Artificial intelligence algorithms for cyberspace security applications: a technological and status review

Jie CHEN; Dandan WU; Ruiyun XIE

doi:10.1631/FITEE.2200314

Your Location：

Home >

Browse articles >

Artificial intelligence algorithms for cyberspace security applications: a technological and status review

Regular Papers | Updated：2023-08-29

- Artificial intelligence algorithms for cyberspace security applications: a technological and status review
- 人工智能算法在网络空间安全中的应用：技术与现状综述
- Frontiers of Information Technology & Electronic Engineering Vol. 24, Issue 8, Pages: 1117-1142(2023)
- Affiliations：
  
  1.School of Cybersecurity, Northwestern Polytechnical University, Xi'an 710000, China
  2.China Electronics Technology Network Information Security Co., Ltd., Chengdu 610000, China
- Author bio：
  
  ‡Corresponding author
  wudd@cetcsc.com
- Funds：
- DOI：10.1631/FITEE.2200314
  CLC： TP393.08
- Received：21 July 2022，
  
  Revised：2022-12-11，
  
  Published：0 August 2023
- Accepted：
Scan QR Code
Jie CHEN, Dandan WU, Ruiyun XIE. Artificial intelligence algorithms for cyberspace security applications: a technological and status review[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(8): 1117-1142.
DOI：

Jie CHEN, Dandan WU, Ruiyun XIE. Artificial intelligence algorithms for cyberspace security applications: a technological and status review[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(8): 1117-1142. DOI： 10.1631/FITEE.2200314.

摘要

网络空间安全急需解决的3个技术问题是：网络攻击检测的及时性和准确性、安全态势的可信评估和预测以及安全防御策略优化的有效性。人工智能算法已成为网络安全应用增加安全机会和提高对抗能力的核心手段。近年来，人工智能技术的突破和应用为提高网络防御能力提供了先进的技术支持。本综述对2017至2022年间人工智能技术在网络空间安全领域的最新应用进行了全面回顾。参考文献来源于各种期刊和会议，其中52.68%的论文来自Elsevier、Springer和IEEE期刊，25%来自国际学术会议。本综述重点介绍了机器学习、深度学习和一些流行的优化算法在该领域的最新应用进展，对算法模型的特点、性能结果、数据集、以及潜在的优点和局限性进行了分析，强调了现存的挑战。本工作旨在为想进一步挖掘人工智能技术在网络空间安全领域应用的潜力、解决特定网络空间安全问题的研究人员提供技术指导，掌握当前技术和应用的发展趋势以及网络安全领域的热点问题。同时，本综述对当前面临的挑战提供了有效应对策略和方向。

Abstract

Three technical problems should be solved urgently in cyberspace security: the timeliness and accuracy of network attack detection

the credibility assessment and prediction of the security situation

and the effectiveness of security defense strategy optimization. Artificial intelligence (AI) algorithms have become the core means to increase the chance of security and improve the network attack and defense ability in the application of cyberspace security. Recently

the breakthrough and application of AI technology have provided a series of advanced approaches for further enhancing network defense ability. This work presents a comprehensive review of AI technology articles for cyberspace security applications

mainly from 2017 to 2022. The papers are selected from a variety of journals and conferences: 52.68% are from Elsevier

Springer

and IEEE journals and 25% are from international conferences. With a specific focus on the latest approaches in machine learning (ML)

deep learning (DL)

and some popular optimization algorithms

the characteristics of the algorithmic models

performance results

datasets

potential benefits

and limitations are analyzed

and some of the existing challenges are highlighted. This work is intended to provide technical guidance for researchers who would like to obtain the potential of AI technical methods for cyberspace security and to provide tips for the later resolution of specific cyberspace security issues

and a mastery of the current development trends of technology and application and hot issues in the field of network security. It also indicates certain existing challenges and gives directions for addressing them effectively.

关键词

Keywords

references

Aggarwal P , Thakoor O , Jabbari S , et al. , 2022 . Designing effective masking strategies for cyberdefense through human experimentation and cognitive models . Comput Secur , 117 : 102671 . https://doi.org/10.1016/j.cose.2022.102671 https://doi.org/10.1016/j.cose.2022.102671

Al-Garadi MA , Mohamed A , Al-Ali AK , et al. , 2020 . A survey of machine and deep learning methods for Internet of Things (IoT) security . IEEE Commun Surv Tut , 22 ( 3 ):‍ 1646 - 1685 . https://doi.org/10.1109/COMST.2020.2988293 https://doi.org/10.1109/COMST.2020.2988293

Al-Omari M , Rawashdeh M , Qutaishat F , et al. , 2021 . An intelligent tree-based intrusion detection model for cyber security . J Netw Syst Manag , 29 ( 2 ): 20 . https://doi.org/10.1007/s10922-021-09591-y https://doi.org/10.1007/s10922-021-09591-y

Al-Yaseen WL , Othman ZA , Nazri MZA , 2017 . Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system . Expert Syst Appl , 67 : 296 - 303 . https://doi.org/10.1016/j.eswa.2016.09.041 https://doi.org/10.1016/j.eswa.2016.09.041

Andresini G , Appice A , di Mauro N , et al. , 2020 . Multi-channel deep feature learning for intrusion detection . IEEE Access , 8 : 53346 - 53359 . https://doi.org/10.1109/ACCESS.2020.2980937 https://doi.org/10.1109/ACCESS.2020.2980937

Apruzzese G , Colajanni M , Ferretti L , et al. , 2018 . On the effectiveness of machine and deep learning for cyber security . Proc 10 th Int Conf on Cyber Conflict , p. 371 - 390 . https://doi.org/10.23919/CYCON.2018.8405026 https://doi.org/10.23919/CYCON.2018.8405026

