Data-mining based SQL injection attack detection using internal query trees

Detecting SQL injection attacks (SQLIAs) is becoming increasingly important in database-driven web sites. Until now, most of the studies on SQLIA detection have focused on the structured query language (SQL) structure at the application level. Unfortunately, this approach inevitably fails to detect those attacks that use already stored procedure and data within the database system. In this paper, we propose a framework to detect SQLIAs at database level by using SVM classification and various kernel functions. The key issue of SQLIA detection framework is how to represent the internal query tree collected from database log suitable for SVM classification algorithm in order to acquire good performance in detecting SQLIAs. To solve the issue, we first propose a novel method to convert the query tree into an n-dimensional feature vector by using a multi-dimensional sequence as an intermediate representation. The reason that it is difficult to directly convert the query tree into an n-dimensional feature vector is the complexity and variability of the query tree structure. Second, we propose a method to extract the syntactic features, as well as the semantic features when generating feature vector. Third, we propose a method to transform string feature values into numeric feature values, combining multiple statistical models. The combined model maps one string value to one numeric value by containing the multiple characteristic of each string value. In order to demonstrate the feasibility of our proposals in practical environments, we implement the SQLIA detection system based on PostgreSQL, a popular open source database system, and we perform experiments. The experimental results using the internal query trees of PostgreSQL validate that our proposal is effective in detecting SQLIAs, with at least 99.6% of the probability that the probability for malicious queries to be correctly predicted as SQLIA is greater than the probability for normal queries to be incorrectly predicted as SQLIA. Finally, we perform additional experiments to compare our proposal with syntax-focused feature extraction and single statistical model based on feature transformation. The experimental results show that our proposal significantly increases the probability of correctly detecting SQLIAs for various SQL statements, when compared to the previous methods.     

Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems

Recommender systems apply data mining and machine learning techniques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on gray-sheep users problem responsible for the increased error rate in collaborative filtering based recommender systems. This paper makes the following contributions: we show that (1) the presence of gray-sheep users can affect the performance – accuracy and coverage – of the collaborative filtering based algorithms, depending on the data sparsity and distribution; (2) gray-sheep users can be identified using clustering algorithms in offline fashion, where the similarity threshold to isolate these users from the rest of community can be found empirically. We propose various improved centroid selection approaches and distance measures for the K-means clustering algorithm; (3) content-based profile of gray-sheep users can be used for making accurate recommendations. We offer a hybrid recommendation algorithm to make reliable recommendations for gray-sheep users. To the best of our knowledge, this is the first attempt to propose a formal solution for gray-sheep users problem. By extensive experimental results on two different datasets (MovieLens and community of movie fans in the FilmTrust website), we showed that the proposed approach reduces the recommendation error rate for the gray-sheep users while maintaining reasonable computational performance.     

A weighted multivariate Fuzzy C-Means method in interval-valued scientific production data

Clustering is the process of organizing objects into groups whose members are similar in some way. Most of the clustering methods involve numeric data only. However, this representation may not be adequate to model complex information which may be: histogram, distributions, intervals. To deal with these types of data, Symbolic Data Analysis (SDA) was developed. In multivariate data analysis, it is common some variables be more or less relevant than others and less relevant variables can mask the cluster structure. This work proposes a clustering method based on fuzzy approach that produces weighted multivariate memberships for interval-valued data. These memberships can change at each iteration of the algorithm and they are different from one variable to another and from one cluster to another. Furthermore, there is a different relevance weight associated to each variable that may also be different from one cluster to another. The advantage of this method is that it is robust to ambiguous cluster membership assignment since weights represent how important the different variables are to the clusters. Experiments are performed with synthetic data sets to compare the performance of the proposed method against other methods already established by the clustering literature. Also, an application with interval-valued scientific production data is presented in this work. Clustering quality results have shown that the proposed method offers higher accuracy when variables have different variabilities.     

An effective node-selection scheme for the energy efficiency of solar-powered WSNs in a stream environment

Previous research on scheduling and solar power issues of wireless sensor networks (WSNs) assumes that the sensors are deployed in a general environment. While monitoring the stream environment, sensors are attached to the stream side to collect the sensed data and transmit the data back to the sink. The stream environment can be scaled in several similar environments. This type of geographic limitation not only exists in a stream environment but also on streets, roads, and trails. This study presents an effective node-selection scheme to enhance the efficiency of saving power and coverage of solar-powered WSNs in a stream environment. Analysis of the sensor deployment in the stream environment permits sensors to be classified into different segments, and then allows the selection of active nodes for building inter-stream connections, inter-segment connections, and intra-segment connections. Based on these connections, the number of active nodes and transmitted packets is minimized. Simulation results show that this scheme can significantly increase the energy efficiency and maintain the monitoring area in solar-powered WSNs.     

