面向MANET异常检测的分布式遗传k-means研究

针对移动自组网（MANET，mobile ad hoc networks）入侵检测过程中的攻击类型多样性和监测数据海量性问题，提出了一种基于改进k-means算法的MANET异常检测方法。通过引入划分贡献度的概念，可合理地计算各维特征在检测中占有的权重，并将遗传算法与快速聚类检测算法k-means相结合，解决了聚类检测结果容易陷入局部最优的问题，进而，提出了以上检测算法在MapReduce框架下的设计方案，利用种群迁移策略在分布式处理器上实现了并行聚类检测。实验结果证明了该方法的检测准确率和运行效率均优于传统聚类检测方法。

Research on distributed genetic k-means for anomaly detection in MANET

Aiming at the diversity and the large amount of monitoring data of MANET (mobile ad hoc networks), an anomaly detection method in MANET based on improved k-means algorithm was proposed. By introducing the classification the contribution degree, the weight of each dimension can be calculated reasonably, and genetic algorithm and k-means were combined to prevent the results of clustering from getting in local optimization. Then, the detection method under the framework of MapReduce was put forward, and parallel clustering was achieved by using population migration strategy . The experimental results show that the detection accuracy and efficiency of the proposed method are better than the traditional ones.