Comparison of catalytic ozonation of phenol by activated carbon and manganese-supported activated carbon prepared from brewing yeast

Guiping Wu,Longzhe Cui,정태섭,원찬희 한국화학공학회 2010 Korean Journal of Chemical Engineering Vol.27 No.1

Activated carbon (AC) was prepared using brewing yeast as precursor by chemical activation and manganese was supported on activated carbon (Mn/AC) by adsorption-activation method. The characterizations of prepared AC and Mn/AC and their performance as ozonation catalysts was tested. The results indicated that the crystalline phase of supported manganese was MnO. The total BET surface areas of prepared AC and Mn/AC were found to be 1603.0m2/g and 598.9 m2/g, with total pore volumes of 1.43 and 0.49 cm3/g, respectively. The average pore diameters of AC and Mn/AC were found to be 3.5 nm and 3.3 nm. Adsorption capacities of phenol onto the produced AC and Mn/AC were determined by batch test, at 25 oC and pH 7. Langmuir and Freundlich isotherm models were used to fit the isotherm experimental data, and the Langmuir isotherm model fitted these two adsorption systems well. The maximum uptakes of phenol by AC and Mn/AC were estimated to be 513.5 mg/g and 128.2 mg/g. The presence of AC prepared from brewing yeast was advantageous for TOC reduction of phenol solution compared with single ozonation, and the greatest TOC removal efficiency was obtained in the presence of Mn/AC. All ozonation reactions followed the pseudofirst-order kinetics model well, the degradation rate of phenol was enhanced in the presence of catalysts, and the more pronounced degradation rate was achieved in O3/Mn/AC system. The rate constants were determined to be 2.16×10−2min−1 for O3 alone, 5.70×10−2 min−1 for O3/AC and 6.82×10−2 min−1 for O3/Mn/AC.

Anomaly-based Intrusion Detection using Multiclass-SVM with Parameters Optimized by PSO

GuiPing Wang,ShuYu Chen,Jun Liu 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.6

Intrusion detection systems (IDS) play an important role in defending network systems from insider misuse as well as external attackers. Compared with misuse-based techniques, anomaly-based intrusion detection techniques perform well in detecting new attacks. Firstly, this paper proposes a feature selection algorithm based on SVM (termed FS-SVM) to reduce the dimensionality of sample data. Moreover, this paper presents an anomaly-based intrusion detection algorithm, i.e., multiclass support vector machine (MSVM) with parameters optimized by particle swarm optimization (PSO) (termed MSVM-PSO), to detect anomalous connections. To verify the effectiveness of these two proposed algorithms (FS-SVM and MSVM-PSO) and the detection precision of MSVM-PSO, this paper conducts experiments on the famous KDD Cup dataset. This paper compares MSVM-PSO with three commonly adopted algorithms, namely, Bayesian, K-Means, and multiclass SVM with parameters optimized grid method (MSVM-grid). The experimental results show that MSVM-PSO outperforms these three algorithms in detection accuracy, FP rate, and FN rate.

A Dependable Monitoring Mechanism Combining Static and Dynamic Anomaly Detection for Network Systems

GuiPing Wang,ShuYu Chen,Zhen Zhou,MingWei Lin 보안공학연구지원센터 2014 International Journal of Future Generation Communi Vol.7 No.1

An Anomaly Detection Framework Based on ICA and Bayesian Classification for IaaS Platforms

( Guiping Wang ),( Jianxi Yang ),( Ren Li ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.8

