网络流量监控系统的设计与实现

在网络技术高速发展的同时,互联网的网络安全问题已经成为广泛关注的焦点,恶意攻击、病毒感染、非法访问都是影响网络运行的主要问题,在目前网络安全体系中,针对流量的监控便是其中一种有效分析网络状况的办法,它将从流量的角度来监控网络是否存在异常情况。     

浅谈TCP拥塞控制与其改进思路

如今网络已经离不开人们的生活,发展势头迅猛,网络的终端设备不仅在数量上,种类也越来越多,在此发展之下,保证大量的网络数据传输与网络性能相协调是极其重要的,经典的TCP拥塞控制机制是网络发展历史中人们想出的一种解决网络拥塞的方法,但如何适应当今网络发展的需求相信还是靠人们不断的探索来寻找合适的答案。本文致力于对经典TCP拥塞控制机制的讨论并提出相应的改进思路     

A review of machine learning applications in IoT-integrated modern power systems

This paper aims to provide a systematic and comprehensive survey of state-of-the-art machine learning techniques and their potential applications in IoT-integrated power systems.     

有效的IPSec安全策略管理方案*

针对IPSec协议在安全策略管理存在的问题,引入信任管理的思想,介绍了一种基于信任管理的IPSec安全策略管理方案。该方案对分布式网络中的策略可以进行统一的描述,并通过一致性证明能够实现策略的委托授权管理,这样大大提高了IPSec安全策略管理效率和IPSec的灵活性。     

LabVIEW 7 Express平台上的液压测试系统

为了满足工程实践对先进液压测试技术的需要，研究了基于美国NI公司最新推出的LabVIEW 7 Express虚拟仪器开发平台的液压测试系统的软硬件结构，介绍了测试系统实现网络通讯、数据库链接和多种编程语言联合作业的方法，提出了现代液压测试系统的技术方案。     

小波分析与漏磁检测信号处理

详细介绍了小波分析的基本理论,并将其引入到钢管漏磁探伤信号的分析中,可以去除噪声、压缩数据和检测奇异性,而且为缺陷的定性和定量分析提供了新的判定方法.     

2000—2014年辽宁省植被动态演变特征分析

基于MODIS － NDVI 数据,辅以线性回归法与分段线性回归法,并借助ArcGIS 软件,对辽宁 省2000—2014 年植被覆盖的动态演变过程进行分析。结果表明: ( 1) 时间上,辽宁省植被NDVI 在年 际尺度上呈现出明显的增大趋势,2005 年出现突变,多年平均NDVI 值为0. 496; 春季、夏季、秋季 以及植被生长季NDVI 突变年份分别为2006 年、2005 年、2009 年和2004 年,秋季波动变化的突变点 明显滞后; 植被生长最旺盛的季节为夏季,且集中于8 月。( 2) 空间上,辽宁省植被覆盖具有明显的 地域性差异,呈现出东部高、中西部低的分布特征; 辽宁省植被覆盖优良区与辽东山地的界限基本吻 合,植被覆盖贫乏区主要集中在朝阳市和阜新市的东北部地区。( 3) 辽宁省植被覆盖程度呈山地阴坡 高于阳坡的形态,并且植被覆盖程度最好的坡向为北偏西方向。( 4) 2000—2014 年辽宁省植被覆盖度 整体以维持现状和轻微改善为主,保持不变的区域集中于中东部地区,辽阳市与沈阳市一带有轻微退 化现象,辽西北地区改善情况较为明显。     

Improving the performance of Invertible Bloom Lookup Tables

Invertible Bloom Lookup Tables (IBLTs) have been recently introduced as an extension of traditional Bloom filters. IBLTs store key-value pairs. Unlike traditional Bloom filters, IBLTs support both a lookup operation (given a key, return a value) and an operation that lists out all the key-value pairs stored. One issue with IBLTs is that there is a probability that a lookup operation will return “not found” for a key. In this paper, a technique to reduce this probability without affecting the storage requirement and only moderately increasing the search time is presented and evaluated. The results show that it can significantly reduce the probability of not returning a value that is actually stored in the IBLT. The overhead of the modified search procedure, compared to the standard IBLT search procedure, is small and has little impact on the average search time.     

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.     

Incrementally building frequent closed itemset lattice

A concept lattice is an ordered structure between concepts. It is particularly effective in mining association rules. However, a concept lattice is not efficient for large databases because the lattice size increases with the number of transactions. Finding an efficient strategy for dynamically updating the lattice is an important issue for real-world applications, where new transactions are constantly inserted into databases. To build an efficient storage structure for mining association rules, this study proposes a method for building the initial frequent closed itemset lattice from the original database. The lattice is updated when new transactions are inserted. The number of database rescans over the entire database is reduced in the maintenance process. The proposed algorithm is compared with building a lattice in batch mode to demonstrate the effectiveness of the proposed algorithm.