工业互联网流量分析技术综述
Survey of industrial Internet traffic analysis technology
- 通信学报 2024年45卷第8期 页码：221-237
- 作者机构：
1.中国科学院信息工程研究所，北京 100085
2.中国科学院大学网络空间安全学院，北京 100049
- 作者简介：
[ "刘奇旭（1984- ），男，江苏徐州人，博士，中国科学院信息工程研究所研究员，中国科学院大学教授，主要研究方向为网络攻防技术、网络安全评测。" ]
[ "肖聚鑫（1999- ），男，江西萍乡人，中国科学院大学博士生，主要研究方向为网络攻防技术、物联网安全、网络流量分析等。" ]
[ "谭耀康（1998- ），男，广东肇庆人，中国科学院大学硕士生，主要研究方向为Web安全、程序分析。" ]
[ "王承淳（2000- ），男，山东济南人，中国科学院大学博士生，主要研究方向为异常流量检测、恶意软件分析等。" ]
[ "黄昊（2001- ），男，江西九江人，中国科学院大学博士生，主要研究方向为Web安全、AI安全等。" ]
[ "张方娇（1989- ），女，山东泰安人，博士，中国科学院信息工程研究所高级工程师，主要研究方向为Web安全、溯源取证等。" ]
[ "尹捷（1991- ），女，重庆人，博士，中国科学院信息工程研究所工程师，主要研究方向为恶意代码对抗、僵尸网络、物联网安全等。" ]
[ "刘玉岭（1982- ），男，山东济阳人，博士，中国科学院信息工程研究所正高级工程师，主要研究方向为网络安全测评和等级保护。" ]
- 基金信息：中国科学院青年创新促进会基金资助项目;国家电网有限公司科技基金资助项目(SG270000YXJS2311060);中国科学院网络测评技术重点实验室和网络安全防护技术北京市重点实验室基金资助项目
DOI：10.11959/j.issn.1000-436x.2024145
中图分类号： TP393收稿：2024-03-07，
修回：2024-07-22，
纸质出版：2024-08-25
- 稿件说明：
移动端阅览
刘奇旭,肖聚鑫,谭耀康等.工业互联网流量分析技术综述[J].通信学报,2024,45(08):221-237.
LIU Qixu,XIAO Juxin,TAN Yaokang,et al.Survey of industrial Internet traffic analysis technology[J].Journal on Communications,2024,45(08):221-237.
刘奇旭,肖聚鑫,谭耀康等.工业互联网流量分析技术综述[J].通信学报,2024,45(08):221-237. DOI： 10.11959/j.issn.1000-436x.2024145.
LIU Qixu,XIAO Juxin,TAN Yaokang,et al.Survey of industrial Internet traffic analysis technology[J].Journal on Communications,2024,45(08):221-237. DOI： 10.11959/j.issn.1000-436x.2024145.
摘要
为了深入理解流量分析技术在工业互联网中的应用，基于流量分析的5个主要步骤阐述了工业互联网区别于传统互联网的独特性。同时，通过调研大量相关研究工作，总结了流量预测、协议识别与逆向、工业资产指纹识别、入侵检测、加密流量识别和漏洞挖掘6个主流研究任务，并根据任务性质将其分类为面向服务质量提高和面向安全能力提升的2类应用，充分挖掘了工业互联网中的流量分析技术应用场景。最后，针对流量分析未来进一步应用于工业互联网所面临的挑战进行了讨论，并展望了潜在的研究方向。
Abstract
To gain an in-depth awareness of the application of traffic analysis technology in the industrial Internet
the differences between the industrial Internet and the traditional Internet through the five core traffic analysis processes were illustrated. By reviewing a large number of related papers
the application of six popular were summarized in the industrial Internet
such as traffic prediction
protocol identification and reverse engineering
industrial asset fingerprinting
intrusion detection
encrypted traffic identification and vulnerability mining. Depending on the nature of the task
traffic analysis technology was classified into two types of applications
such as service quality enhancement and security capability development
allowing to thoroughly explore the application scenarios of traffic analysis technology in the industrial Internet. Finally
the challenges associated with future traffic analysis applications in the industrial Internet were examined
as well as potential development possibilities.
