{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:01:21Z","timestamp":1783008081584,"version":"3.54.5"},"reference-count":121,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T00:00:00Z","timestamp":1686355200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T00:00:00Z","timestamp":1686355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-023-01926-7","type":"journal-article","created":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T11:02:18Z","timestamp":1686394938000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Survey on Intrusion Detection and Prevention Systems"],"prefix":"10.1007","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8299-4317","authenticated-orcid":false,"given":"Neha","family":"Gupta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0481-4840","authenticated-orcid":false,"given":"Vinita","family":"Jindal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6007-7961","authenticated-orcid":false,"given":"Punam","family":"Bedi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,10]]},"reference":[{"key":"1926_CR1","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.cose.2016.11.004","volume":"65","author":"A Aburomman","year":"2017","unstructured":"Aburomman A, Reaz MB. A survey of intrusion detection systems based on ensemble and hybrid classifiers. Comput Secur. 2017;65:135\u201352. https:\/\/doi.org\/10.1016\/j.cose.2016.11.004.","journal-title":"Comput Secur"},{"key":"1926_CR2","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.jnca.2015.11.016","volume":"60","author":"M Ahmed","year":"2016","unstructured":"Ahmed M, Mahmood AN, Hu J. A survey of network anomaly detection techniques. J Netw Comput Appl. 2016;60:19\u201331. https:\/\/doi.org\/10.1016\/j.jnca.2015.11.016.","journal-title":"J Netw Comput Appl"},{"key":"1926_CR3","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.patrec.2016.11.018","volume":"85","author":"WL Al-Yaseen","year":"2017","unstructured":"Al-Yaseen WL, Othman ZA, Nazri MZ. Real-time multi-agent system for an adaptive intrusion detection system. Pattern Recognit Lett. 2017;85:56\u201364. https:\/\/doi.org\/10.1016\/j.patrec.2016.11.018.","journal-title":"Pattern Recognit Lett"},{"key":"1926_CR4","doi-asserted-by":"publisher","unstructured":"Anantvalee T, Wu J. A survey on intrusion detection in mobile ad hoc networks. In: Wireless network security. signals and communication technology. Boston: Springer; 2007. p. 159\u2013180. https:\/\/doi.org\/10.1007\/978-0-387-33112-6_7.","DOI":"10.1007\/978-0-387-33112-6_7"},{"issue":"1","key":"1926_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3103\/S0146411616010028","volume":"50","author":"K Anusha","year":"2016","unstructured":"Anusha K, Sathiyamoorthy E. Comparative study for feature selection algorithms in intrusion detection system. Autom Control Comput Sci. 2016;50(1):1\u20139. https:\/\/doi.org\/10.3103\/S0146411616010028.","journal-title":"Autom Control Comput Sci"},{"key":"1926_CR6","doi-asserted-by":"publisher","unstructured":"Anwar S, Zain JM, Zolkipli MF, Inayat Z, Jabir AN, Odili JB. Response option for attacks detected by intrusion detection system. In: 2015 4th international conference on software engineering and computer systems (ICSECS). Kuantan: IEEE; 2015. p. 195\u2013200. https:\/\/doi.org\/10.1109\/ICSECS.2015.7333109.","DOI":"10.1109\/ICSECS.2015.7333109"},{"issue":"2","key":"1926_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/a10020039","volume":"10","author":"S Anwar","year":"2017","unstructured":"Anwar S, Zain JM, Zolkipli MF, Inayat Z, Khan S, Anthony B, Chang V. From intrusion detection to an intrusion response system: fundamentals, requirements, and future directions. Algorithms. 