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.
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。
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.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
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
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
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
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
Apruzzese G, Colajanni M, Ferretti L, et al., 2018. On the effectiveness of machine and deep learning for cyber security. Proc 10th Int Conf on Cyber Conflict, p.371–390. 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
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 7th Conf on Systems, Process and Control, p.269–274. 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 17th Int Conf on Computer and Information Science, p.34–38. 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
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
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
Bhuiyan TH, Medal HR, Nandi AK, et al., 2021. Risk-averse bi-level stochastic network interdiction model for cybersecurity risk management. Int J Crit Infrastr Prot, 32: 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 14th Int ISC (Iranian Society of Cryptology) Conf on Information Security and Cryptology, p.8–12. 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
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
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
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
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
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 4th 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
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
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
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
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
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
Diao WP, 2021. Network security situation forecast model based on neural network algorithm development and verification. IEEE 4th Int Conf on Automation, Electronics and Electrical Engineering, p.462–465. 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
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
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
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
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
Goodfellow IJ, Pouget-Abadie J, Mirza M, et al., 2014. Generative adversarial nets. Proc 27th 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
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
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
Gupta ARB, Agrawal J, 2020. A comprehensive survey on various machine learning methods used for intrusion detection system. IEEE 9th Int Conf on Communication Systems and Network Technologies, p.282–289. 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
Hamrioui S, Bokhari S, 2021. A new cybersecurity strategy for IoE by exploiting an optimization approach. 12th Int Conf on Information and Communication Systems, p.23–28. 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
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
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
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
Hossain D, Ochiai H, Doudou F, et al., 2020. SSH and FTP brute-force attacks detection in computer networks: LSTM and machine learning approaches. 5th Int Conf on Computer and Communication Systems, p.491–497. 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
Hu CH, Liu GK, Li M, 2021. A network security situation prediction method based on SA-SSA. 14th Int Symp on Computational Intelligence and Design, p.105–110. 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
Huo D, Li XY, Li LH, et al., 2022. The application of 1D-CNN in microsoft malware detection. 7th Int Conf on Big Data Analytics, p.181–187. 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
Issa ASA, Albayrak Z, 2021. CLSTMNet: a deep learning model for intrusion detection. 3rd Int Scientific Conf of Engineering Sciences and Advances Technologies, Article 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. 9th Int Conf on Cloud Computing, Data Science & Engineering, p.120–125. 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
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
Kherlenchimeg Z, Nakaya N, 2018. Network intrusion classifier using autoencoder with recurrent neural network. Proc 4th 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
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
Krizhevsky A, Sutskever I, Hinton GE, 2012. ImageNet classification with deep convolutional neural networks. Proc 25th Int Conf on Neural Information Processing Systems, p.1097–1105. https://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
Kumar VS, Narasimhan VL, 2021. Using deep learning for assessing cybersecurity economic risks in virtual power plants. 7th Int Conf on Electrical Energy Systems, p.530–537. https://doi.org/10.1109/ICEES51510.2021.9383723
Kunal, Dua M, 2019. Machine learning approach to IDS: a comprehensive review. 3rd Int Conf on Electronics, Communication and Aerospace Technology, p.117–121. 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
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
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
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
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
Liu P, Zang WY, 2003. Incentive-based modeling and inference of attacker intent, objectives, and strategies. Proc 10th ACM Conf on Computer and Communications Security, p.179–189. 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
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
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
Luan D, Tan XB, 2021. EWM-IFAHP: an improved network security situation assessment model. 2nd 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
Mehta V, Bartzis C, Zhu HF, et al., 2006. Ranking attack graphs. Proc 9th Int Workshop on Recent Advances in Intrusion Detection, p.127–144. 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
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
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.kinosys.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
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
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
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
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
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
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
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
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
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
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
Roopak M, Tian GY, Chambers J, 2019. Deep learning models for cyber security in IoT networks. IEEE 9th Annual Computing and Communication Workshop and Conf, p.452–457. 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. 3rd Int Conf on Computing Methodologies and Communication, p.717–720. 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. 7th Int Engineering Conf “Research & Innovation amid Global Pandemic”, p.61–66. 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. 8th IEEE Int Conf on Cyber Security and Cloud Computing (CSCloud)/7th IEEE Int Conf on Edge Computing and Scalable Cloud, p.97–102. 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. 2nd Int Conf on Advances in Computing and Data Sciences, p.411–420. 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
Shaikh RA, Shashikala SV, 2019. An autoencoder and LSTM based intrusion detection approach against denial of service attacks. Proc 1st Int Conf on Advances in Information Technology, p.406–410. 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. 2nd Int Conf on Innovative Mechanisms for Industry Applications, p.173–177. 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 24th 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 28th 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
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
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
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
Sutskever I, Vinyals O, Le QV, 2014. Sequence to sequence learning with neural networks. Proc 27th 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
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
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
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
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
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
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
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
Wickramasinghe CS, Marino DL, Amarasinghe K, et al., 2018. Generalization of deep learning for cyber-physical system security: a survey. Proc 44th Annual Conf of the IEEE Industrial Electronics Society, p.745–751. 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
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
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
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
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
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
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
Yang XJ, Jia YM, 2021. IPSO-LSTM: a new Internet security situation prediction model. 2nd 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
Yeom S, Kim K, 2019. Detail analysis on machine learning based malicious network traffic classification. Proc 8th 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
Zhang HY, Lin KY, Chen WW, et al., 2019. Using machine learning techniques to improve intrusion detection accuracy. IEEE 2nd Int Conf on Knowledge Innovation and Invention, p.308–310. 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 24th Int Conf on Neural Information Processing, p.828–836. 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
Zhang R, Pan ZH, Yin YF, 2021. Research on assessment algorithm for network security situation based on SSA-BP neural network. 7th Int Symp on System and Software Reliability, p.140–145. 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
Zhang ZQ, 2021. Research on network security situation prediction based on improved and optimized BP neural network. 2nd Int Conf on Electronics, Communications and Information Technology, p.1014–1018. 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
Zhou ZH, 2016. Machine Learning. Tsinghua University Press, Beijing, China, p.390–392 (in Chinese).
Author information
Authors and Affiliations
Contributions
Jie CHEN determined the whole research framework and drafted the paper. Dandan WU searched the literature and made algorithm data analysis. Ruiyun XIE reviewed the entire research framework and technology. Jie CHEN and Dandan WU revised and finalized the paper.
Corresponding author
Ethics declarations
Jie CHEN, Dandan WU, and Ruiyun XIE declare that they have no conflict of interest.
Additional information
List of supplementary materials
1 Machine learning
2 Deep learning
3 Swarm intelligence optimization algorithm and population search algorithm
4 Main AI algorithms and applications
Fig. S1 Convolutional neural network method
Fig. S2 Recurrent neural network structure
Table S1 Main AI algorithms and applications
Supplementary materials
Rights and permissions
About this article
Cite this article
Chen, J., Wu, D. & Xie, R. Artificial intelligence algorithms for cyberspace security applications: a technological and status review. Front Inform Technol Electron Eng 24, 1117–1142 (2023). https://doi.org/10.1631/FITEE.2200314
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.2200314
Key words
- Artificial intelligence (AI)
- Machine learning (ML)
- Deep learning (DL)
- Optimization algorithm
- Hybrid algorithm
- Cyberspace security