Arshad SA , Murtaza MA , Tahir M , 2012 . Fair buffer allocation scheme for integrated wireless sensor and vehicular networks using Markov decision processes . IEEE Vehicular Technology Conf , p. 1 - 5 . https://doi.org/10.1109/VTCFall.2012.6399151 https://doi.org/10.1109/VTCFall.2012.6399151

Atefi K , Hashim H , Kassim M , 2019 . Anomaly analysis for the classification purpose of intrusion detection system with K-nearest neighbors and deep neural network . IEEE 7 th Conf on Systems, Process and Control , p. 269 - 274 . https://doi.org/10.1109/ICSPC47137.2019.9068081 https://doi.org/10.1109/ICSPC47137.2019.9068081

Aung YY , Min MM , 2018 . Hybrid intrusion detection system using K-means and K-nearest neighbors algorithms . Proc IEEE/ACIS 17 th Int Conf on Computer and Information Science , p. 34 - 38 . https://doi.org/10.1109/ICIS.2018.8466537 https://doi.org/10.1109/ICIS.2018.8466537

Bahnsen AC , Torroledo I , Camacho LD , et al. , 2018 . Simulating malicious AI . Proc Symp on Electronic Crime Research , p. 15 - 17 .

Balamurugan E , Mehbodniya A , Kariri E , et al. , 2022 . Network optimization using defender system in cloud computing security based intrusion detection system with game theory deep neural network (IDSGT-DNN) . Patt Recogn Lett , 156 : 142 - 151 . https://doi.org/10.1016/j.patrec.2022.02.013 https://doi.org/10.1016/j.patrec.2022.02.013

Bdrany A , Sadkhan SB , 2020 . Decision making approaches in cognitive radio—status, challenges and future trends . Int Conf on Advanced Science and Engineering , p. 195 - 198 . https://doi.org/10.1109/ICOASE51841.2020.9436597 https://doi.org/10.1109/ICOASE51841.2020.9436597

Berman DS , Buczak NL , Chavis JS , et al. , 2019 . A survey of deep learning methods for cyber security . Information , 10 ( 4 ): 122 . https://doi.org/10.3390/INFO10040122 https://doi.org/10.3390/INFO10040122

Bhuiyan TH , Medal HR , Nandi AK , et al. , 2021 . Risk-averse bi-level stochastic network interdiction model for cyber-security risk management . Int J Crit Infrastr Prot , 32 : 100408 . https://doi.org/10.1016/j.ijcip.2021.100408 https://doi.org/10.1016/j.ijcip.2021.100408

Bitaab M , Hashemi S , 2017 . Hybrid intrusion detection: combining decision tree and Gaussian mixture model . Proc 14 th Int ISC (Iranian Society of Cryptology) Conf on Information Security and Cryptology , p. 8 - 12 . https://doi.org/10.1109/ISCISC.2017.8488375 https://doi.org/10.1109/ISCISC.2017.8488375

Bouhamed O , Bouachir O , Aloqaily M , et al. , 2021 . Lightweight IDS for UAV networks: a periodic deep reinforcement learning-based approach . IFIP/IEEE Int Symp on Integrated Network Management , p. 1032 - 1037 .

Bresniker K , Gavrilovska A , Holt J , et al. , 2019 . Grand challenge: applying artificial intelligence and machine learning to cybersecurity . Computer , 52 ( 12 ): 45 - 52 . https://doi.org/10.1109/MC.2019.2942584 https://doi.org/10.1109/MC.2019.2942584

Buczak AL , Guven E , 2016 . A survey of data mining and machine learning methods for cyber security intrusion detection . IEEE Commun Surv Tut , 18 ( 2 ): 1153 - 1176 . https://doi.org/10.1109/COMST.2015.2494502 https://doi.org/10.1109/COMST.2015.2494502

Burke D , 1999 . Toward a Game Theory Model of Information Warfare . Technical Report, AFIT/GSS/LAL/99D-1 . Airforce Institute of Technology , USA .

Buşoniu L , Babuška R , de Schutter B , 2010 . Multi-agent reinforcement learning: an overview . In: Srinivasan D , Jain LC (Eds.) , Innovations in Multi-agent Systems and Applications . Springer , Heidelberg , p. 183 - 221 . https://doi.org/10.1007/978-3-642-14435-6_7 https://doi.org/10.1007/978-3-642-14435-6_7

Cao G , Lu ZM , Wen XM , et al. , 2018 . AIF: an artificial intelligence framework for smart wireless network management . IEEE Commun Lett , 22 ( 2 ): 400 - 403 . https://doi.org/10.1109/LCOMM.2017.2776917 https://doi.org/10.1109/LCOMM.2017.2776917

Challita U , Dong L , Saad W , 2018 . Proactive resource management for LTE in unlicensed spectrum: a deep learning perspective . IEEE Trans Wirel Commun , 17 ( 7 ):‍ 4674 - 4689 . https://doi.org/10.1109/TWC.2018.2829773 https://doi.org/10.1109/TWC.2018.2829773

Chen F , Ye ZW , Wang CZ , et al. , 2018 . A feature selection approach for network intrusion detection based on tree-seed algorithm and K-nearest neighbor . IEEE 4 th Int Symp on Wireless Systems within the Int Conf on Intelligent Data Acquisition and Advanced Computing Systems , p. 68 - 72 . https://doi.org/10.1109/IDAACS-SWS.2018.8525522 https://doi.org/10.1109/IDAACS-SWS.2018.8525522

Chen SS , Lian YF , Jia W , 2008 . A network vulnerability evaluation method based on Bayesian networks . J Univ Chin Acad Sci , 25 ( 5 ): 639 - 648 (in Chinese) . https://doi.org/10.7523/j.issn.2095-6134.2008.5.011 https://doi.org/10.7523/j.issn.2095-6134.2008.5.011

Chen Y , Lin QZ , Wei WH , et al. , 2022 . Intrusion detection using multi-objective evolutionary convolutional neural network for Internet of Things in fog computing . Knowl-Based Syst , 244 : 108505 . https://doi.org/10.1016/j.knosys.2022.108505 https://doi.org/10.1016/j.knosys.2022.108505