2017;10(2):1\u201324.","journal-title":"Algorithms"},{"issue":"6","key":"1926_CR8","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1080\/19393555.2020.1767240","volume":"29","author":"FE Ayo","year":"2020","unstructured":"Ayo FE, Folorunso SO, Abayomi-Alli AA, Adekunle AO, Awotunde JB. Network intrusion detection based on deep learning model optimized with rule-based hybrid feature selection. Inf Secur J Glob Perspect. 2020;29(6):267\u201383. https:\/\/doi.org\/10.1080\/19393555.2020.1767240.","journal-title":"Inf Secur J Glob Perspect"},{"key":"1926_CR9","doi-asserted-by":"crossref","unstructured":"Bachl M, Meghdouri F, Fabini J, Zseby T. SparseIDS: learning packet sampling with reinforcement learning. arXiv:2002.03872. 2020. p. 1\u20139.","DOI":"10.1109\/CNS48642.2020.9162253"},{"key":"1926_CR10","doi-asserted-by":"crossref","unstructured":"Bedi P, Gupta N, Jindal V. Siam-IDS: handling class imbalance problem in intrusion detection systems using siamese neural network. In: Presented in third international conference on computing and network communications, Trivandrum. 2019.","DOI":"10.1016\/j.procs.2020.04.085"},{"issue":"2021","key":"1926_CR11","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1007\/s10489-020-01886-y","volume":"51","author":"P Bedi","year":"2020","unstructured":"Bedi P, Gupta N, Jindal V. I-SiamIDS: an improved Siam-IDS for handling class imbalance in network-based intrusion detection systems. Appl Intell. 2020;51(2021):1133\u201351. https:\/\/doi.org\/10.1007\/s10489-020-01886-y.","journal-title":"Appl Intell"},{"key":"1926_CR12","unstructured":"Bejoy B, Subbiah J. Artificial immune system based intrusion detection system\u2014a comprehensive review. Int J Comput Eng Technol. 2017;8(1):85\u201395. http:\/\/www.iaeme.com\/MasterAdmin\/Journal_uploads\/IJCET\/VOLUME_8_ISSUE_1\/IJCET_08_01_010.pdf."},{"key":"1926_CR13","doi-asserted-by":"publisher","unstructured":"Berenjian S, Shajari M, Farshid N, Hatamian M. Intelligent automated intrusion response system based on fuzzy decision making and risk assessment. In: 2016 IEEE 8th international conference on intelligent systems (IS). Sofia: IEEE; 2016. p. 709\u2013714. https:\/\/doi.org\/10.1109\/IS.2016.7737389.","DOI":"10.1109\/IS.2016.7737389"},{"issue":"3","key":"1926_CR14","doi-asserted-by":"publisher","first-page":"69","DOI":"10.12691\/ajis-4-3-2","volume":"4","author":"M Bijone","year":"2016","unstructured":"Bijone M. A survey on secure network: intrusion detection & prevention approaches. Am J Inf Syst. 2016;4(3):69\u201388. https:\/\/doi.org\/10.12691\/ajis-4-3-2.","journal-title":"Am J Inf Syst"},{"key":"1926_CR15","unstructured":"Biswas SK. Intrusion detection using machine learning: a comparison study. Special Issue in Int J Pure Appl Math (IJPAM). 2018;118(19):101\u2013114. https:\/\/acadpubl.eu\/jsi\/2018-118-19\/articles\/19a\/8.pdf."},{"key":"1926_CR16","doi-asserted-by":"publisher","unstructured":"Blanco R, Cilla JJ, Briongos S, Malag\u00f3n P, Moya JM. Applying cost-sensitive classifiers with reinforcement learning to IDS. In: Intelligent data engineering and automated learning\u2014IDEAL 2018. Madrid: Springer; 2018. p. 531\u2013538. https:\/\/doi.org\/10.1007\/978-3-030-03493-1_55.","DOI":"10.1007\/978-3-030-03493-1_55"},{"issue":"2","key":"1926_CR17","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/COMST.2015.2494502","volume":"18","author":"AL Buczak","year":"2016","unstructured":"Buczak AL, Guven E. