This paper focuses on discovering the potential attack behaviors by analyzing association rules in audit data.
本文通过对审计数据进行关联规则分析，发现潜在的攻击系统行为。
This model makes it hard to predict and prevent attack behaviors, and enhances the possibility of success attack.
这种模型使得攻击非常难以预测和防范，并大大提高了攻击成功的可能性。
By correlating the systems vulnerabilities and attackers behaviors, attack state graph(ASG) was introduced, and its generating algorithm presented.
在提取目标系统及其弱点信息和攻击行为特征的基础上，模拟攻击者的入侵状态改变过程，生成攻击状态图，并给出其生成算法。
Results Compared with controls, attack and explore behaviors of anxiety rats manifolded, and embellishment decreased(P<0.01).
结果空瓶刺激期间，同对照组相比，焦虑组探究、攻击行为明显增多，修饰行为减少（P< 0 0 1）。
Network traffic anomaly refers to the status that traffic behaviors depart from the normal behaviors, which has characteristics of a sudden attack and the unknown threatened characteristics.
网络流量异常是指网络的流量行为偏离其正常行为的情形，具有发作突然、先兆特征未知的特点，有可能在短时间内给网络及其设备带来极大的伤害。
Network traffic anomaly refers to the status that traffic behaviors depart from the normal behaviors, which has characteristics of a sudden attack and the unknown threatened characteristics.
网络流量异常是指网络的流量行为偏离其正常行为的情形，具有发作突然、先兆特征未知的特点，有可能在短时间内给网络及其设备带来极大的伤害。
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