Chohra A , Shirani P , Karbab EB , et al. , 2022 . Chameleon: optimized feature selection using particle swarm optimization and ensemble methods for network anomaly detection . Comput Secur , 117 : 102684 . https://doi.org/10.1016/j.cose.2022.102684 https://doi.org/10.1016/j.cose.2022.102684

Choi YH , Liu P , Shang ZT , et al. , 2020 . Using deep learning to solve computer security challenges: a survey . Cybersecurity , 3 ( 1 ): 15 . https://doi.org/10.1186/s42400-020-00055-5 https://doi.org/10.1186/s42400-020-00055-5

Deng SG , Xiang ZZ , Zhao P , et al. , 2020 . Dynamical resource allocation in edge for trustable Internet-of-Things systems: a reinforcement learning method . IEEE Trans Ind Inform , 16 ( 9 ): 6103 - 6113 . https://doi.org/10.1109/TII.2020.2974875 https://doi.org/10.1109/TII.2020.2974875

Diao WP , 2021 . Network security situation forecast model based on neural network algorithm development and verification . IEEE 4 th Int Conf on Automation, Electronics and Electrical Engineering , p. 462 - 465 . https://doi.org/10.1109/AUTEEE52864.2021.9668668 https://doi.org/10.1109/AUTEEE52864.2021.9668668

Ding HW , Chen LY , Dong L , et al. , 2022 . Imbalanced data classification: a KNN and generative adversarial networks-based hybrid approach for intrusion detection . Fut Gener Comput Syst , 131 : 240 - 254 . https://doi.org/10.1016/j.future.2022.01.026 https://doi.org/10.1016/j.future.2022.01.026

Elbes M , Alzubi S , Kanan T , et al. , 2019 . A survey on particle swarm optimization with emphasis on engineering and network applications . Evol Intell , 12 ( 2 ): 113 - 129 . https://doi.org/10.1007/S12065-019-00210-Z https://doi.org/10.1007/S12065-019-00210-Z

Faker O , Dogdu E , 2019 . Intrusion detection using big data and deep learning techniques . Proc ACM Southeast Conf , p. 86 - 93 . https://doi.org/10.1145/3299815.3314439 https://doi.org/10.1145/3299815.3314439

Garcia AB , Babiceanu RF , Seker R , 2021 . Artificial intelligence and machine learning approaches for aviation cybersecurity: an overview . Integrated Communications Navigation and Surveillance Conf , p. 1 - 8 . https://doi.org/10.1109/ICNS52807.2021.9441594 https://doi.org/10.1109/ICNS52807.2021.9441594

Gharib A , Sharafaldin I , Lashkari AH , et al. , 2016 . An evaluation framework for intrusion detection dataset . Proc Int Conf on Information Science and Security , p. 1 - 6 . https://doi.org/10.1109/ICISSEC.2016.7885840 https://doi.org/10.1109/ICISSEC.2016.7885840

Goodfellow IJ , Pouget-Abadie J , Mirza M , et al. , 2014 . Generative adversarial nets . Proc 27 th Int Conf on Neural Information Processing Systems , p. 2672 - 2680 .

Goodfellow IJ , Bengio Y , Courville A , 2016 . Deep Learning . MIT Press , Cambridge, USA .

Graves A , Mohamed AR , Hinton G , 2013 . Speech recognition with deep recurrent neural networks . Proc IEEE Int Conf on Acoustics, Speech and Signal Processing , p. 6645 - 6649 . https://doi.org/10.1109/ICASSP.2013.6638947 https://doi.org/10.1109/ICASSP.2013.6638947

Gronauer S , Diepold K , 2022 . Multi‑agent deep reinforcement learning: a survey . Artif Intell Rev , 55 : 895 - 943 . https://doi.org/10.1007/s10462-021-09996-w https://doi.org/10.1007/s10462-021-09996-w

Gu YH , Li KY , Guo ZY , et al. , 2019 . Semi-supervised K-means DDoS detection method using hybrid feature selection algorithm . IEEE Access , 7 : 64351 - 64365 . https://doi.org/10.1109/ACCESS.2019.2917532 https://doi.org/10.1109/ACCESS.2019.2917532

Gupta ARB , Agrawal J , 2020 . A comprehensive survey on various machine learning methods used for intrusion detection system . IEEE 9 th Int Conf on Communication Systems and Network Technologies , p. 282 - 289 . https://doi.org/10.1109/CSNT48778.2020.9115764 https://doi.org/10.1109/CSNT48778.2020.9115764

Gupta N , Jindal V , Bedi P , 2022 . CSE-IDS: using cost-sensitive deep learning and ensemble algorithms to handle class imbalance in network-based intrusion detection systems . Comput Secur , 112 : 102499 . https://doi.org/10.1016/j.cose.2021.102499 https://doi.org/10.1016/j.cose.2021.102499

Hamrioui S , Bokhari S , 2021 . A new cybersecurity strategy for IoE by exploiting an optimization approach . 12 th Int Conf on Information and Communication Systems , p. 23 - 28 . https://doi.org/10.1109/ICICS52457.2021.9464595 https://doi.org/10.1109/ICICS52457.2021.9464595

He XM , Wang K , Huang HW , et al. , 2020 . Green resource allocation based on deep reinforcement learning in content-centric IoT . IEEE Trans Emerg Top Comput , 8 ( 3 ): 781 - 796 . https://doi.org/10.1109/TETC.2018.2805718 https://doi.org/10.1109/TETC.2018.2805718

Hessel M , Modayil J , van Hasselt H , et al. , 2018 . Rainbow: combining improvements in deep reinforcement learning . Proc AAAI Conf on Artificial Intelligence , p. 3215 - 3222 . https://doi.org/10.1609/aaai.v32i1.11796 https://doi.org/10.1609/aaai.v32i1.11796

