噪声保密通信方案

针对非实时通信对安全性要求高且信道中存在噪声的问题,提出了基于超混沌掩盖调制的自适应异步抗噪声保密通信方案。通信原理是由超混沌Lorenz系统生成四个状态变量的伪随机向量,并通过信号增益将它们调整到同一区间;再由分段线性混沌映射（PWLCM）生成两个向量,用于在四个状态变量之间随机切换选取和确定动态时滞步长,以生成载体信号;然后将预编码后的二进制信息成对掩盖到载体信号中,添加高斯噪声后发送出去。实验结果分析发现最小信号增益和噪声系数的比值稳定在一个小区间[0.08,0.11]内,如果把该比值设置成某个大于该区间上限的值,则比特误码率可达到零,因此接收端可在噪声信道中接收并完美恢复出被掩盖信号。该方案利用超混沌系统的非线性动力学特性,能够在噪声信道中自适应实现异步保密通信,数值仿真验证了其有效性。     

Study of carbon nanotube field effect transistors performance based on changes in gate parameters

Carbon nanotubes are known as an interesting material to be used in the next generations of electronic technology, especially at nano regime. Nowadays, carbon nanotube field effect transistor or CNTFET is one of the promising devices for future electronic applications. A CNTFET which uses carbon nanotube as channel or source/drain region is the most promising candidate for replacing the current silicon transistor technology. The study of modern manufacturing approach and impact of device parameters on its performance is one of the important research fields in nanoelectronics. In this paper we study some aspects of changes in gate parameters at different channel diameters. This paper shows that for small values of diameter, increasing the dielectric constant of gate insulator doesn't help to improve the performance as value of dielectric constant of gate insulator reaches a certain amount. Also, increasing the oxide thickness of gate insulator doesn't always decrease transistor performance. For high diameter values, increasing the thickness up to a certain value improves the transistor performance.     

A SPECIFIC QUALITATIVE DETECTION METHOD FOR PEANUT (ARACHIS HYPOGAEA) IN FOODS USING POLYMERASE CHAIN REACTION

A qualitative method for detection of peanuts in foods using polymerase chain reaction was developed. A universal primer pair CP 03‐5′/CP 03‐3′ was designed to confirm the validity of the DNAs for PCR. The plant‐specific amplified fragments were detected from 13 kinds of plants using the universal primer pair. In addition, for the specific detection of peanuts with high sensitivity, the primer pair agg 04‐5′/agg 05‐3′ was designed to detect the gene encoding the peanut agglutinin precursor. The primer pair specifically generates a 95‐bp amplified fragment from peanut genomic DNA. Five hundred femto grams of peanut genomic DNA can be detected using the established method. The same qualitative results were obtained from both model processed and nonprocessed food samples containing 0.001, 0.01 and 0.1% of peanut. Moreover, it was shown that the trace amount of peanut in the commercial food products could be qualitatively detected using this method. The reproducibility and applicability of the proposed methods were verified in a six‐laboratory collaborative study.     

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.     

Nutritional evaluation of some commercial baby foods consumed in Pakistan

The nutritive value of some commercial baby foods commonly consumed in Pakistan was evaluated by means of chemical and biological assays. The protein content of milk based formulas and cereal-milk blends ranged from 13.3–26.0 and 11.1–13.2%, respectively. The fat content was highest (27%) in P-7f and was lowest (3%) in Robinson Baby Food. The available carbohydrates in milk based formulas were lower (44.1–59.9%) than cereal-milk blends (71.5–77.7%). The highest content of ash (5.6%) was found in Ostermilk. The metabolisable energy (ME) values ranged from 428–473 and 349–391 kcal, respectively, in milk based formulas and cereal-milk blends. The net protein utilisation (NPU) values of milk based formulas varied between 0.68 and 0.75 and NPU values of cereal-milk blends ranged from 0.69 to 0.71. The protein quality of baby foods was compared with the standards and the low NPU values reflected the influence of the processing technique.