基于嵌入式技术的溶解氧检测系统设计

水中的溶解氧是各种水生生物呼吸代谢的基础,其含量的高低是水质好坏的一个重要指标.如何对水质中的溶解氧参数进行科学合理的检测,在水产养殖中具有重要的参考意义[1].文中提出了一种基于嵌入式Web的溶解氧检测系统,以ARM9开发板作为采集终端和嵌入式网关的核心,构建系统的软硬件平台及Web服务器.系统可以对水质水量中的温度、溶解氧参数进行科学检测,同时将数据接入以太网,用户可通过客户端IE浏览器实现对检测的数据进行远程在线监测.     

基于HTTP协议的服务器程序分析

绝大多数的Web开发,都是构建在HTTP协议之上的Web应用。为了分析基于HTTP协议服务器程序以及浏览器与服务器的交互过程,以一个简单的基于HTTP协议中服务器程序为实例,探讨怎样使用多线程与异步操作的理论,运用WinSock编程来逐步解析HTTP协议的服务器程序的核心部分,这样既可达到避免调用线程阻塞的目的,又可提高服务器程序的可响应性。     

高性能的HTTP检测方法

高性能网络指的是具有高数据流量、高处理能力的网络。由于这样的网络在单位时间内需要处理的信息量非常庞大,因而对IDS也有较高的要求。一方面,IDS的检测速度必须能跟上网络数据的流速;另一方面,检测结果必须准确,漏报和误报必须被限     

嵌入式Web服务器的实现及其CGI应用

嵌入式设备资源有限,在这种情况下实现Web服务功能比较困难。针对嵌入式设备这种特点,本文通过比较几种Web服务器的优缺点,选定了Shttpd作为嵌入式Web服务器,并对其在Linux嵌入式平台下进行了移植。文中详细解析了CGI技术的相关原理,并结合HTML表单和CGI技术,给出了用C语言编写的基于CGIC库的串口程序,实现了客户端与服务端的动态交互。     

SIP代理服务器抗拒绝服务攻击自防御模型

通过对拒绝服务攻击的原理、类型、特征及SIP网络面对的典型DoS攻击的分析,结合Client Puzzle思想、DFA、远程联动、本机联动技术及SIP网络特点、SIP代理服务器工作原理,提出了SIP代理服务器抗拒绝服务攻击自防御模型,并设计相应的实验环境对该模型抵御DoS攻击的能力进行测试,实验结果证明该模型在增加代理服务器转发信令毫秒级的时间延迟下,可有效地抵御针对SIP代理服务器发起的DoS攻击,增强SIP系统服务的可用性.     

基于ARM9的WEB服务器设计与实现

设计并实现了一种基于S3C2440开发板上使用linux操作系统做为开发平台,开发并实现了嵌入式Web服务.介绍了此系统的硬件组成部分和软件组成部分.此系统融入了嵌入式,网络通信等技术.在对建好的Web服务器测试表明系统稳定,反应速度快,便于控制.并且该方案具有成本低、体积小、易于安装等优点,具有广阔的应用前景.     

基于Vmware的server2008web服务器配置实验综述报告

随着我国当前科学技术的发展与传承,针对基于Vmware的server2008 web服务器配置实验研究越来越重视,要想更加科学的展示出整个实验配置水平,必须要按照实验配置分析中的要求,将实验配置中的相关要素完善,保障在相应要素的配置整合过程中,能够为server2008 web服务器配置水平提升奠定基础。鉴于此,本文以文献综述角度,针对Vmware应用下的server2008 web服务器配置实验综述进行了分析,希望在本文研究帮助下,能够为Vmware的server2008 web服务器配置实验研究应用提供参考。     

检测DDos攻击的方法研究

DDos攻击已经严重影响网络的安全,文中对两种DDos检测方法进行了研究,包括基于流连接密度和流特征熵的方法.介绍了这两种方法的核心思想,给出了检测的过程,并分析了它们的应用范围.实验表明,这两种方法能够有效检测DDos攻击.     

DDoS attack detection in IEEE 802.16 based networks

Achieving high data rate transmission, WiMAX has acquired noticeable attention by communication industry. One of the vulnerabilities of the WiMAX network which leads to DDoS attack is sending a high volume of ranging request messages to base station (BS) in the initial network entry process. In the initial network entry process, BS and subscriber station (SS) exchange management messages. Since some of these messages are not authenticated, malicious SSs can attack the network by exploiting this vulnerability which may increase the traffic load of the BS and prevent it from serving the SSs. So, detecting such attacks is one of the most important issues in such networks. In this research, an artificial neural network (ANN) based approach is proposed in order to detect DDoS attacks in IEEE 802.16 networks. Although lots of studies have been devoted to the detection of DDoS attack, some of them focus just on some statistical features of the traffic and some other focus on packets’ headers. The proposed approach exploits both qualitative and quantitative methods. It detects the attack by feeding some features of the network traffic under attack to an appropriate ANN structure. To evaluate the method, first a typical attacked network is implemented in OPNet simulator, and then by using the proposed system, the efficiency of the method is evaluated. The results show that by choosing suitable time series we can classify 93 % of normal traffic and 91 % of attack traffic.     