Hindy H , Atkinson R , Tachtatzis C , et al. , 2020 . Utilising deep learning techniques for effective zero-day attack detection . Electronics , 9 ( 10 ): 1684 . https://doi.org/10.3390/electronics9101684 https://doi.org/10.3390/electronics9101684

Ho S , Al Jufout S , Dajani K , et al. , 2021 . A novel intrusion detection model for detecting known and innovative cyberattacks using convolutional neural network . IEEE Open J Comput Soc , 2 : 14 - 25 . https://doi.org/10.1109/OJCS.2021.3050917 https://doi.org/10.1109/OJCS.2021.3050917

Hossain D , Ochiai H , Doudou F , et al. , 2020 . SSH and FTP brute-force attacks detection in computer networks: LSTM and machine learning approaches . 5 th Int Conf on Computer and Communication Systems , p. 491 - 497 . https://doi.org/10.1109/ICCCS49078.2020.9118459 https://doi.org/10.1109/ICCCS49078.2020.9118459

Hu BW , Zhou CJ , Tian YC , et al. , 2021 . Decentralized consensus decision-making for cybersecurity protection in multimicrogrid systems . IEEE Trans Syst Man Cybern Syst , 51 ( 4 ): 2187 - 2198 . https://doi.org/10.1109/TSMC.2020.3019272 https://doi.org/10.1109/TSMC.2020.3019272

Hu CH , Liu GK , Li M , 2021 . A network security situation prediction method based on SA-SSA . 14 th Int Symp on Computational Intelligence and Design , p. 105 - 110 . https://doi.org/10.1109/ISCID52796.2021.00033 https://doi.org/10.1109/ISCID52796.2021.00033

Hühn J , Hüllermeier E , 2009 . FURIA: an algorithm for unordered fuzzy rule induction . Data Min Knowl Discov , 19 ( 3 ): 293 - 319 . https://doi.org/10.1007/s10618-009-0131-8 https://doi.org/10.1007/s10618-009-0131-8

Huo D , Li XY , Li LH , et al. , 2022 . The application of 1D-CNN in microsoft malware detection . 7 th Int Conf on Big Data Analytics , p. 181 - 187 . https://doi.org/10.1109/ICBDA55095.2022.9760349 https://doi.org/10.1109/ICBDA55095.2022.9760349

Hyder B , Govindarasu M , 2020 . Optimization of cybersecurity investment strategies in the smart grid using game-theory . IEEE Power & Energy Society Innovative Smart Grid Technologies Conf , p. 1 - 5 . https://doi.org/10.1109/ISGT45199.2020.9087634 https://doi.org/10.1109/ISGT45199.2020.9087634

Issa ASA , Albayrak Z , 2021 . CLSTMNet: a deep learning model for intrusion detection . 3 rd Int Scientific Conf of Engineering Sciences and Advances Technologies , Article 012244 . https://doi.org/10.1088/1742-6596/1973/1/012244 https://doi.org/10.1088/1742-6596/1973/1/012244

Jain M , Kaur G , 2019 . A novel distributed semi-supervised approach for detection of network based attacks . 9 th Int Conf on Cloud Computing, Data Science & Engineering , p. 120 - 125 . https://doi.org/10.1109/CONFLUENCE.2019.8776616 https://doi.org/10.1109/CONFLUENCE.2019.8776616

Kan X , Fan YX , Fang ZJ , et al. , 2021 . A novel IoT network intrusion detection approach based on adaptive particle swarm optimization convolutional neural network . Inform Sci , 568 : 147 - 162 . https://doi.org/10.1016/J.INS.2021.03.060 https://doi.org/10.1016/J.INS.2021.03.060

Khaw YM , Jahromi AA , Arani MFM , et al. , 2021 . A deep learning-based cyberattack detection system for transmission protective relays . IEEE Trans Smart Grid , 12 ( 3 ): 2554 - 2565 . https://doi.org/10.1109/TSG.2020.3040361 https://doi.org/10.1109/TSG.2020.3040361

Kherlenchimeg Z , Nakaya N , 2018 . Network intrusion classifier using autoencoder with recurrent neural network . Proc 4 th Int Conf on Electronics and Software Science , p. 94 - 100 .

Khoa TV , Saputra YM , Hoang DT , et al. , 2020 . Collaborative learning model for cyberattack detection systems in IoT Industry 4.0 . IEEE Wireless Communications and Networking Conf , p. 1 - 6 . https://doi.org/10.1109/WCNC45663.2020.9120761 https://doi.org/10.1109/WCNC45663.2020.9120761

Kim J , Shin Y , Choi E , 2019 . An intrusion detection model based on a convolutional neural network . J Multim Inform Syst , 6 ( 4 ): 165 - 172 . https://doi.org/10.33851/jmis.2019.6.4.165 https://doi.org/10.33851/jmis.2019.6.4.165

Krizhevsky A , Sutskever I , Hinton GE , 2012 . ImageNet classification with deep convolutional neural networks . Proc 25 th Int Conf on Neural Information Processing Systems , p. 1097 - 1105 . http://doi.org/10.1145/3065386 http://doi.org/10.1145/3065386

Kumar N , Zeadally S , Chilamkurti N , et al. , 2015 . Performance analysis of Bayesian coalition game-based energy-aware virtual machine migration in vehicular mobile cloud . IEEE Netw , 29 ( 2 ): 62 - 69 . https://doi.org/10.1109/MNET.2015.7064905 https://doi.org/10.1109/MNET.2015.7064905

Kumar VS , Narasimhan VL , 2021 . Using deep learning for assessing cybersecurity economic risks in virtual power plants . 7 th Int Conf on Electrical Energy Systems , p. 530 - 537 . https://doi.org/10.1109/ICEES51510.2021.9383723 https://doi.org/10.1109/ICEES51510.2021.9383723