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutor. 2016;18(2):1153\u201376. https:\/\/doi.org\/10.1109\/COMST.2015.2494502.","journal-title":"IEEE Commun Surv Tutor"},{"key":"1926_CR18","doi-asserted-by":"publisher","first-page":"18558","DOI":"10.1109\/ACCESS.2019.2895898","volume":"7","author":"W Bul\u2019ajoul","year":"2019","unstructured":"Bul\u2019ajoul W, James A, Shaikh S. A new architecture for network intrusion and prevention. IEEE Access. 2019;7:18558\u201373. https:\/\/doi.org\/10.1109\/ACCESS.2019.2895898.","journal-title":"IEEE Access"},{"key":"1926_CR19","doi-asserted-by":"crossref","unstructured":"Chalapathy R, Chawla S. Deep learning for anomaly detection: a survey. arXiv:1901.03407. 2019. p. 1\u201350.","DOI":"10.1145\/3394486.3406704"},{"key":"1926_CR20","doi-asserted-by":"publisher","unstructured":"Chandra A, Khatri SK, Simon R. Filter-based attribute selection approach for intrusion detection using k-means clustering and sequential minimal optimization technique. In: 2019 amity international conference on artificial intelligence (AICAI). Dubai: IEEE; 2019. p. 740\u2013745. https:\/\/doi.org\/10.1109\/AICAI.2019.8701373.","DOI":"10.1109\/AICAI.2019.8701373"},{"key":"1926_CR21","doi-asserted-by":"publisher","unstructured":"Chapaneri R, Shah S. Comprehensive survey of machine learning-based network intrusion detection. In: smart intelligent computing and applications. Singapore: Springer; 2019. p. 345\u2013356. https:\/\/doi.org\/10.1007\/978-981-13-1921-1_35.","DOI":"10.1007\/978-981-13-1921-1_35"},{"key":"1926_CR22","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1016\/j.procs.2018.05.108","volume":"132","author":"A Chellam","year":"2018","unstructured":"Chellam A, Ramanathan L, Surbhi R. Intrusion detection in computer networks using lazy learning algorithm. Proc Comput Sci. 2018;132:928\u201336.","journal-title":"Proc Comput Sci"},{"key":"1926_CR23","doi-asserted-by":"publisher","unstructured":"Chowdhury MU, Hammond F, Konowicz G, Xin C, Wu H, Li J. A few-shot deep learning approach for improved intrusion detection. In: 2017 IEEE 8th annual ubiquitous computing, electronics and mobile communication conference (UEMCON). New York: IEEE; 2017. p. 456\u2013462. https:\/\/doi.org\/10.1109\/UEMCON.2017.8249084.","DOI":"10.1109\/UEMCON.2017.8249084"},{"issue":"1","key":"1926_CR24","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1080\/19393555.2020.1797248","volume":"30","author":"W Cui","year":"2021","unstructured":"Cui W, Lu Q, Qureshi AM, Li W, Wu K. An adaptive LeNet-5 model for anomaly detection. Inf Secur J Glob Perspect. 2021;30(1):19\u201329. https:\/\/doi.org\/10.1080\/19393555.2020.1797248.","journal-title":"Inf Secur J Glob Perspect"},{"key":"1926_CR25","doi-asserted-by":"publisher","unstructured":"Das R, Morris TH. Machine learning and cyber security. In: 2017 international conference on computer, electrical & communication engineering (ICCECE). Kolkata: IEEE; 2017. p. 1\u20137. doi:https:\/\/doi.org\/10.1109\/ICCECE.2017.8526232","DOI":"10.1109\/ICCECE.2017.8526232"},{"issue":"2","key":"1926_CR26","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1109\/TSE.1987.232894","volume":"13","author":"DE Denning","year":"1987","unstructured":"Denning DE. An intrusion-detection model. IEEE Trans Softw Eng. 1987;13(2):222\u201332.","journal-title":"IEEE Trans Softw Eng"},{"key":"1926_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00991-0","volume":"3","author":"B Deore","year":"2022","unstructured":"Deore B, Bhosale S. Intrusion detection system based on RNN classifier for feature reduction. SN Comput Sci. 2022;3:1\u20139. https:\/\/doi.org\/10.1007\/s42979-021-00991-0.","journal-title":"SN Comput