SD‐WLB: An SDN‐aided mechanism for web load balancing based on server statistics

Software‐defined networking (SDN) is a modern approach for current computer and data networks. The increase in the number of business websites has resulted in an exponential growth in web traffic. To cope with the increased demands, multiple web servers with a front‐end load balancer are widely used by organizations and businesses as a viable solution to improve the performance. In this paper, we propose a load‐balancing mechanism for SDN. Our approach allocates web requests to each server according to its response time and the traffic volume of the corresponding switch port. The centralized SDN controller periodically collects this information to maintain an up‐to‐date view of the load distribution among the servers, and incoming user requests are redirected to the most appropriate server. The simulation results confirm the superiority of our approach compared to several other techniques. Compared to LBBSRT, round robin, and random selection methods, our mechanism improves the average response time by 19.58%, 33.94%, and 57.41%, respectively. Furthermore, the average improvement of throughput in comparison with these algorithms is 16.52%, 29.72%, and 58.27%, respectively.     

基于PROBE的DDOS攻击检测分析

分布式拒绝服务(DDOS)攻击是目前严重威胁网络安全和影响网站服务质量的一种攻击手段DDOS攻击就是利用多个分布式攻击源向攻击对象发送超出攻击目标处理能力的海量数据包,来消耗可用系统和带宽资源,从而导致网络服务瘫痪的一种攻击.目前有很多方法检测和防御DDOS攻击,传统的检测和防范措施是基于特征匹配的检测往往要求有一定的先验知识难以区分突发正常流量与DDOS攻击.本文通过介绍PROBE技术来检测应对DDOS攻击,并探究了PROBE在DDOS攻击检测中的应用策略.     

入侵检测系统攻击语言研究

本文对入侵检测系统中现有的攻击语言进行了分类综述,在此基础上分析了这些语言存在的不足,提出应对现有语言进行合并,从而设计出一些综合性语言。这些综合语言应能从多个方面描述一个攻击,从而有利于数据的一致性和可重用性,推动攻击语言的标准化。本文最后给出了今后研究的方向。     

FD-DBN: Flow directed deep belief network for accurate anomaly detection in cloud computing

Security becomes the key concern in a cloud environment, as the servers are distributed throughout the globe and involve the circulation of highly sensitive data. Intrusions in the cloud are common because of the huge network traffic that paves the way for intruders to breach traditional security systems with sophisticated software. To avoid such problems, intrusion detection systems (IDSs) have been introduced by various researchers. Each IDS was developed to achieve a particular objective, that is, providing security by detecting intrusions. Most of the available IDS are inefficient and are unable to provide accurate classification. Also, some of them are computationally expensive to be implemented in practical scenarios. This article proposes a new and efficient IDS framework that can accurately classify the intrusion type through effective training to address the existing drawbacks. The proposed framework, named flow directed deep belief network (FD-DBN), involves three main phases: pre-processing, clustering, and classification. In pre-processing, certain data mining operations are carried out to clean the data. The clustering phase is carried out using the game-based k-means (GBKM) clustering algorithm. The clustered data is then provided as input to the FD-DBN classification framework, where the training process is carried out. The deep belief network (DBN) training is performed with dataset features, and the flow direction algorithm is adopted for tuning the weight parameters of DBN. Through tuning, the model yielded accurate classification outcomes. The simulations are done in Python 3.6, and the results proved that the proposed framework is much more effective than the existing IDS frameworks.     

Bandwidth requirements for accurate detection of direct path in multipath environment

The accurate detection of the direct path in a dense multipath environment is critical in time-based and angle-of-arrival-based location estimation techniques. It is generally assumed that an infinitely large bandwidth is helpful in the accurate detection of the direct path and high-range resolution. It is experimentally demonstrated that, for a wideband system, a bandwidth of up to 4 GHz, centred on 12.5 GHz, is sufficient for accurate detection of direct path in a typical indoor scattering environment. Additionally, the bandwidth gain, incremental range accuracy with bandwidth, is an effective indicator of the bandwidth requirements for accurate detection of the direct path.     