关键词
Keywords
references
张恒升 , 赵锋 , 刘东坡 . 工业互联网 总体网络架构 : GB/T 42021-2022 [S ] . 2022 .
ZHANG H S , ZHAO F , LIU D B . Industrial Internet overall network architecture : GB/T 42021-2022 [S ] . 2022 .
张云明 . 数实融合-数智赋能 高质量推进新型工业化 [R ] . 2023 .
ZHANG Y M . Digital real integration-digital intelligence empowers high quality promotion of new industrialization [R ] . 2023 .
郝志强 , 刘冬 , 王冲华 . 工业领域网络流量安全分析关键技术研究 [J ] . 工业信息安全 , 2022 ( 3 ): 27 - 35 .
HAO Z Q , LIU D , WANG C H . Research on key techniques of industrial network traffic security analysis [J ] . Industry Information Security , 2022 ( 3 ): 27 - 35 .
SCHOLTEN B . The road to integration: a guide to applying the I-SA-95 standard in manufacturing [M ] . Durham : ISA , 2007 .
Functional architecture of industrial Internet system for industrial automation applications : IEC PAS 63441 [S ] . 2022 .
SVERKO M , GRBAC T G , MIKUC M . SCADA systems with focus on continuous manufacturing and steel industry: a survey on architectures, standards, challenges and industry 5.0 [J ] . IEEE Access , 2022 , 10 : 109395 - 109430 .
MOAYEDI H Z , MASNADI-SHIRAZI M A . ARIMA model for network traffic prediction and anomaly detection [C ] // Proceedings of the 2008 International Symposium on Information Technology . Piscataway : IEEE Press , 2008 : 1 - 6 .
EDDY S R . What is a hidden Markov model? [J ] . Nature Biotechnology , 2004 , 22 ( 10 ): 1315 - 1316 .
ABBASI M , SHAHRAKI A , TAHERKORDI A . Deep learning for network traffic monitoring and analysis (NTMA): a survey [J ] . Computer Communications , 2021 , 170 : 19 - 41 .
ALQUDAH N , YASEEN Q . Machine learning for traffic analysis: a review [J ] . Procedia Computer Science , 2020 , 170 : 911 - 916 .
KOAY A M Y , KO R K L , HETTEMA H , et al . Machine learning in industrial control system (ICS) security: current landscape, opportunities and challenges [J ] . Journal of Intelligent Information Systems , 2023 , 60 ( 2 ): 377 - 405 .
FIGUEROA-LORENZO S , AÑORGA J , ARRIZABALAGA S . A survey of IIoT protocols: a measure of vulnerability risk analysis based on CVSS [J ] . ACM Computing Surveys , 2020 , 53 : 1 - 53 .
刘奇旭 , 陈艳辉 , 尼杰硕 , 等 . 基于机器学习的工业互联网入侵检测综述 [J ] . 计算机研究与发展 , 2022 , 59 ( 5 ): 994 - 1014 .
LIU Q X , CHEN Y H , NI J S , et al . Survey on machine learning-based anomaly detection for industrial Internet [J ] . Journal of Computer Research and Development , 2022 , 59 ( 5 ): 994 - 1014 .
LU K D , ZENG G Q , LUO X Z , et al . Evolutionary deep belief network for cyber-attack detection in industrial automation and control system [J ] . IEEE Transactions on Industrial Informatics , 2021 , 17 ( 11 ): 7618 - 7627 .
SHEN M , YE K , LIU X T , et al . Machine learning-powered encrypted network traffic analysis: a comprehensive survey [J ] . IEEE Communications Surveys & Tutorials , 2023 , 25 ( 1 ): 791 - 824 .
