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.     

Discourse cues to deception in the case of multiple receivers

Behavioral cues to deception are instrumental in detecting deception. As one of the primary sources of deception behavior, text has been analyzed at the level of sub-sentence or message but not the discourse of interaction. Additionally, empirical studies on cues to deception in the case of multiple receivers remain nonexistent. To fill these voids, we propose a discourse framework and six hypotheses about deception behaviors in a multi-receiver environment. The deception behaviors are operationalized by discourse features based on an analysis of real-world data. The results of statistical analysis validate the efficacy of discourse features in discriminating deceivers from truth-tellers.     

Face detection based on skin color likelihood

We propose a face detection method based on skin color likelihood via a boosting algorithm which emphasizes skin color information while deemphasizing non-skin color information. A stochastic model is adapted to compute the similarity between a color region and the skin color. Both Haar-like features and Local Binary Pattern (LBP) features are utilized to build a cascaded classifier. The boosted classifier is implemented based on skin color emphasis to localize the face region from a color image. Based on our experiments, the proposed method shows good tolerance to face pose variation and complex background with significant improvements over classical boosting-based classifiers in terms of total error rate performance.     

分光光度法测定饱和盐水中微量杂质

详细描述了用分光光度计测定饱和盐水中的微量铝、硅、铁、碘等杂质的分析方法，利用该方法对饱和盐水进行测定，证明该检测方法简便、快速、实用，能够满足生产需要。     

基于线性动态系统稀疏编码的异常事件检测

线性动态系统模型结合稀疏编码实现异常事件检测。线性动态系统可有效地捕捉动态纹理在时间和空间的转移信息,描述视频的时空小块。然而,线性动态系统属于非欧氏空间,无法直接用传统的稀疏编码进行异常检测。基于约束凸优化公式,将相似性变换与稀疏编码结合,可实现线性动态系统稀疏编码的优化求解。实验表明,所提出的方法具有更好的性能。     

入侵检测技术在消防信息网安全中的应用

消防信息网作为公安网子网,与互联网物理分隔,网络安全相对薄弱,自身的安全防护能力并不高。非法入侵、病毒破坏屡有发生,给消防信息网带来不可预测的后果。     

一种改进的VI CFAR检测器

为得到不同杂波背景下的良好检测性能,给出了一种改进的VI CFAR检测器.该检测器主要利用均值类可变性指示（MLVI）CFAR检测器和有序统计类可变性指示（OSVI）CFAR检测器的各自优点以及VI CFAR的以背景均匀程度自动选择不同检测器思想.理论分析和仿真结果表明：该检测器在均匀背景下性能基本与MLVI CFAR重合;多目标背景下具有较强的抗多目标能力;杂波边缘背景下有较好的虚警概率控制能力且运算量小,是一种稳健的检测器.     

Damage and deterioration mechanism and curing technique of concrete structure in main coal cleaning plants

Concrete structures in main coal cleaning plants have been rebuilt and reinforced in the coal mines of the Shanghai Da-tun Energy Sources Co. Ltd., the first colliery of the Pingdingshan Coal Co. Ltd. and the Sanhejian mine of the Xuzhou Mining Group Co. Ltd. In these projects, the operating environment and reliability of concrete structures in the main plants of the three companies were investigated and the safety of the structures inspected. Qualitative and quantitative analyses were made on the spe-cial natural, technological and mechanical environments around the structures. On the basis of these analyses, we discuss the long-term, combined actions of the harsh natural (corrosive gases, liquids and solids) and mechanical environments on concrete structures and further investigated the damage and deteriorating mechanisms and curing techniques of concrete structures in the main coal cleaning plants. Our study can provide a theoretical basis for ensuring the reliability of concrete structures in main coal cleaning plants.