Sci"},{"key":"1926_CR28","doi-asserted-by":"publisher","first-page":"3787","DOI":"10.1007\/s10462-019-09779-4","volume":"53","author":"R Domingues","year":"2020","unstructured":"Domingues R, Michiardi P, Barlet J, Filippone M. A comparative evaluation of novelty detection algorithms for discrete sequences. Artif Intell Rev. 2020;53:3787\u2013812. https:\/\/doi.org\/10.1007\/s10462-019-09779-4.","journal-title":"Artif Intell Rev"},{"issue":"1","key":"1926_CR29","doi-asserted-by":"publisher","first-page":"39","DOI":"10.12928\/telkomnika.v17i1.9191","volume":"17","author":"GI Duppa","year":"2019","unstructured":"Duppa GI, Surantha N. Evaluation of network security based on next generation intrusion prevention system. Telkomnika. 2019;17(1):39\u201348.","journal-title":"Telkomnika"},{"key":"1926_CR30","doi-asserted-by":"publisher","unstructured":"Enache A-C, Sg\u00e2rciu V, Togan M. Comparative study on feature selection methods rooted in swarm intelligence for intrusion detection. In: 2017 21st international conference on control systems and computer science (CSCS). Bucharest: IEEE; 2017. p. 239\u2013244. https:\/\/doi.org\/10.1109\/CSCS.2017.40.","DOI":"10.1109\/CSCS.2017.40"},{"issue":"5","key":"1926_CR31","doi-asserted-by":"publisher","first-page":"675","DOI":"10.6633\/IJNS.201709.19(5).04","volume":"19","author":"Y Farhaoui","year":"2017","unstructured":"Farhaoui Y. Design and implementation of an intrusion prevention system. Int J Netw Secur. 2017;19(5):675\u201383. https:\/\/doi.org\/10.6633\/IJNS.201709.19(5).04.","journal-title":"Int J Netw Secur"},{"key":"1926_CR32","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.jisa.2017.06.007","volume":"35","author":"DA Fernandes","year":"2017","unstructured":"Fernandes DA, Freire MM, Fazendeiro PA, In\u00e1cio PR. Applications of artificial immune systems to computer security: a survey. J Inf Secur Appl. 2017;35:138\u201359. https:\/\/doi.org\/10.1016\/j.jisa.2017.06.007.","journal-title":"J Inf Secur Appl"},{"key":"1926_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jnca.2016.03.011","volume":"66","author":"G Folino","year":"2016","unstructured":"Folino G, Sabatino P. Ensemble based collaborative and distributed intrusion detection systems: a survey. J Netw Comput Appl. 2016;66:1\u201316. https:\/\/doi.org\/10.1016\/j.jnca.2016.03.011.","journal-title":"J Netw Comput Appl"},{"key":"1926_CR34","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.cose.2016.06.005","volume":"62","author":"M GhasemiGol","year":"2016","unstructured":"GhasemiGol M, Takabi H, Ghaemi-Bafghi A. A foresight model for intrusion response management. Comput Secur. 2016;62:73\u201394. https:\/\/doi.org\/10.1016\/j.cose.2016.06.005.","journal-title":"Comput Secur"},{"key":"1926_CR35","doi-asserted-by":"crossref","unstructured":"Glass-Vanderlan TR, Iannacone MD, Vincent MS, Chen Q, Bridges RA. A survey of intrusion detection systems leveraging host data. arXiv:1805.06070 [cs.CR]. 2018. p. 1\u201340.","DOI":"10.1145\/3344382"},{"issue":"2","key":"1926_CR36","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1080\/19393555.2020.1722296","volume":"29","author":"A Gupta","year":"2020","unstructured":"Gupta A, Sharma LS. Detecting attacks in high-speed networks: Issues and solutions. Inf Secur J Glob Perspect. 