康梦轩 , 宋俊平 , 范鹏飞 , 等 . 基于深度学习的网络流量预测研究综述 [J ] . 计算机工程与应用 , 2021 , 57 ( 10 ): 1 - 9 .
KANG M X , SONG J P , FAN P F , et al . Survey of network traffic forecast based on deep learning [J ] . Computer Engineering and Applications , 2021 , 57 ( 10 ): 1 - 9 .
GUO Y G , CONG H Y , GUO K Y , et al . A GRNN-based traffic reconstruction algorithm to surface wave-based power line communications [C ] // Proceedings of the 2019 IEEE International Conference on Industrial Internet (ICII) . Piscataway : IEEE Press , 2019 : 138 - 142 .
LI Q M , WANG S , LIU Y Z , et al . Traffic self-similarity analysis and application of industrial Internet [J ] . Wireless Networks , 2020 , 30 ( 5 ): 3571 - 3585 .
SUN X C , MA S H , LI Y Q , et al . Enhanced echo-state restricted Boltzmann machines for network traffic prediction [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 2 ): 1287 - 1297 .
NIE L S , NING Z L , OBAIDAT M S , et al . A reinforcement learning-based network traffic prediction mechanism in intelligent Internet of Things [J ] . IEEE Transactions on Industrial Informatics , 2021 , 17 ( 3 ): 2169 - 2180 .
NIE L S , WANG X J , WANG S P , et al . Network traffic prediction in industrial Internet of things backbone networks: a multitask learning mechanism [J ] . IEEE Transactions on Industrial Informatics , 2021 , 17 ( 10 ): 7123 - 7132 .
WANG H , BAI Y X , XIE X . Dynamic resource allocation for 5G-enabled industrial Internet of Things system with delay tolerance [C ] // Proceedings of the 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) . Piscataway : IEEE Press , 2022 : 1 - 6 .
WANG R R , ZHANG Y , PENG L M , et al . Time-varying-aware network traffic prediction via deep learning in IIoT [J ] . IEEE Transactions on Industrial Informatics , 2022 , 18 ( 11 ): 8129 - 8137 .
LIU X T , HUANG C H , ASHRAF M W A , et al . Spatiotemporal self-attention-based network traffic prediction in IIoT [J ] . Wireless Communications and Mobile Computing , 2023 ( 1 ): 8331642 ..
ZHAO J H , PENG G J . Industrial Internet network slice prediction algorithm based on multidimensional and deep neural networks [C ] // Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition . New York : ACM Press , 2022 : 1045 - 1051 .
李峻辰 , 程光 , 杨刚芹 . 基于网络流量的私有协议逆向技术综述 [J ] . 计算机研究与发展 , 2023 , 60 ( 1 ): 167 - 190 .
LI J C , CHENG G , YANG G Q . Private protocol reverse engineering based on network traffic: a survey [J ] . Journal of Computer Research and Development , 2023 , 60 ( 1 ): 167 - 190 .
WANG X W , LV K Z , LI B . IPART: an automatic protocol reverse engineering tool based on global voting expert for industrial protocols [J ] . International Journal of Parallel, Emergent and Distributed Systems , 2020 , 35 ( 3 ): 376 - 395 .
SHIM K S , GOO Y H , LEE M S , et al . Clustering method in protocol reverse engineering for industrial protocols [J ] . International Journal of Network Management , 2020 , 30 ( 6 ): e2126 .
LIU O Y , ZHENG B , SUN W , et al . A data-driven approach for reverse engineering electric power protocols [J ] . Journal of Signal Processing Systems , 2021 , 93 ( 7 ): 769 - 777 .
WANG R , SHI Y J , DING J K . Reverse engineering of industrial control protocol by XGBoost with V-gram [C ] // Proceedings of the 2020 IEEE 6th International Conference on Computer and Communications (ICCC) . Piscataway : IEEE Press , 2020 : 172 - 176 .
