Eclipse将显示出未知异常的错误。
使用未知的用户名和/或密码进行尝试，以了解SAP 如何抛出异常。
Try with an unknown username and/or password to see how SAP throws an exception.
它们也不可能因为异常的发生而处于一种未知的或是无法预期的状态中。
They can also never exist in unknown or undesirable state because of an exception.
它会捕获很多标准异常，如“未知uri”和“拒绝访问”，并将这些异常转为适当的HTTP相应(在这里各为404和403)。
It catches many standard exceptions, such as "URI unknown" and "access denied," and turns them into the proper HTTP responses in this case, 404 and 403, respectively.
“这个城市本来就应该是这样的，”我想着，对即将到来的一天和它将带来的未知之数感到异常担心。
"This is what the city is supposed to be about," I thought, 4 dreading the morning to come and all the uncertainty it held.
内核处于你的Linux系统的心脏部位，内核崩溃通常是由于硬件动作异常而导致内核强制进入系统内存的未知区域。
The kernel is at the heart of your Linux system, and a panic is usually caused by misbehaving hardware forcing the kernel into uncharted areas of your system's memory.
该模型可以发现已知的和未知的滥用入侵和异常入侵活动，具有自学习、自完善功能。
The model with the functions of self-learning and self-completing can detect the known and novel intrusion activities.
通过比较当前的系统行为模式和已有的模式规则的相似度来发现已知或者未知的误用入侵和异常入侵活动。
Then, we can compare the current action pattern with the pattern in the pattern database to find out the known or unknown misuse intrusions and anomaly intrusions.
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.
利用协议分析技术发现网络中的异常报文，标识出未知攻击，发现攻击者使用的躲避技术和变种攻击。
And according to Protocol Analysis detection, it can flag the anomaly traffic, and detect some attack variations, and resist attackers' obfuscation attempts.
提出了一种k-均值聚类算法和SOM自组织神经网络算法相结合的异常检测模型，使得系统可以更好的分类正常数据流和异常数据流，以此来防范未知的攻击。
Secondly, the anomaly detection model based on K-means algorithm and SOM network is constructed. It can classify the normal and abnormal network data stream so better to detect the unknown attack.
针对网络入侵的不确定性导致异常检测系统误报率较高的不足，提出一种基于Q-学习算法的异常检测模型（QLADM）。 该模型把Q-学习、行为意图跟踪和入侵预测结合起来，可获得未知入侵行为的检测和响应。
To the problems higher rate of false retrieval in anomaly detection system due to the uncertainty of intrusion, this paper presents an Anomaly Detection Model Based on Q- Learning Algorithm (QLADM).
针对网络入侵的不确定性导致异常检测系统误报率较高的不足，提出一种基于Q-学习算法的异常检测模型（QLADM）。 该模型把Q-学习、行为意图跟踪和入侵预测结合起来，可获得未知入侵行为的检测和响应。
To the problems higher rate of false retrieval in anomaly detection system due to the uncertainty of intrusion, this paper presents an Anomaly Detection Model Based on Q- Learning Algorithm (QLADM).
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