计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 518-523.doi: 10.11896/jsjkx.200700129
俞建业1, 戚湧1, 王宝茁2
YU Jian-ye1, QI Yong1, WANG Bao-zhuo2
摘要: 随着5G等技术在车联网领域中被广泛应用,入侵检测作为车联网信息安全重要的检测工具发挥着越来越重要的作用。由于车联网结构变化快,数据流量大,入侵形式复杂多样,传统检测方法无法确保其准确性和实时性要求,不能直接被应用到车联网。针对这些问题,提出了一种基于Apache Spark框架的车联网分布式组合深度学习入侵检测方法,通过构建Spark集群,将深度学习卷积神经网络(Convolutional Neural Networks,CNN)和长短期记忆网络(LSTM)组合,进行车联网入侵特征提取和数据检测,从大规模车联网数据流量中发现异常行为。实验结果证明,与其他现有模型相比,该模型算法在时间上最快达到20.1s,准确率最高可达99.7%,具有较好的检测效果。
中图分类号:
[1] HALIMAA A A,SUNDARAKANTHAM K.Machine learning based intrusion detection system[C]//Proceedings of the International Conference on Trends in Electronics and Informatics(ICOEI 2019).2019:916-920. [2] VINAYAKUMAR R,SOMAN K P,POORNACHANDRANY P.Applying convolutional neural network for network intrusion detection[C]//2017 International Conference on Advances in Computing,Communications and Informatics(ICACCI 2017).2017:1222-1228. [3] DONG B,WANG X.Comparison deep learning method to traditional methods using for network intrusion detection[C]//Proceedings of 2016 8th IEEE International Conference on Communication Software and Networks(ICCSN 2016).IEEE,2016:581-585. [4] ISHAQUE M,HUDEC L.Feature extraction using Deep Learning for Intrusion Detection System[C]//2nd International Conference on Computer Applications and Information Security(ICCAIS 2019).IEEE,2019:1-5. [5] YAO Y,YANG W,GAO F X,et al.Anomaly intrusion detection approach using hybrid MLP/CNN neural network[J].Proceedings-ISDA 2006:Sixth International Conference on Intelligent Systems Design and Applications,2006,2(60473073):1095-1102. [6] PENG W,KONG X,PENG G,et al.Network intrusion detection based on deep learning[C]//Proceedings-2019 International Conference on Communications,Information System,and Computer Engineering(CISCE 2019).IEEE,2019:431-435. [7] DING W H,ZHOU K,LONG Y Y,et al.Research on Intrusion Detection Based on deep convolution neural network[J].Computer Science,2019(10):1-11. [8] CHOCKWANICH N,VISOOTTIVISETH V.Intrusion Detec-tion by Deep Learning with TensorFlow[J].International Conference on Advanced Communication Technology,ICACT,Global IT Research Institute (GIRI),2019(2):654-659. [9] DOBSON A,ROY K,YUAN X,et al.Performance Evaluation of Machine Learning Algorithms in Apache Spark for Intrusion Detection[C]//2018 28th International Telecommunication Networks and Applications Conference(ITNAC 2018).IEEE,2019:1-6. [10] MENG X,BRADLEY J,YAVUZ B,et al.MLlib:machine lea-ning in apache spark[J].Computer Science,2015, 17(1):1235-1241. [11] CHEUNG L.The rise and predominance of Apache Spark,” infoworld.com[OL].https://www.infoworld.com/article/3216144/spark/ apache-spark.html. [12] HOCHREITER SSCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780. [13] REVATHI S,MALATHI A.A detailed analysis on NSL-KDD dataset using various machine learning techniques for Intrusion detection[J].ESRSA Publications,2013. [14] MOUSTAFA N,SLAY J.Unsw-nb15:a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set)[C]//2015 Military Communications and Information Systems Conference(MilCIS).IEEE,2015:1-6. [15] ZHOU F Y,JIN L P,DONG J.Summary of Research on Convolutional Neural Networks[J].Chinese Journal of Computers,2017,40(6):1229-1251. [16] ANZER A,ELHADEF M.A multilayer perceptron-based dis-tributed intrusion detection system for internet of vehicles[C]//Proceedings-4th IEEE International Conference on Collaboration and Internet Computing(CIC 2018).IEEE,2018:438-445. [17] VIMALKUMAR K,RADHIKA N.A big data framework for intrusion detection in smart grids using apache spark[C]//2017 International Conference on Advances in Computing,Communications and Informatics(ICACCI 2017).2017:198-204. [18] FERRAG M A,MAGLARAS L,MOSCHOYIANNIS S,et al.Deep learning for cyber security intrusion detection:Approaches,datasets,and comparative study[J].Information Security Technical Report,2020,50(2):102419.1-102419.19. [19] RING M,WUNDERLICH S,SCHEURING D,et al.A survey of network-based intrusion detection data sets[J].Computers and Security,Elsevier Ltd,2019,86:147-167. [20] SEDJELMACI H,SENOUCI S M,ABU-RGHEFF M A.An efficient and lightweight intrusion detection mechanism for service-oriented vehicular networks[J].IEEE Internet of Things Journal,IEEE,2014,1(6):570-577. [21] GAO Y,WU H,SONG B,et al.A distributed network intrusion detection system for distributed denial of service attacks in vehicular ad hoc network[J].IEEE Access,2019,7:154560-154571. |
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