YU C H , ZHANG Z , GAO M . An ICS traffic classification based on industrial control protocol keyword feature extraction algorithm [J ] . Applied Sciences , 2022 , 12 ( 21 ): 11193 .
ZHAO R , LIU Z H . Analysis of private industrial control protocol format based on LSTM-FCN model [C ] // Proceedings of the 2020 International Conference on Aviation Safety and Information Technology . New York : ACM Press , 2020 : 330 - 335 .
ZHAI L , ZHENG Q H , ZHANG X , et al . Identification of private ICS protocols based on raw traffic [J ] . Symmetry , 2021 , 13 ( 9 ): 1743 .
NING B W , ZONG X J , HE K , et al . PREIUD: an industrial control protocols reverse engineering tool based on unsupervised learning and deep neural network methods [J ] . Symmetry , 2023 , 15 ( 3 ): 706 .
YE Y P , ZHANG Z , WANG F , et al . NetPlier: probabilistic network protocol reverse engineering from message traces [C ] // Proceedings of the 2021 Network and Distributed System Security Symposium . Reston : Internet Society , 2021 : 1 - 18 .
CAI J , ZHONG W J , LUO J Z . SeMiner: side-information-based semantics miner for proprietary industrial control protocols [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 22 ): 22796 - 22810 .
PENG Y , XIANG C , GAO H H , et al . Industrial control system fingerprinting and anomaly detection [C ] // IFIP Advances in Information and Communication Technology . Berlin : Springer , 2015 : 73 - 85 .
FORMBY D , SRINIVASAN P , LEONARD A , et al . Who’s in control of your control system? device fingerprinting for cyber-physical systems [C ] // Proceedings of the 2016 Network and Distributed System Security Symposium . Reston : Internet Society , 2016 : 1 - 15 .
KELIRIS A , MANIATAKOS M . Remote field device fingerprinting using device-specific modbus information [C ] // Proceedings of the 2016 IEEE 59th International Midwest Symposium on Circuits and Systems (MWSCAS) . Piscataway : IEEE Press , 2016 : 1 - 4 .
NIEDERMAIER M , HANKA T , PLAGA S , et al . Efficient passive ICS device discovery and identification by MAC address correlation [J ] . arXiv Preprint , arXiv: 1904.04271 , 2019 .
CHAKRABORTY I , KELLEY B M , GALLAGHER B . Industrial control system device classification using network traffic features and neural network embeddings [J ] . Array , 2021 , 12 : 100081 .
李长连 , 余思阳 , 程驰 . 基于设备流量行为的工业物联网指纹识别技术 [J ] . 工业信息安全 , 2022 ( 10 ): 47 - 56 .
LI C L , YU S Y , CHENG C . Industrial Internet of things fingerprint identification technology based on traffic behavior drift [J ] . Industry Information Security , 2022 ( 10 ): 47 - 56 .
TAO J , YUAN X , ZHANG S Z , et al . Development of fingerprint identification based on device flow in industrial control system [J ] . Applied Sciences , 2023 , 13 ( 2 ): 731 .
卿斯汉 , 蒋建春 , 马恒太 , 等 . 入侵检测技术研究综述 [J ] . 通信学报 , 2004 , 25 ( 7 ): 19 - 29 .
QING S H , JIANG J C , MA H T , et al . Research on intrusion detection techniques: a survey [J ] . Journal on Communications , 2004 , 25 ( 7 ): 19 - 29 .
ANDERSON J P . Computer security threat monitoring and surveillance [R ] . 1980 .
HADŽIOSMANOVIĆ D , SOMMER R , ZAMBON E , et al . Through the eye of the PLC: semantic security monitoring for industrial processes [C ] // Proceedings of the 30th Annual Computer Security Applications Conference . New York : ACM Press , 2014 : 126 - 135 .