A study of process monitoring based on inverse Gaussian distribution

The inverse Gaussian distribution has considerable applications in describing product life, employee service times, and so on. In this paper, the average run length (ARL) unbiased control charts, which monitor the shape and location parameters of the inverse Gaussian distribution respectively, are proposed when the in-control parameters are known. The effects of parameter estimation on the performance of the proposed control charts are also studied. An ARL-unbiased control chart for the shape parameter with the desired ${\mathit{ARL}}_0$, which takes the variability of the parameter estimate into account, is further developed. The performance of the proposed control charts is investigated in terms of the ARL and standard deviation of the run length. Finally, an example is used to illustrate the proposed control charts.     

Crystal structure and microwave dielectric properties of (Ba1−αSrα)6−3xSm8+2xTi18O54 solid solutions

The tungstenbronze-type-like (Ba_1−αSr_α)_6−3xR_8+2xTi₁₈O₅₄ (R = rare earth) solid solutions have been studied. Microwave dielectric properties of these solid solutions link to the substitution of large cations such as Ba ions by small Sr ions. In these solid solutions for x = 0, the quality factor (Q·f) exibits extremely low due to the inner strain resulting by the occupation of large Ba ions at relatively small A1-sites. However, dielectric constant (ɛ_r) of this composition show a high value. In this paper, improvement of quality factor (Q·f) by substituting small Sr ions for Ba ions in (Ba_1−αSr_α)_6−3xSm_8+2xTi₁₈O₅₄ solid solutions, where x = 0, is reported. In addition, the relationship between crystal structure and microwave dielectric properties is discussed from the viewpoint of structural formula and occupational state of large cations such as Ba, Sr and Sm in A1-sites.     

An improved global-best harmony search algorithm for faster optimization

In this paper, an improved global-best harmony search algorithm, named IGHS, is proposed. In the IGHS algorithm, initialization based on opposition-based learning for improving the solution quality of the initial harmony memory, a new improvisation scheme based on differential evolution for enhancing the local search ability, a modified random consideration based on artificial bee colony algorithm for reducing randomness of the global-best harmony search (GHS) algorithm, as well as two perturbation schemes for avoiding premature convergence, are integrated. In addition, two parameters of IGHS, harmony memory consideration rate and pitch adjusting rate, are dynamically updated based on a composite function composed of a linear time-varying function, a periodic function and a sign function in view of approximate periodicity of evolution in nature. Experimental results tested on twenty-eight benchmark functions indicate that IGHS is far better than basic harmony search (HS) algorithm and GHS. In further study, IGHS has also been compared with other eight well known metaheuristics. The results show that IGHS is better than or at least similar to those approaches on most of test functions.     

A fuzzy neural network approach for online fault detection in waste water treatment process

In this paper, an effective strategy for fault detection of sludge volume index (SVI) sensor is proposed and tested on an experimental hardware setup in waste water treatment process (WWTP). The main objective of this fault detection strategy is to design a system which consists of the online sensors, the SVI predicting plant and fault diagnosis method. The SVI predicting plant is designed utilizing a fuzzy neural network (FNN), which is trained by a historical set of data collected during fault-free operation of WWTP. The fault diagnosis method, based on the difference between the measured concentration values and FNN predictions, allows a quick revealing of the faults. Then this proposed fault detection method is applied to a real WWTP and compared with other approaches. Experimental results show that the proposed fault detection strategy can obtain the fault signals of the SVI sensor online.     

An efficient and accurate approach of circular object detection in color images

Object detection is a critical step of many image recognition systems. In this paper, we discuss the problem of circular shape object detection in still color images. An isotropic edge detector merged with spatial information and region based analysis is employed to extract image edge and obtain accurate gradient information of edge pixels, which assures the high accuracy of the subsequent circle detection. Then three circle parameters are estimated efficiently with only one 2-dimensional accumulator array and one 1-dimensional accumulator array, which greatly reduces the storage requirements and time complexity of our approach. Experimental results show that our method is robust in locating objects with complete or incomplete or concentric circle boundary in real color images without any prior knowledge.     

Probabilistic pseudo-morphology for grayscale and color images

Mathematical morphology offers popular image processing tools, successfully used for binary and grayscale images. Recently, its extension to color images has become of interest and several approaches were proposed. Due to various issues arising from the vectorial nature of the data, none of them imposed as a generally valid solution. We propose a probabilistic pseudo-morphological approach, by estimating two pseudo-extrema based on Chebyshev inequality. The framework embeds a parameter which allows controlling the linear versus non-linear behavior of the probabilistic pseudo-morphological operators. We compare our approach for grayscale images with the classical morphology and we emphasize the impact of this parameter on the results. Then, we extend the approach to color images, using principal component analysis. As validation criteria, we use the estimation of the color fractal dimension, color textured image segmentation and color texture classification. Furthermore, we compare our proposed method against two widely used approaches, one morphological and one pseudo-morphological.