计算机科学 ›› 2021, Vol. 48 ›› Issue (6): 324-331.doi: 10.11896/jsjkx.200400033
贾琳1, 杨超1,2,3, 宋玲玲1, 程镇1, 李琲珺1
JIA Lin1, YANG Chao1,2,3, SONG Ling-ling1, CHENG Zhen1and LI Bei-jun1
摘要： 否定选择算法(Negative Selection Algorithm,NSA)作为人工免疫系统的典型算法被广泛应用于入侵检测中。针对传统否定选择算法在处理入侵检测问题时出现的准确率低、误报率高以及检测器集合冗余度高等问题,提出了一种改进的否定选择算法并将其应用到入侵检测中。其主要思想是:首先通过密度峰值聚类算法对非自体抗原进行聚类,生成一类已知检测器,该检测器可检测已知入侵行为;然后定义异常点并将其优先作为候选检测器中心,计算和生成未知检测器,该检测器可检测未知入侵行为,以此降低检测器生成的随机性。在实验阶段,选择准确率(Accuracy,AC)和误报率(False Alarm,FA)作为评价指标。分别在KDDCUP99和CSE-CIC-IDS2018数据集上进行了仿真实验,实验结果表明,所提算法在这两种数据集上均有较低的误报率和较高的准确率,这验证了其具有较好的检测效果。
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| [1]LI W,YANG Z M.Review of Intrusion Detection System[J].Journal of Jilin University(Information Science Edition),2016,34(5):657-662. [2]WEI Z,YANG W C,WILLY S.Interactive three-dimensional visualization of network intrusion detection data for machine learning[J].Future generation computer systems,2020,102(Jan.):292-306. [3]MAHENDRA P,SACHIN T.An efficient feature selectionbased Bayesian and Rough set approach for intrusion detection[J].Applied Soft Computing,2020,87(2):105980. [4]HYUN M S,JIYOUNG W,HUY K K.In-vehicle network intrusion detection using deep convolutional neural network[J].Vehicular Communications,2020,21(2):100198.1-100198.13. [5]VIJAYANAND R,DEVARAJ D,KANNAPIRAN B.Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection[J].Computers & Security,2018,77(8):304-314. [6]SAIFUL I S,MUHAMMAD S A.Network Intrusion Detection System Using Artificial Immune System(AIS)[C]//International Conference on Computer and Communication Systems.2018:178-182. [7]CHUNG M O.Host-based Intrusion Detection Systems Inspired by Machine Learning of Agent-Based Artificial Immune Systems[C]//IEEE International Symposium on INnovations in Intelligent Systems and Applications.2019:1-5. [8]NASIR R,JAVAID I,FAHAD M.Artificial Immune System-Negative Selection Classification Algorithm(NSCA) for Four Class Electroencephalogram(EEG) Signals[J].Frontiers in Human Neuroscience,2018,12:439-453. [9]TAKESHI O,MITSUNOBU T.An artificial immunity-enhancing module for internet servers against cyberattacks[J].Artificial Life and Robotics,2018,23(3):292-297. [10]FORREST S,PERELSON A S,ALLEN L,et al.Self-nonselfdiscrimination in a computer[M].IEEE Computer Society,1994:202-212. [11]SYDNEY M K,SUN Y X.A deep learning method with wrapper based feature extraction for wireless intrusion detection system[J].Computer & Security,2020,92(5):1-15. [12]GUO X D,LI X M.Intrusion detection based on improvedsparse denoising autoencoder[J].Journal of Computer Applications,2019,39(3):153-157. [13]ZHOU Y Y,CHENG G.Building an efficient intrusion detection system based on feature selection and ensemble classifier[J].Computer Networks,2020,174(19):1-12. [14]HE F M,MA H Z.Research on Anomaly Intrusion Detection System Based on Feature Grouping Clustering[J].Computer Engineering,2020,46(4):123-128. [15]MRAIN E P,VASIL A S,VLADIMIR K P,et al.Negative Selection and Neural Network Based Algorithm for Intrusion Detection in IoT[C]//International Conference on Telecommunications and Signal Processing(TSP).2018. [16]BHUVANESWARI G,MANIKANDAN G.An intelligent in-trusion detection system for secure wireless communication using IPSO and negative selection classifier[J].Cluster Computing,2019,22(5):12429-12441. [17]JIN J,HAN H,CUI Y J.Application of improved negative select algorithm in intrusion detection system[J].Electronic Design Engineering,2015,23(1):7-9. [18]LIU H H,NIU L,KONG W W,et al.Technique for Intrusion Detection Based on Dual Negative Splitting Selection Algorithm[J].Fire Control &Command Control,2018,43(10):181-186. [19]CHIKH R,SALIM C.A New Negative Selection Algorithm for Adaptive Network Intrusion Detection System[J].International Journal of Information Security and Privacy IJISP,2014,8(4):1-25. [20]JIN Z Z,LIAO M H,XIAO G.Survey of negative selection algorithms[J].Journal on Communications,2013,34(1):159-170. [21]ZHOU J,DASGUPTA D.Real-valued negative selection algo-rithm with variable-sized detectors[J].Genetic and Evolutio-nary Computation-GECCO,2004:287-298. [22]RODRIGUEZ A,LAIO A.Clustering by fast search and find of density peaks[J].Science,2014,344(6191):1492-1496. [23]MA C L,SHAN H,MA T.Improved Density Peaks Based Clustering Algorithm with Strategy Choosing Cluster Center Automatically[J].Computer Science,2016,43(7):255-258,280. [24]YANG C,JIA L,CHEN B Q,et al.Negative Selection Algorithm Based on Antigen Density Clustering[J].IEEE Access,2020,8:44967-44975. [25]ZHANG X Y,ZENG H S,JIA L.Research of intrusion detection system dataset-KDD CUP99[J].Computer Engineering and Design,2010,31(22):4809-4813. [26]UCI data set[OL].http://archive.ics.uci.edu/ml/index.php. [27]CSE-CIC-IDS2018 data set[OL].https://www.unb.ca/cic/datasets/ids-2018.html. [28]NIU L,SUN Z L.PCA-AKM Algorithm and Its Application in Intrusion Detection System[J].Computer Science,2018,(45)2:226-230. |
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