CASELLI M , ZAMBON E , KARGL F . Sequence-aware intrusion detection in industrial control systems [C ] // Proceedings of the 1st ACM Workshop on Cyber-Physical System Security . New York : ACM Press , 2015 : 13 - 24 .
HAVLENA V , MATOUŠEK P , RYŠAVÝ O , et al . Accurate automata-based detection of cyber threats in smart grid communication [J ] . IEEE Transactions on Smart Grid , 2023 , 14 ( 3 ): 2352 - 2366 .
ZHOU C J , HUANG S , XIONG N X , et al . Design and analysis of multimodel-based anomaly intrusion detection systems in industrial process automation [J ] . IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2015 , 45 ( 10 ): 1345 - 1360 .
ZHANG F , KODITUWAKKU H A D E , HINES J W , et al . Multilayer data-driven cyber-attack detection system for industrial control systems based on network, system, and process data [J ] . IEEE Transactions on Industrial Informatics , 2019 , 15 ( 7 ): 4362 - 4369 .
LIU J J , LIN X D , CHEN X , et al . ShadowPLCs: a novel scheme for remote detection of industrial process control attacks [J ] . IEEE Transactions on Dependable and Secure Computing , 2022 , 19 ( 3 ): 2054 - 2069 .
PONOMAREV S , ATKISON T . Industrial control system network intrusion detection by telemetry analysis [J ] . IEEE Transactions on Dependable and Secure Computing , 2016 , 13 ( 2 ): 252 - 260 .
TERAI A , ABE S , KOJIMA S , et al . Cyber-attack detection for industrial control system monitoring with support vector machine based on communication profile [C ] // Proceedings of the 2017 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) . Piscataway : IEEE Press , 2017 : 132 - 138 .
SHEN C , LIU C , TAN H L , et al . Hybrid-augmented device fingerprinting for intrusion detection in industrial control system networks [J ] . IEEE Wireless Communications , 2018 , 25 ( 6 ): 26 - 31 .
YUN J H , HWANG Y , LEE W , et al . Statistical similarity of critical infrastructure network traffic based on nearest neighbor distances [C ] // International Symposium on Research in Attacks, Intrusions, and Defenses . Berlin : Springer , 2018 : 577 - 599 .
HASSAN M M , GUMAEI A , HUDA S , et al . Increasing the trustworthiness in the industrial IoT networks through a reliable cyberattack detection model [J ] . IEEE Transactions on Industrial Informatics , 2020 , 16 ( 9 ): 6154 - 6162 .
CHEN T S , LIN P , LING J . An intrusion detection method for industrial control system based on gate recurrent unit [J ] . Journal of Physics: Conference Series , 2019 , 1302 ( 2 ): 022016 .
YANG K , LI Q , LIN X D , et al . iFinger: intrusion detection in industrial control systems via register-based fingerprinting [J ] . IEEE Journal on Selected Areas in Communications , 2020 , 38 ( 5 ): 955 - 967 .
WANG J , LI P , KONG W , et al . Unknown security attack detection of industrial control system by deep learning [J ] . Mathematics , 2022 , 10 ( 16 ): 2872 .
WANG W B , ZHANG B , YU Z C , et al . Anomaly detection method of unknown protocol in power industrial control system based on RNN [C ] // Proceedings of the 2022 5th International Conference on Renewable Energy and Power Engineering (REPE) . Piscataway : IEEE Press , 2022 : 68 - 72 .
ZHANG S J , LAI J , YAO Q G . Traffic anomaly detection model of electric power industrial control based on DBN-LSTM [C ] // Proceedings of the 2021 IEEE 23rd Int Conf on High Performance Computing & Communications ; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys) . Piscataway : IEEE Press , 2021 : 1902 - 1907 .
DENG H W , ZHAO Y Q , LI X W , et al . Abnormal flow monitoring of industrial control network based on neural network [C ] // Proceedings of the 2022 IEEE 8th International Conference on Computer and Communications (ICCC) . Piscataway : IEEE Press , 2022 : 627 - 631 .
