| 基于量子粒子群优化的网络入侵检测算法 |
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| 引用本文： | 徐磊,李永忠,李正洁.基于量子粒子群优化的网络入侵检测算法[J].计算机工程与应用,2011,47(36):102-104. |
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| 作者姓名： | 徐磊 李永忠 李正洁 |
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| 作者单位： | 江苏科技大学 计算机学院，江苏 镇江 212003 |
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| 基金项目： | 江苏省高校自然科学基金资助项目(No.05KJD52006); 江苏科技大学科研资助项目(No.2005DX006J) |
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| 摘 要： | 提出了一种将量子粒子群优化算法和半监督模糊核聚类算法相结合的混合算法，用以解决入侵检测算法中模糊聚类算法对初始值敏感，容易陷入局部最优的问题。该算法对少量标记数据进行监督聚类得到正确模型，运用这个模型指导大量未标记数据进行聚类，扩充标记数据集合，对仍没有确定标记的数据利用量子粒子群优化的模糊核聚类算法进行聚类，确定其标记类型。通过KDD CUP99实验数据的仿真，实验结果表明，该算法在入侵检测中能获得理想的检测率和误检率。
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| 关 键 词： | 入侵检测 量子粒子群优化 半监督聚类 核函数 |
| 修稿时间： | |
Network intrusion detection algorithm based on quantum-behaved particle swarm optimization |
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| XU Lei,LI Yongzhong,LI Zhengjie.Network intrusion detection algorithm based on quantum-behaved particle swarm optimization[J].Computer Engineering and Applications,2011,47(36):102-104. |
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| Authors: | XU Lei LI Yongzhong LI Zhengjie |
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| Affiliation: | Department of Computer，Jiangsu University of Science and Technology，Zhenjiang，Jiangsu 212003，China |
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| Abstract: | A hybrid algorithm based on quantum-behaved particle swarm optimization algorithm and semi-supervised fuzzy kernel clustering algorithm is proposed.It overcomes the drawbacks of fuzzy clustering methods which are sensitive to the initial cluster centers and easily trapped into local minima.The few labeled data can generate correct model with supervised clustering,and then the model aids to guide lots of unlabeled data to clustering,enlarges the numbles of labeled data.Those data that still can’t be labeled are clustered by the fuzzy kernel method based on quantum-behaved particle swarm optimization,and determine marker types.KDD CUP99 data set is implemented to evaluate the proposed algorithm.Compared to other algorithms,the results show the outstanding performance of the proposed algorithm. |
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| Keywords: | intrusion detection Quantum-behaved Particle Swarm Optimization(QPSO) semi-supervised clustering kernel function |
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