Kunal , Dua M , 2019 . Machine learning approach to IDS: a comprehensive review . 3 rd Int Conf on Electronics, Communication and Aerospace Technology , p. 117 - 121 . https://doi.org/10.1109/ICECA.2019.8822120 https://doi.org/10.1109/ICECA.2019.8822120

Kunang YN , Nurmaini S , Stiawan D , et al. , 2019 . Automatic features extraction using autoencoder in intrusion detection system . Proc Int Conf on Electrical Engineering and Computer Science , p. 219 - 224 . https://doi.org/10.1109/ICECOS.2018.8605181 https://doi.org/10.1109/ICECOS.2018.8605181

Ledig C , Theis L , Huszár F , et al. , 2017 . Photo-realistic single image super-resolution using a generative adversarial network . IEEE Conf on Computer Vision and Pattern Recognition , p. 105 - 114 . https://doi.org/10.1109/CVPR.2017.19 https://doi.org/10.1109/CVPR.2017.19

Li BB , Wu YH , Song JR , et al. , 2021 . DeepFed: federated deep learning for intrusion detection in industrial cyber-physical systems . IEEE Trans Ind Inform , 17 ( 8 ): 5615 - 5624 . https://doi.org/10.1109/TII.2020.3023430 https://doi.org/10.1109/TII.2020.3023430

Li DT , Feng HY , Gao YH , 2021 . A network security evaluation method based on machine learning algorithm . Electr Des Eng , 29 ( 12 ): 138 - 142, 147 (in Chinese) . https://doi.org/10.14022/j.issn1674-6236.2021.12.030 https://doi.org/10.14022/j.issn1674-6236.2021.12.030

Li GF , Huang YX , Bie ZH , et al. , 2020 . Machine-learning-based reliability evaluation framework for power distribution networks . IET Gener Trans Distrib , 14 ( 12 ): 2282 - 2291 . https://doi.org/10.1049/iet-gtd.2019.1520 https://doi.org/10.1049/iet-gtd.2019.1520

Liu P , Zang WY , 2003 . Incentive-based modeling and inference of attacker intent, objectives, and strategies . Proc 10 th ACM Conf on Computer and Communications Security , p. 179 - 189 . https://doi.org/10.1145/948109.948135 https://doi.org/10.1145/948109.948135

Liu XH , Zhang HW , Dong SQ , et al. , 2021 . Network defense decision-making based on a stochastic game system and a deep recurrent Q-network . Comput Secur , 111 : 102480 . https://doi.org/10.1016/j.cose.2021.102480 https://doi.org/10.1016/j.cose.2021.102480

Liu XX , Zhang JX , Zhu PD , et al. , 2021 . Quantitative cyber-physical security analysis methodology for industrial control systems based on incomplete information Bayesian game . Comput Secur , 102 : 102138 . https://doi.org/10.1016/j.cose.2020.102138 https://doi.org/10.1016/j.cose.2020.102138

Long J , Shelhamer E , Darrell T , 2015 . Fully convolutional networks for semantic segmentation . Proc IEEE Conf on Computer Vision and Pattern Recognition , p. 3431 - 3440 . https://doi.org/10.1109/CVPR.2015.7298965 https://doi.org/10.1109/CVPR.2015.7298965

Luan D , Tan XB , 2021 . EWM-IFAHP: an improved network security situation assessment model . 2 nd Int Conf on Machine Learning and Computer Application , p. 1 - 6 .

Lye KW , Wing J , 2002 . Game Strategies in Cyberspace Security . Technical Report, No. CMU-CS-02-136 , School of Computer Science, Carnegie Mellon University , Pittsburgh, USA .

Ma PC , Jiang B , Lu ZG , et al. , 2021 . Cybersecurity named entity recognition using bidirectional long short-term memory with conditional random fields . Tsinghua Sci Technol , 26 ( 3 ): 259 - 265 . https://doi.org/10.26599/TST.2019.9010033 https://doi.org/10.26599/TST.2019.9010033

Mehta V , Bartzis C , Zhu HF , et al. , 2006 . Ranking attack graphs . Proc 9 th Int Workshop on Recent Advances in Intrusion Detection , p. 127 - 144 . https://doi.org/10.1007/11856214_7 https://doi.org/10.1007/11856214_7

Mishra P , Varadharajan V , Tupakula U , et al. , 2019 . A detailed investigation and analysis of using machine learning techniques for intrusion detection . IEEE Commun Surv Tut , 21 ( 1 ): 686 - 728 . https://doi.org/10.1109/COMST.2018.2847722 https://doi.org/10.1109/COMST.2018.2847722

Mohiuddin MA , Khan SA , Engelbrecht AP , 2016 . Fuzzy particle swarm optimization algorithms for the open shortest path first weight setting problem . Appl Intell , 45 ( 3 ): 598 - 621 . https://doi.org/10.1007/s10489-016-0776-0 https://doi.org/10.1007/s10489-016-0776-0

Moizuddin MD , Jose MV , 2022 . A bio-inspired hybrid deep learning model for network intrusion detection . Knowl-Based Syst , 238 : 107894 . https://doi.org/10.1016/j.knosys.2021.107894 https://doi.org/10.1016/j.knosys.2021.107894

Mushtaq E , Zameer A , Umer M , et al. , 2022 . A two-stage intrusion detection system with auto-encoder and LSTMs . Appl Soft Comput , 121 : 108768 . https://doi.org/10.1016/j.asoc.2022.108768 https://doi.org/10.1016/j.asoc.2022.108768

Narudin FA , Feizollah A , Anuar NB , et al. , 2016 . Evaluation of machine learning classifiers for mobile malware detection . Soft Comput , 20 ( 1 ): 343 - 357 . https://doi.org/10.1007/s00500-014-1511-6 https://doi.org/10.1007/s00500-014-1511-6