Infrastructure as a Service (IaaS) encapsulates computer hardware into a large amount of virtual and manageable instances mainly in the form of virtual machine (VM), and provides rental service for users. Currently, VM anomaly incidents occasionally occur, which leads to performance issues and even downtime. This paper aims at detecting anomalous VMs based on performance metrics data of VMs. Due to the dynamic nature and increasing scale of IaaS, detecting anomalous VMs from voluminous correlated and non-Gaussian monitored performance data is a challenging task. This paper designs an anomaly detection framework to solve this challenge. First, it collects 53 performance metrics to reflect the running state of each VM. The collected performance metrics are testified not to follow the Gaussian distribution. Then, it employs independent components analysis (ICA) instead of principal component analysis (PCA) to extract independent components from collected non-Gaussian performance metric data. For anomaly detection, it employs multi-class Bayesian classification to determine the current state of each VM. To evaluate the performance of the designed detection framework, four types of anomalies are separately or jointly injected into randomly selected VMs in a campus-wide testbed. The experimental results show that ICA-based detection mechanism outperforms PCA-based and LDA-based detection mechanisms in terms of sensitivity and specificity.

UFKLDA: An unsupervised feature extraction algorithm for anomaly detection under cloud environment

GuiPing Wang,JianXi Yang,Ren Li 한국전자통신연구원 2019 ETRI Journal Vol.41 No.5

In a cloud environment, performance degradation, or even downtime, of virtual machines (VMs) usually appears gradually along with anomalous states of VMs. To better characterize the state of a VM, all possible performance metrics are collected. For such high‐dimensional datasets, this article proposes a feature extraction algorithm based on unsupervised fuzzy linear discriminant analysis with kernel (UFKLDA). By introducing the kernel method, UFKLDA can not only effectively deal with non‐Gaussian datasets but also implement nonlinear feature extraction. Two sets of experiments were undertaken. In discriminability experiments, this article introduces quantitative criteria to measure discriminability among all classes of samples. The results show that UFKLDA improves discriminability compared with other popular feature extraction algorithms. In detection accuracy experiments, this article computes accuracy measures of an anomaly detection algorithm (i.e., C‐SVM) on the original performance metrics and extracted features. The results show that anomaly detection with features extracted by UFKLDA improves the accuracy of detection in terms of sensitivity and specificity.

Effect of Easy Transaction, Consumer Interests, and Systems Security Level Measures against Fraud Online Shopping in Lazada

GuiPing Wang,Ren Li,XiaoYi Yuan 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.12

This study aimed to examine the effect of the ease of the transaction, the consumer interest, and the level of system security against acts of fraud on online shopping. Ease of transaction is measured from the transaction speed, high accuracy, high volume transaction, highly correlated, and ease of access are high. Measurement of consumer interests is including motivation, perception, learning, and memory. System security level measured from the privacy, integrity, autentication, availability, and access control.Sample selection is done by using purposive sampling method. The research data were collected from students of the Faculty of Economics, University of Trisakti. The samples used were 100 accounting students from semesters 1 to 9. The analysis technique used is multiple regression in SPSS version 23. The results showed that the factors such as the ease of transactions, consumer interest, and the security level of the system is partially measured by the transaction speed, high accuracy, high volume transaction are highly correlated. Meanwhile, ease of access is high, motivation, perception, learning and memory does not have a significant effect on the action of cheating but the privacy, integrity, autentication, availability. The access control can influence the actions of fraud significantly. Influence ease of transactions, consumer interest, and the security level of positive and significant impact on fraud actions simultaneously.

Imbalanced SVM-Based Anomaly Detection Algorithm for Imbalanced Training Datasets

GuiPing Wang,JianXi Yang,Ren Li 한국전자통신연구원 2017 ETRI Journal Vol.39 No.5

Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)-based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned classification hyperplane skews toward the positive samples, resulting in a high false-negative rate. This article proposes a new imbalanced SVM (termed ImSVM)- based anomaly detection algorithm, which assigns a different weight for each positive support vector in the decision function. ImSVM adjusts the learned classification hyperplane to make the decision function achieve a maximum GMean measure value on the dataset. The above problem is converted into an unconstrained optimization problem to search the optimal weight vector. Experiments are carried out on both Cloud datasets and Knowledge Discovery and Data Mining datasets to evaluate ImSVM. Highly imbalanced training sample sets are constructed. The experimental results show that ImSVM outperforms over-sampling techniques and several existing imbalanced SVM-based techniques.