ZHOU L Y , GUO H Q , DENG G L . A fog computing based approach to DDoS mitigation in IIoT systems [J ] . Computers & Security , 2019 , 85 : 51 - 62 .
ABDEL-BASSET M , CHANG V , HAWASH H , et al . Deep-IFS: intrusion detection approach for industrial Internet of things traffic in fog environment [J ] . IEEE Transactions on Industrial Informatics , 2021 , 17 ( 11 ): 7704 - 7715 .
LI Y J , JI X Y , LI C G , et al . Cross-domain anomaly detection for power industrial control system [C ] // Proceedings of the 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC) . Piscataway : IEEE Press , 2020 : 383 - 386 .
GU H R , LAI Y X , WANG Y P , et al . DEIDS: a novel intrusion detection system for industrial control systems [J ] . Neural Computing and Applications , 2022 , 34 ( 12 ): 9793 - 9811 .
JIANG J R , CHEN Y T . Industrial control system anomaly detection and classification based on network traffic [J ] . IEEE Access , 2022 , 10 : 41874 - 41888 .
CHEN Y L , SU S D , YU D , et al . Cross-domain industrial intrusion detection deep model trained with imbalanced data [J ] . IEEE Internet of Things Journal , 2023 , 10 ( 1 ): 584 - 596 .
GÓMEZ Á L P , MAIMÓ L F , CELDRÁN A H , et al . Crafting adversarial samples for anomaly detectors in industrial control systems [J ] . Procedia Computer Science , 2021 , 184 : 573 - 580 .
GAO S , CHEN J J , ZHANG B S , et al . Privacy-preserving industrial control system anomaly detection platform [J ] . Security and Communication Networks , 2023 ( 1 ): 7010155 .
LIN K D , XU X L , GAO H H . TSCRNN: a novel classification scheme of encrypted traffic based on flow spatiotemporal features for efficient management of IIoT [J ] . Computer Networks , 2021 , 190 : 107974 .
LUKSHA I , DUY DINH T , KARELIN E , et al . Method for filtering encrypted traffic using a neural network between an industrial Internet of things system and digital twin [C ] // Proceedings of the 5th International Conference on Future Networks & Distributed Systems . New York : ACM Press , 2021 : 595 - 601 .
NIU Z Q , XUE J F , QU D C , et al . A novel approach based on adaptive online analysis of encrypted traffic for identifying Malware in IIoT [J ] . Information Sciences , 2022 , 601 : 162 - 174 .
DAI J B , XU X L , GAO H H , et al . CMFTC: cross modality fusion efficient multitask encrypt traffic classification in IIoT environment [J ] . IEEE Transactions on Network Science and Engineering , 2023 , 10 ( 6 ): 3989 - 4009 .
ZHAO R J , HUANG Y T , DENG X W , et al . A novel traffic classifier with attention mechanism for industrial Internet of things [J ] . IEEE Transactions on Industrial Informatics , 2023 , 19 ( 11 ): 10799 - 10810 .
ZHU Z S , SHI J Z , WANG C H , et al . MCFM: discover sensitive behavior from encrypted traffic in industrial control system [C ] // Proceedings of the 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) . Piscataway : IEEE Press , 2022 : 897 - 904 .
NIEDERMAIER M , FISCHER F , BODISCO A V . PropFuzz—an IT-security fuzzing framework for proprietary ICS protocols [C ] // Proceedings of the 2017 International Conference on Applied Electronics (AE) . Piscataway : IEEE Press , 2017 : 1 - 4 .
FANG D L , SONG Z W , GUAN L , et al . ICS3Fuzzer: a framework for discovering protocol implementation bugs in ICS supervisory software by fuzzing [C ] // Annual Computer Security Applications Conference . New York : ACM Press , 2021 : 849 - 860 .
RAMOS S H , VILLALBA M T , LACUESTA R . MQTT security: a novel fuzzing approach [J ] . Wireless Communications and Mobile Computing , 2018 , 2018 : 1 - 11 .