Nguyen HT , Torrano-Gimenez C , Alvarez G , et al. , 2011 . Application of the generic feature selection measure in detection of web attacks . In: Herrero Á , Corchado E (Eds.) , Computational Intelligence in Security for Information Systems . Springer , Berlin , p. 25 - 32 . https://doi.org/10.1007/978-3-642-21323-6_4 https://doi.org/10.1007/978-3-642-21323-6_4

Nguyen TTT , Armitage G , 2008 . A survey of techniques for Internet traffic classification using machine learning . IEEE Commun Surv Tut , 10 ( 4 ): 56 - 76 . https://doi.org/10.1109/SURV.2008.080406 https://doi.org/10.1109/SURV.2008.080406

Nishiyama T , Kumagai A , Kamiya K , et al. , 2020 . SILU: strategy involving large-scale unlabeled logs for improving malware detector . IEEE Symp on Computers and Communications , p. 1 - 7 . https://doi.org/10.1109/ISCC50000.2020.9219571 https://doi.org/10.1109/ISCC50000.2020.9219571

Nisioti A , Mylonas A , Yoo PD , et al. , 2018 . From intrusion detection to attacker attribution: a comprehensive survey of unsupervised methods . IEEE Commun Surv Tut , 20 ( 4 ): 3369 - 3388 . https://doi.org/10.1109/COMST.2018.2854724 https://doi.org/10.1109/COMST.2018.2854724

Olowononi FO , Rawat DB , Liu CM , 2021 . Resilient machine learning for networked cyber physical systems: a survey for machine learning security to securing machine learning for CPS . IEEE Commun Surv Tut , 23 ( 1 ): 524 - 552 . https://doi.org/10.1109/COMST.2020.3036778 https://doi.org/10.1109/COMST.2020.3036778

Park JB , Jeong YW , Shin JR , et al. , 2010 . Closure to discussion of "An improved particle swarm optimization for nonconvex economic dispatch problems." IEEE Trans Power Syst , 25 ( 4 ): 2010 - 2011 . https://doi.org/10.1109/TPWRS.2010.2069890 https://doi.org/10.1109/TPWRS.2010.2069890

Pouyanfar S , Sadiq S , Yan YL , et al. , 2019 . A survey on deep learning: algorithms, techniques, and applications . ACM Comput Surv , 51 ( 5 ): 92 . https://doi.org/10.1145/3234150 https://doi.org/10.1145/3234150

Pu ZY , 2020 . Network security situation analysis based on a dynamic Bayesian network and phase space reconstruction . J Supercomput , 76 ( 2 ): 1342 - 1357 . https://doi.org/10.1007/s11227-018-2575-3 https://doi.org/10.1007/s11227-018-2575-3

Qazi EUH , Imran M , Haider N , et al. , 2022 . An intelligent and efficient network intrusion detection system using deep learning . Comput Electr Eng , 99 : 107764 . https://doi.org/10.1016/j.compeleceng.2022.107764 https://doi.org/10.1016/j.compeleceng.2022.107764

Roopak M , Tian GY , Chambers J , 2019 . Deep learning models for cyber security in IoT networks . IEEE 9 th Annual Computing and Communication Workshop and Conf , p. 452 - 457 . https://doi.org/10.1109/CCWC.2019.8666588 https://doi.org/10.1109/CCWC.2019.8666588

Sagar BS , Niranjan S , Kashyap N , et al. , 2019 . Providing cyber security using artificial intelligence—a survey . 3 rd Int Conf on Computing Methodologies and Communication , p. 717 - 720 . https://doi.org/10.1109/ICCMC.2019.8819719 https://doi.org/10.1109/ICCMC.2019.8819719

Salih A , Zeebaree ST , Ameen S , et al. , 2021 . A survey on the role of artificial intelligence, machine learning and deep learning for cybersecurity attack detection . 7 th Int Engineering Conf "Research & Innovation amid Global Pandemic" , p. 61 - 66 . https://doi.org/10.1109/IEC52205.2021.9476132 https://doi.org/10.1109/IEC52205.2021.9476132

Sapavath NN , Muhati E , Rawat DB , 2021 . Prediction and detection of cyberattacks using AI model in virtualized wireless networks . 8 th IEEE Int Conf on Cyber Security and Cloud Computing (CSCloud)/7 th IEEE Int Conf on Edge Computing and Scalable Cloud , p. 97 - 102 . https://doi.‍org/10.1109/CSCloud-EdgeCom52276.2021.00027 https://doi.‍org/10.1109/CSCloud-EdgeCom52276.2021.00027

Seth JK , Chandra S , 2018 . MIDS: metaheuristic based intrusion detection system for cloud using k-NN and MGWO . 2 nd Int Conf on Advances in Computing and Data Sciences , p. 411 - 420 . https://doi.org/10.1007/978-981-13-1810-8_41 https://doi.org/10.1007/978-981-13-1810-8_41

Shafiqur R , Salman K , Luai MA , 2020 . The effect of acceleration coefficients in particle swarm optimization algorithm with application to wind farm layout design . FME Trans , 48 ( 4 ): 922 - 930 . https://doi.org/10.5937/fme2004922r https://doi.org/10.5937/fme2004922r

Shaikh RA , Shashikala SV , 2019 . An autoencoder and LSTM based intrusion detection approach against denial of service attacks . Proc 1 st Int Conf on Advances in Information Technology , p. 406 - 410 . https://doi.org/10.1109/ICAIT47043.2019.8987336 https://doi.org/10.1109/ICAIT47043.2019.8987336

Shende S , Thorat S , 2020 . A review on deep learning method for intrusion detection in network security . 2 nd Int Conf on Innovative Mechanisms for Industry Applications , p. 173 - 177 . https://doi.org/10.1109/ICIMIA48430.2020.9074975 https://doi.org/10.1109/ICIMIA48430.2020.9074975

Socher R , Huang EH , Pennington J , et al. , 2011a . Dynamic pooling and unfolding recursive autoencoders for paraphrase detection . Proc 24 th Int Conf on Neural Information Processing Systems , p. 801 - 809 .