SIFT-Symmetry: A robust detection method for copy-move forgery with reflection attack

Copy-move forgery (CMF) is a popular image manipulation technique that is simple and effective in creating forged illustrations. The bulk of CMF detection methods concentrate on common geometrical transformation attacks (e.g., rotation and scale) and post-processing attacks (e.g., Joint Photographic Experts Group (JPEG) compression and Gaussian noise addition). However, geometrical transformation that involves reflection attacks has not yet been highlighted in the literature. As the threats of reflection attack are inevitable, there is an urgent need to study CMF detection methods that are robust against this type of attack. In this study, we investigated common geometrical transformation attacks and reflection-based attacks. Also, we suggested a robust CMF detection method, called SIFT-Symmetry, that innovatively combines the Scale Invariant Feature Transform (SIFT)-based CMF detection method with symmetry-based matching. We evaluated the SIFT-Symmetry with three established methods that are based on SIFT, multi-scale analysis, and patch matching using two new datasets that cover simple transformation and reflection-based attacks. The results show that the F-score of the SIFT-Symmetry method surpassed the average 80% value for all geometrical transformation cases, including simple transformation and reflection-based attacks, except for the reflection with rotation case which had an average F-score of 65.3%. The results therefore show that the SIFT-Symmetry method gives better performance compared to the other existing methods.     

人脸识别中视频回放假冒攻击的实时检测方法

利用合法用户的脸部视频进行回放假冒攻击,是目前人脸认证系统的重要安全威胁。针对此问题,本文提出了一种仅用单个普通摄像头来抵抗人脸视频假冒攻击的方法。不同于以往从人脸区域中获取假冒线索进行活体检测的方法,本文通过人脸输入图像与场景参考图像之间的背景对比,从人脸周围背景区域中寻求视频假冒攻击线索。首先,本文在尺度空间里构建人脸周围区域图像的背景特征点集合;然后,利用背景特征点集合建立识别场地背景和人脸背景的Gabor背景描述子,并用融合相位补差的相似度来进行背景比对。实验表明该方法能有效地识别视频回放假冒攻击。     

对抗隐身进攻兵器的光电探测研究

采用低可见性技术或隐身技术制造的进攻兵器是21世纪防空系统重点对抗的主要目标威胁。隐身兵器并非不可探测,采用常规的长波低频雷达可以探测低可见性目标,采用激光雷达、光电探测和雷达组网等均具有探测隐身的能力。     

基于极化状态—幅度—相位调制的高效无线传输方案

针对双极化通信场景下正交极化信号串扰导致的解调性能恶化问题,提出了一种基于正交向量的极化相关衰减效应消除方法. 该方法通过对发送信号进行设计,利用一组正交向量分别在发射端和接收端对极化信号进行处理,达到消除极化间串扰的目的,同时在发送端利用估计的信道参数处理发送信号,进一步消除非理想信道影响. 在不改变噪声功率的基础上,实现正交极化信号的无串扰接收,有效提高接收端信号解调性能. 理论分析和仿真结果表明,极化状态调制信号的解调误码率逼近高斯信道下的理论值,相比于预补偿方法和迫零矩阵法有更好的误码率性能,仿真结果与理论推导具有较好的一致性. 本文方法能有效解决无线信道中正交极化信号间串扰,提高接收端信号解调性能.     

Spatial spectrum approach for pilot spoofing attack detection in MIMO systems

In this study, a spatial spectrum method is proposed to cope with the pilot spoofing attack (PSA) problem by exploiting the of uplink–downlink channel reciprocity in time-division-duplex multiple-input multiple-output systems. First, the spoofing attack in the uplink stage is detected by a threshold derived from the predefined false alarm based on the estimated spatial spectrum. When the PSA occurs, the transmitter (That is Alice) can detect either one or two spatial spectrum peaks. Then, the legitimate user (That is Bob) and Eve are recognized in the downlink stage via the channel reciprocity property based on the difference between the spatial spectra if PSA occurs. This way, the presence of Eve and the direction of arrival of Eve and Bob can be identified at the transmitter end. Because noise is suppressed by a spatial spectrum, the detection performance is reliable even for low signal-noise ratios and a short training length. Consequently, Bob can use beamforming to transmit secure information during the data transmission stage. Theoretical analysis and numerical simulations are performed to evaluate the performance of the proposed scheme compared with conventional methods.     

基于wireshark的分布式拒绝服务攻击检测系统

wireshark是一款方便实用的网络封包分析软件,使用WinPCAP作为接口,与网卡直接进行数据报文交换。文章在使用wireshark获取网络封包的基础上,通过分析网络封包中TCP层包头在建立网络连接过程中协议标志位的特点,根据不同标志位信号在各端口的占比情况对例如SYN flood类型的分布式拒绝服务攻击进行检测。并介绍了基于wireshark的分布式拒绝服务攻击检测系统的总体设计和实现方法。