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.     

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.     

Cross-evaluation-based weighted linear optimization for multi-criteria ABC inventory classification

Multi-criteria ABC inventory classification (MCIC), which aims to classify inventory items by considering more than one criterion, is one of the most widely employed techniques for inventory control. This paper suggests a cross-evaluation-based weighted linear optimization (CE-WLO) model for MCIC that incorporates a cross-efficiency evaluation method into a weighted linear optimization model for finer classification (or ranking) of inventory items. The present study demonstrated the inventory-management-cost effectiveness and advantages of the proposed model using a simulation technique to conduct a comparative experiment with the previous, related investigations. We established that the proposed model enables more accurate classification of inventory items and better inventory management cost effectiveness for MCIC, specifically by mitigating the adverse effect of flexibility in the choice of weights and yielding a unique ordering of inventory items.     

Differentially private data fusion and deep learning Framework for Cyber–Physical–Social Systems: State-of-the-art and perspectives

The modern technological advancement influences the growth of the cyber–physical system and cyber–social system to a more advanced computing system cyber–physical–social system (CPSS). Therefore, CPSS leads the data science revolution by promoting tri-space information resource from a single space. The establishment of CPSSs increases the related privacy concerns. To provide privacy on CPSSs data, various privacy-preserving schemes have been introduced in the recent past. However, technological advancement in CPSSs requires the modifications of previous techniques to suit its dynamics. Meanwhile, differential privacy has emerged as an effective method to safeguard CPSSs data privacy. To completely comprehend the state-of-the-art developments and learn the field’s research directions, this article provides a comprehensive review of differentially private data fusion and deep learning in CPSSs. Additionally, we present a novel differentially private data fusion and deep learning Framework for Cyber–Physical–Social Systems , and various future research directions for CPSSs.     

自动焊机监测与参数化曲线控制系统设计

设计了自动焊机的监测与参数化曲线控制系统,以可视化方式为工艺人员改变焊接过程的控制参数提供了便利,该控制系统的数据库的数据存储与查询功能为工艺人员进行离线工艺分析提供了大量的数据,且该系统与焊机的数控系统密切配合,实现了焊接自动生产线的可靠监控.     

基于Word文档的数据隐藏方法

在分析当前数据隐藏与恢复相关技术研究基础上,参照微软官方最新公布的复合文档存储规范,从利用载体Word文档现有空间中未真正使用部分和适度扩展新空间两条思路出发,设计一种利用Word文档作载体隐藏秘密信息的解决方案,包括在文字、各种嵌入式对象及其相应控制结构数据中隐藏信息。实践表明,隐藏数据后的载体文档可以被正常拷贝和使用电子邮件传送,根据需要隐藏的信息随机选择使用该方案,被隐藏信息更不容易被侦测出来,被二次编辑后被隐藏信息一般不会被破坏,鲁棒性不错,具有一定的实用价值。     

ASP.NET中数据库的连接方法

网站建设中的核心技术是数据库技术,掌握数据库连接方法是掌握数据库操作的重要内容。对ASP.NET中的数据库的连接进行研究,并针对常用数据库给出相应的连接实例,使用该方法能较好地解决ASP.NET中数据库连接建立的问题     

GPS-北斗双模目标定位系统的设计

为解决目标远程监控的要求,设计了GPS-北斗双模目标定位系统,充分利用两种导航系统的优势,提高目标的定位精度。本文在阐述该系统工作原理的基础上,重点给出了系统的具体设计方法。     

基于以太网络的数据传输系统设计研究

随着电子通信技术的发展,分布式测控系统也应运而生,并且规模在逐渐的扩大。在大的发展趋势前提下,监测数据在远距离传输的时候,如何保证其高效、安全、可靠地传输,已经成为计算机产业热议的话题。嵌入式以太网的实现为数据传输系统的设计创造了有利条件。