Socher R , Lin CCY , Ng AY , et al. , 2011b . Parsing natural scenes and natural language with recursive neural networks . Proc 28 th Int Conf on Machine Learning , p. 129 - 136 .

Stampa G , Arias M , Sanchez-Charles D , et al. , 2017 . A deep-reinforcement learning approach for software-defined networking routing optimization . https://arxiv.org/abs/1709.07080 https://arxiv.org/abs/1709.07080

Stevens-Navarro E , Lin YX , Wong VWS , 2008 . An MDP-based vertical handoff decision algorithm for heterogeneous wireless networks . IEEE Trans Veh Technol , 57 ( 2 ): 1243 - 1254 . https://doi.org/10.1109/TVT.2007.907072 https://doi.org/10.1109/TVT.2007.907072

Su JY , 2021 . Intelligent network security situation prediction method based on deep reinforcement learning . IEEE Int Conf on Industrial Application of Artificial Intelligence , p. 343 - 348 . https://doi.org/10.1109/IAAI54625.2021.9699894 https://doi.org/10.1109/IAAI54625.2021.9699894

Sun YY , Liu JJ , Wang JD , et al. , 2020 . When machine learning meets privacy in 6G: a survey . IEEE Commun Surv Tut , 22 ( 4 ): 2694 - 2724 . https://doi.org/10.1109/COMST.2020.3011561 https://doi.org/10.1109/COMST.2020.3011561

Sutskever I , Vinyals O , Le QV , 2014 . Sequence to sequence learning with neural networks . Proc 27 th Int Conf on Neural Information Processing Systems , p. 3104 - 3112 .

Tekerek T , 2021 . A novel architecture for web-based attack detection using convolutional neural network . Comput Secur , 100 : 102096 . https://doi.org/10.1016/j.cose.2020.102096 https://doi.org/10.1016/j.cose.2020.102096

Torres JM , Comesaña CI , García-Nieto PJ , 2019 . Review: machine learning techniques applied to cybersecurity . Int J Mach Learn Cybern , 10 ( 10 ): 2823 - 2836 . https://doi.org/10.1007/S13042-018-00906-1 https://doi.org/10.1007/S13042-018-00906-1

Touhiduzzaman M , Hahn A , Srivastava AK , 2019 . A diversity-based substation cyber defense strategy utilizing coloring games . IEEE Trans Smart Grid , 10 ( 5 ): 5405 - 5415 . https://doi.org/10.1109/TSG.2018.2881672 https://doi.org/10.1109/TSG.2018.2881672

Ullah F , Naeem H , Jabbar S , et al. , 2019 . Cyber security threats detection in Internet of Things using deep learning approach . IEEE Access , 7 : 124379 - 124389 . https://doi.org/10.1109/ACCESS.2019.2937347 https://doi.org/10.1109/ACCESS.2019.2937347

Waibel A , Hanazawa T , Hinton G , et al. , 1990 . Phoneme recognition using time-delay neural networks . In: Waibe A , Lee KF (Eds.) , Readings in Speech Recognition . Elsevier , Amsterdam, the Netherlands , p. 393 - 404 . https://doi.org/10.1016/B978-0-08-051584-7.50037-1 https://doi.org/10.1016/B978-0-08-051584-7.50037-1

Wang JH , Shan ZL , Tan HS , et al. , 2021 . Network security situation assessment based on genetic optimized PNN neural network . Comput Sci , 48 ( 6 ): 338 - 342 (in Chinese) .

Wang PY , Govindarasu M , 2020 . Multi-agent based attack-resilient system integrity protection for smart grid . IEEE Trans Smart Grid , 11 ( 4 ): 3447 - 3456 . https://doi.org/10.1109/TSG.2020.2970755 https://doi.org/10.1109/TSG.2020.2970755

Wei MH , 2021 . A new information security evaluation algorithm based on recurrent neural . J Mianyang Teach Coll , 40 ( 2 ): 75 - 80, 87 (in Chinese) . https://doi.org/10.16276/j.cnki.cn51-1670/g.2021.02.015 https://doi.org/10.16276/j.cnki.cn51-1670/g.2021.02.015

Wei YF , Yu FR , Song M , et al. , 2019 . Joint optimization of caching, computing, and radio resources for fog-enabled IoT using natural actor-critic deep reinforcement learning . IEEE Int Things J , 6 ( 2 ): 2061 - 2073 . https://doi.org/10.1109/JIOT.2018.2878435 https://doi.org/10.1109/JIOT.2018.2878435

Wickramasinghe CS , Marino DL , Amarasinghe K , et al. , 2018 . Generalization of deep learning for cyber-physical system security: a survey . Proc 44 th Annual Conf of the IEEE Industrial Electronics Society , p. 745 - 751 . https://doi.org/10.1109/IECON.2018.8591773 https://doi.org/10.1109/IECON.2018.8591773

Wu SX , Banzhaf W , 2010 . The use of computational intelligence in intrusion detection systems: a review . Appl Soft Comput , 10 ( 1 ): 1 - 35 . https://doi.org/10.1016/j.asoc.2009.06.019 https://doi.org/10.1016/j.asoc.2009.06.019

Xiao JP , Long C , Zhao J , et al. , 2021 . Survey of network intrusion detection based on deep learning . Front Data Comput , 3 ( 3 ): 59 - 74 (in Chinese) . https://doi.org/10.12379/j.issn.2096-1057.2022.12.03 https://doi.org/10.12379/j.issn.2096-1057.2022.12.03