LAI Y X , GAO H J , LIU J . Vulnerability mining method for the modbus TCP using an anti-sample fuzzer [J ] . Sensors , 2020 , 20 ( 7 ): 2040 .
HU Z C , SHI J Q , HUANG Y H , et al . GANFuzz: a GAN-based industrial network protocol fuzzing framework [C ] // Proceedings of the 15th ACM International Conference on Computing Frontiers . New York : ACM Press , 2018 : 138 - 145 .
ZHAO H , LI Z H , WEI H S , et al . SeqFuzzer: an industrial protocol fuzzing framework from a deep learning perspective [C ] // Proceedings of the 2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST) . Piscataway : IEEE Press , 2019 : 59 - 67 .
LV W Y , XIONG J W , SHI J Q , et al . A deep convolution generative adversarial networks based fuzzing framework for industry control protocols [J ] . Journal of Intelligent Manufacturing , 2021 , 32 ( 2 ): 441 - 457 .
YU Z H , WANG H L , WANG D , et al . CGFuzzer: a fuzzing approach based on coverage-guided generative adversarial networks for industrial IoT protocols [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 21 ): 21607 - 21619 .
BYTES A , RAJPUT P H N , DOUMANIDIS C , et al . FieldFuzz: in situ blackbox fuzzing of proprietary industrial automation runtimes via the network [C ] // Proceedings of the 26th International Symposium on Research in Attacks, Intrusions and Defenses . New York : ACM Press , 2023 : 499 - 512 .
CHE X , GENG Y Y , ZHANG G , et al . Fuzzing technology based on information theory for industrial proprietary protocol [J ] . Electronics , 2023 , 12 ( 14 ): 3041 .
LUO Z , YU J , ZUO F , et al . Bleem: packet sequence oriented guzzing for protocol implementations [C ] // 32nd USENIX Security Symposium (USENIX Security 23) . Berkeley : USENIX Association , 2023 : 4481 - 4498 .
化盈盈 , 张岱墀 , 葛仕明 . 深度学习模型可解释性的研究进展 [J ] . 信息安全学报 , 2020 , 5 ( 3 ): 1 - 12 .
HUA Y Y , ZHANG D C , GE S M . Research progress in the interpretability of deep learning models [J ] . Journal of Cyber Security , 2020 , 5 ( 3 ): 1 - 12 .
刘奇旭 , 王君楠 , 尹捷 , 等 . 对抗机器学习在网络入侵检测领域的应用 [J ] . 通信学报 , 2021 , 42 ( 11 ): 1 - 12 .
LIU Q X , WANG J N , YIN J , et al . Application of adversarial machine learning in network intrusion detection [J ] . Journal on Communications , 2021 , 42 ( 11 ): 1 - 12 .
杨安 , 孙利民 , 王小山 , 等 . 工业控制系统入侵检测技术综述 [J ] . 计算机研究与发展 , 2016 , 53 ( 9 ): 2039 - 2054 .
YANG A , SUN L M , WANG X S , et al . Intrusion detection techniques for industrial control systems [J ] . Journal of Computer Research and Development , 2016 , 53 ( 9 ): 2039 - 2054 .
YAO Y F , DUAN J H , XU K D , et al . A survey on large language model (LLM) security and privacy: the good, the bad, and the ugly [J ] . High-Confidence Computing , 2024 , 4 ( 2 ): 100211 .
0
浏览量
1205
下载量
0
CSCD
- 网络PDF下载
- WORD下载
- XML下载
- Meta-XML下载
- BibTeX下载
- Endnote下载
- RefMan 下载
- NoteExpress下载
- NoteFirst下载
关联资源
相关文章
相关作者
相关机构
AI问答- 技术支持由北京北大方正电子有限公司提供 京ICP备09064830号-19
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies
- 总访问量：583912 今日访问量：1728