基于云计算的移动智能终端入侵检测方法研究

Mobile Intelligent Terminal Intrusion Detection Method Based on Cloud Computing

移动智能终端得到的数据中,存在大量与最终入侵检测无关的数据、冗余数据和伪装数据,在进行入侵检测时需要进行大量的迭代计算,导致入侵数据属性特征分类呈现高随机性。采用传统算法检测过程中,这种随机性使入侵数据属性特征参数受到干扰,导致检测准确率低,时间长。提出基于优化贝叶斯算法的云计算环境下移动智能终端入侵检测方法。对入侵数据的相似度进行计算,为入侵数据的检测提供依据。计算贝叶斯模型中各个入侵数据属性特征对先验概率进行更新,在入侵检测的过程中对入侵数据进行降维处理获得最大概率值,并进行准确分类,从而得到准确的入侵数据检测结果。仿真结果表明,利用改进算法进行云计算环境下的智能移动终端入侵检测,能够提高检测的准确率,缩短了检测时间,效果令人满意。

Based on the optimal hayes algorithm, a mobile intelligent terminal intrusion detection method under the cloud computing environment is presented. The similarity of intrusion data is calculated to provide the basis for the data of invasion. Each intrusion data attribute feature of the bayesian model is calculated and the prior probability is updated. In the process of intrusion detection, the intrusion data are processed by dimension reduction to gain the maximum probability value, and are accurately classified, so as to get the accurate detection of intrusion data. Simulation results show that the proposed algorithm can improve the detection accuracy and shorten the testing time, the effect is satisfactory.