Xin Y , Kong LS , Liu Z , et al. , 2018 . Machine learning and deep learning methods for cybersecurity . IEEE Access , 6 : 35365 - 35381 . https://doi.org/10.1109/ACCESS.2018.2836950 https://doi.org/10.1109/ACCESS.2018.2836950

Yang HY , Zeng RY , 2021 . Method for assessment of network security situation with deep learning . J Xidian Univ , 48 ( 1 ): 183 - 190 (in Chinese) . https://doi.org/10.19665/j.issn1001-2400.2021.01.021 https://doi.org/10.19665/j.issn1001-2400.2021.01.021

Yang HY , Zeng RY , Xu GQ , et al. , 2021 . A network security situation assessment method based on adversarial deep learning . Appl Soft Comput , 102 : 107096 . https://doi.org/10.1016/j.asoc.2021.107096 https://doi.org/10.1016/j.asoc.2021.107096

Yang HY , Zhang ZX , Zhang L , 2022a . Network security situation assessment based on deep weighted feature learning . J Cyber Secur , 7 ( 4 ): 32 - 43 (in Chinese) . https://doi.org/10.19363/J.cnki.cn10-1380/tn.2022.07.03 https://doi.org/10.19363/J.cnki.cn10-1380/tn.2022.07.03

Yang HY , Zhang ZX , Zhang L , 2022b . Network security situation assessments with parallel feature extraction and an improved BiGRU . J Tsinghua Univ (Sci Technol) , 62 ( 5 ): 842 - 848 (in Chinese) . https://doi.org/10.16511/j.cnki.qhdxxb.2022.22.006 https://doi.org/10.16511/j.cnki.qhdxxb.2022.22.006

Yang XJ , Jia YM , 2021 . IPSO-LSTM: a new Internet security situation prediction model . 2 nd Int Conf on Machine Learning and Computer Application , p. 1 - 5 .

Ye L , Tan ZJ , 2019 . A method of network security situation assessment based on deep learning . Intell Comput Appl , 9 ( 6 ): 73 - 75, 82 (in Chinese) . https://doi.org/10.3969/j.issn.2095-2163.2019.06.015 https://doi.org/10.3969/j.issn.2095-2163.2019.06.015

Yeom S , Kim K , 2019 . Detail analysis on machine learning based malicious network traffic classification . Proc 8 th Int Conf on Smart Media & Applications , p. 49 - 53 .

Zeadally S , Adi E , Baig Z , et al. , 2020 . Harnessing artificial intelligence capabilities to improve cybersecurity . IEEE Access , 8 : 23817 - 23837 . https://doi.org/10.1109/ACCESS.2020.2968045 https://doi.org/10.1109/ACCESS.2020.2968045

Zhang HY , Lin KY , Chen WW , et al. , 2019 . Using machine learning techniques to improve intrusion detection accuracy . IEEE 2 nd Int Conf on Knowledge Innovation and Invention , p. 308 - 310 . https://doi.org/10.1109/ICKII46306.2019.9042621 https://doi.org/10.1109/ICKII46306.2019.9042621

Zhang M , Xu BY , Bai S , et al. , 2017 . A deep learning method to detect web attacks using a specially designed CNN . Proc 24 th Int Conf on Neural Information Processing , p. 828 - 836 . https://doi.org/10.1007/978-3-319-70139-4_84 https://doi.org/10.1007/978-3-319-70139-4_84

Zhang R , Wang YB , 2016 . Research on machine learning with algorithm and development . J Commun Univ China (Sci Technol) , 23 ( 2 ): 10 - 18, 24 (in Chinese) . https://doi.org/10.16196/j.cnki.issn.1673-4793.2016.02.002 https://doi.org/10.16196/j.cnki.issn.1673-4793.2016.02.002

Zhang R , Pan ZH , Yin YF , 2021 . Research on assessment algorithm for network security situation based on SSA-BP neural network . 7 th Int Symp on System and Software Reliability , p. 140 - 145 . https://doi.org/10.1109/ISSSR53171.2021.00024 https://doi.org/10.1109/ISSSR53171.2021.00024

Zhang R , Pan ZH , Yin YF , et al. , 2022 . Network security situation assessment model based on SAA-SSA-BPNN . Comput Eng Appl , 58 ( 11 ): 117 - 124 (in Chinese) . https://doi.org/10.3778/j.issn.1002-8331.2110-0391 https://doi.org/10.3778/j.issn.1002-8331.2110-0391

Zhang ZQ , 2021 . Research on network security situation prediction based on improved and optimized BP neural network . 2 nd Int Conf on Electronics, Communications and Information Technology , p. 1014 - 1018 . https://doi.org/10.1109/CECIT53797.2021.00180 https://doi.org/10.1109/CECIT53797.2021.00180

Zhou XY , Belkin M , 2014 . Semi-supervised learning . Acad Press Libr Signal Process , 1 : 1239 - 1269 . https://doi.org/10.1016/B978-0-12-396502-8.00022-X https://doi.org/10.1016/B978-0-12-396502-8.00022-X

Zhou ZH , 2016 . Machine Learning . Tsinghua University Press , Beijing, China , p. 390 - 392 (in Chinese) .

Views

928

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Deep3DSketch-im: rapid high-fidelity AI 3D model generation by single freehand sketches

Efficient and optimized approximate GDI full adders based on dynamic threshold CNTFETs for specific least significant bits

Intelligent fractional-order integral sliding mode control for PMSM based on an improved cascade observer

Cyber security meets artificial intelligence: a survey

Related Author

Tianrun CHEN

Runlong CAO

Zejian LI

Ying ZANG

Lingyun SUN

Ayoub SADEGHI

Razieh GHASEMI

Hossein GHASEMIAN

Related Institution

College of Computer Science and Technology, Zhejiang University

School of Software Technology, Zhejiang University

School of Information Engineering, Huzhou University

Department of Electrical Engineering, Shiraz Branch, Islamic Azad University

School of Electrical Engineering, Iran University of Science and Technology

Chat

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