Performance Improvement of Model Predictive Control Using Control Error Compensation for Power Electronic Converters Based on the Lyapunov Function

Guiping Du,Zhifei Liu,Fada Du,Jiajian Li 전력전자학회 2017 JOURNAL OF POWER ELECTRONICS Vol.17 No.4

This paper proposes a model predictive control based on the discrete Lyapunov function to improve the performance of power electronic converters. The proposed control technique, based on the finite control set model predictive control (FCS-MPC), defines a cost function for the control law which is determined under the Lyapunov stability theorem with a control error compensation. The steady state and dynamic performance of the proposed control strategy has been tested under a single phase AC/DC voltage source rectifier (S-VSR). Experimental results demonstrate that the proposed control strategy not only offers global stability and good robustness but also leads to a high quality sinusoidal current with a reasonably low total harmonic distortion (THD) and a fast dynamic response under linear loads.

Isolation of an Indigenous Imidacloprid-Degrading Bacterium and Imidacloprid Bioremediation Under Simulated In Situ and Ex Situ Conditions

( Guiping Hu ),( Yan Zhao ),( Bo Liu ),( Fengqing Song ),( Min Sheng You ) 한국미생물 · 생명공학회 2013 Journal of microbiology and biotechnology Vol.23 No.11

The Bacterial community structure and its complexity of the enrichment culture during the isolation and screening of imidacloprid-degrading strain were studied using denaturating gradient gel electrophoresis analysis. The dominant bacteria in the original tea rhizosphere soil were uncultured bacteria, Rhizobium sp., Sinorhizobium, Ochrobactrum sp., Alcaligenes, Bacillus sp., Bacterium, Klebsiella sp., and Ensifer adhaerens. The bacterial community structure was altered extensively and its complexity reduced during the enrichment process, and four culturable bacteria, Ochrobactrum sp., Rhizobium sp., Geobacillus stearothermophilus, and Alcaligenes faecalis, remained in the final enrichment. Only one indigenous strain, BCL-1, with imidacloprid-degrading potential, was isolated from the sixth enrichment culture. This isolate was a gram-negative rod-shaped bacterium and identified as the genus Ochrobactrum based on its morphological, physiological, and biochemical properties and its 16S rRNA gene sequence. The degradation test showed that approximately 67.67% of the imidacloprid (50 mg/l) was degraded within 48 h by strain BCL-1. The optimum conditions for degradation were a pH of 8 and 30oC. The simulation of imidacloprid bioremediation by strain BCL-1 in soil demonstrated that the best performance in situ (tea soil) resulted in the degradation of 92.44% of the imidacloprid (100 mg/g) within 20 days, which was better than those observed in the ex situ simulations that were 64.66% (cabbage soil), 41.15% (potato soil), and 54.15% (tomato soil).

An Anomaly Detection Framework for Detecting Anomalous Virtual Machines under Cloud Computing Environment

GuiPing Wang,JiaWei Wang 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.1

A variety of faults may cause performance degradation or even downtime of virtual machines (VMs) under Cloud environment, thus lowering the dependability of Cloud platform. Detecting anomalous VMs before real failures occur is an important means to improve the dependability of Cloud platform. Since the performance or state of VMs may be affected by the environmental factors, this article proposes an environment-aware anomaly detection framework (termed EaAD) for VMs under Cloud environment. EaAD partitions all the VMs in Cloud platform into several monitoring domains based on similarity in running environment, which makes the VMs in a same monitoring domain have similar running environment. In each domain, the equipped anomaly detection algorithm detects anomalous VMs based on their performance metrics. In addition, anomaly detection in a certain monitoring domain faces such challenges as multiple anomaly categories, imbalanced training sample sets, increasing number of training samples. To cope with these challenges, several support vector machine (SVM) based anomaly detection algorithms are implemented and equipped in EaAD, including C-SVM, OCSVM, multi-class SVM, imbalanced SVM, online learning SVM. This article conducts experiments on EaAD to test the performance of the adopted detection algorithms and looks into future work.