attack vt. 1.攻击 (opp. defend)。 2.非难,抨击。 3.着手,动手,投入。 4.(疾病)侵袭。 5.【化学】腐蚀。 We will not attack unless we are attacked; if we are attacked, we will certainly counter- attack。 人不犯我,我不犯人;人若犯我,我必犯人。 attack a task 动手工作。 attack a problem 着手解决问题。 Strong acids attack metals. 强酸对金属有腐蚀作用。 be attacked with (a disease) 害(病)。 n. 1.攻击 (opp. defense), 袭击。 2.抨击。 3.【航空】迎角,冲角,攻角。 4.着手,动手。 5.发作,发病。 6.(表演或竞赛中的)主动。 A- is the best defense. 进攻是最好的防御。 an attack formation 攻击队形。 have an attack of 为…所侵袭;害,患(病)。 make an attack on 攻击。 n. -er 攻击者。
Based on the researches of the detection and defense technology uptodate , we develop our own method to detect and defense dos attack . it integrates many technologies and can defense nearly all kinds of dos attacks 通过研究目前dos攻击检测技术和防御技术,我们提出了自己的检测方法和防御方法,我们的方法将多种技术有机结合,实现对各种dos攻击的防御。
The current internet key exchange protocol ike is too complicated to effectively prevent dos attack and has too much rounds , which will influence the performance and interoperability and cause a lot of security hole at the same time 目前ipsec的密钥交换协议ike过于复杂,不能有效的防止dos攻击,协议轮数太多,这些都严重影响了协议的性能和互操作性,同时导致了许多安全漏洞。
Under the critical condition that network intrusion activities become more rampant in recent years , especially quite a few ddos attacks have happened lately , this paper focuses on the research of detecting and defending dos attacks using dm ( data mining ) technology 针对近年来网络入侵行为日益猖獗,多起分布式拒绝服务( ddos )攻击事件发生的严峻现实,本文致力于利用数据挖掘技术对dos攻击检测和防护。
However , these systems are implemented using classic client / server or browser / server mode , whose extensibility is poor because the mode is restricted by its single server , whose resources and computing ability are too limited , moreover , it ’ s vulnerable to dos attack 但是这些系统都是使用客户端/服务器( c / s或b / s )计算模式来构建的,而这种计算模式由于受单一服务器的计算能力及资源的限制,可扩展性差。
With the development of internet , large numbers of vulnerabilities are discovered continually . hackers often exploit the vulnerabilities in computer software or configuration to implement unauthorized access , privilege escalation and dos attack . and all these badly compromise the system security 在internet高速发展的今天,大量的弱点信息不断地出现,黑客经常利用计算机软件或配置上存在的弱点,进行无授权访问、特权提升、 dos攻击等,严重地危害了系统安全。
Based on the study of prevalent dm technology in the detection of network intrusions and the characters of dos attacks , this paper presents a new idea to detect and defend dos attacks by integrating with packet analysis and flow analysis . that is in addition to traditionally producing association rules and frequent episodes rules from packets and connections , trend analysis algorithm is used to forecast and analyze the network flow , the compared results between forecast values and real values become one of the key attributes of rules to detect dos attacks 在对网络攻击检测中数据挖掘的应用和dos攻击流特征深入研究的基础上,创新地提出一种将数据包分析和流量分析共同用于dos攻击特征挖掘的思路,即除了运用传统对数据包和通信连接的关联规则挖掘和序列模式挖掘外,额外增加利用趋势分析算法对网络的流量预测和分析,并将流量预测值与实际值比较结果作为dos特征检测规则的关键属性之一。
Network attacks damaged networks and users , among which dos ( denial of service ) attacks become one of the common network attack techniques by the characteristics , such as extensive area , strong concealment , simpleness and efficiency , etc . dos attacks greatly affected the effective service of network and host systems , especially among which , ddos ( distributed denial of service ) attacks are greatly threatening internet , since they are difficult to recognize and defense due to their concealment and distribution 随着互联网的迅速普及和应用的不断发展,各种黑客工具和网络攻击手段也随之倍出,网络攻击导致网络和用户受到侵害,其中拒绝服务( dos , denialofservice )攻击以其攻击范围广、隐蔽性强、简单有效等特点成为常见的网络攻击技术之一,极大地影响网络和业务主机系统的有效服务。其中,尤其是分布式拒绝服务( ddos , distributeddenialofservice )攻击,由于其隐蔽性和分布性很难被识别和防御严重威胁着internet 。
For detection of the dos attacks , g2som ( generalized grey self - organizing maps ) is presented . the self - organizing maps is an artificial neural networks model and algorithm that implements a characteristic nonlinear projection from the high - dimensional space of signal data into a low - dimensional array of neurons in an orderly fashion , which is made by t . kohonen 自组织特征映射理论( som )具有聚类、自组织、自学习以及可视化的功能,已广泛的应用于模式识基于神经网络的入侵检测研究别、故障诊断、异常检测等领域。