2020;29(2):51\u201361. https:\/\/doi.org\/10.1080\/19393555.2020.1722296.","journal-title":"Inf Secur J Glob Perspect"},{"key":"1926_CR37","doi-asserted-by":"crossref","unstructured":"Gupta N, Bedi P, Jindal V. Effect of activation functions on the performance of deep learning algorithms for network intrusion detection systems. In: International conference on emerging trends in information technology (ICETIT-2019). Delhi: Springer; 2019. p. 1\u201312.","DOI":"10.1007\/978-3-030-30577-2_84"},{"key":"1926_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108076","volume":"192","author":"N Gupta","year":"2021","unstructured":"Gupta N, Jindal V, Bedi P. LIO-IDS: Handling class imbalance using LSTM and improved one-vs-one technique in intrusion detection system. Comput Netw. 2021;192: 108076. https:\/\/doi.org\/10.1016\/j.comnet.2021.108076.","journal-title":"Comput Netw"},{"key":"1926_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102499","volume":"112","author":"N Gupta","year":"2022","unstructured":"Gupta N, Jindal V, Bedi P. CSE-IDS: using cost-sensitive deep learning and ensemble algorithms to handle class imbalance in network-based intrusion detection systems. Comput Secur. 2022;112: 102499. https:\/\/doi.org\/10.1016\/j.cose.2021.102499.","journal-title":"Comput Secur"},{"key":"1926_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102448","volume":"110","author":"Z Halim","year":"2021","unstructured":"Halim Z, Yousaf MN, Waqas M, Sulaiman M, Abbas G, Hussain M, Hanif M. An effective genetic algorithm-based feature selection method for intrusion detection systems. Comput Secur. 2021;110: 102448. https:\/\/doi.org\/10.1016\/j.cose.2021.102448.","journal-title":"Comput Secur"},{"key":"1926_CR41","doi-asserted-by":"publisher","unstructured":"Hamed T, Ernst JB, Kremer SC. A survey and taxonomy of classifiers of intrusion detection systems. In: Computer and network security essentials. Cham: Springer; 2018. p. 21\u201339. https:\/\/doi.org\/10.1007\/978-3-319-58424-9_2.","DOI":"10.1007\/978-3-319-58424-9_2"},{"key":"1926_CR42","doi-asserted-by":"publisher","unstructured":"Hamed T, Ernst JB, Kremer SC. A survey and taxonomy on data and pre-processing techniques of intrusion detection systems. In: Computer and network security essentials. Cham: Springer; 2018. p. 113\u2013134. https:\/\/doi.org\/10.1007\/978-3-319-58424-9_7.","DOI":"10.1007\/978-3-319-58424-9_7"},{"issue":"4","key":"1926_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/widm.1306","volume":"9","author":"A Handa","year":"2019","unstructured":"Handa A, Sharma A, Shukla SK. Machine learning in cybersecurity: a review. Wiley Interdiscip Rev Data Min Knowl Discov. 2019;9(4):1\u20137. https:\/\/doi.org\/10.1002\/widm.1306.","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"issue":"3","key":"1926_CR44","doi-asserted-by":"publisher","first-page":"9","DOI":"10.14569\/IJARAI.2015.040302","volume":"4","author":"NF Haq","year":"2015","unstructured":"Haq NF, Onik AR, Hridoy MK, Rafni M, Shah FM, Farid MD. Application of machine learning approaches in intrusion detection system: a survey. IJARAI Int J Adv Res Artif Intell. 2015;4(3):9\u201318. https:\/\/doi.org\/10.14569\/IJARAI.2015.040302.","journal-title":"IJARAI Int J Adv Res Artif Intell"},{"key":"1926_CR45","doi-asserted-by":"publisher","unstructured":"Hasegawa H, Yamaguchi Y, Shimada H, Takakura H. An incident response support system based on seriousness of infection. In: 2016 international conference on information networking (ICOIN). Kota Kinabalu: IEEE; 2016. p. 69\u201374. https:\/\/doi.org\/10.1109\/ICOIN.2016.7427090.","DOI":"10.1109\/ICOIN.2016.7427090"},{"key":"1926_CR46","doi-asserted-by":"publisher","first-page":"104650","DOI":"10.1109\/ACCESS.2020.3000179","volume":"8","author":"H Hindy","year":"2020","unstructured":"Hindy H, Brosset D, Bayne E, Seeam AK, Tachtatzis C, Atkinson R, Bellekens X. A taxonomy of network threats and the effect of current datasets on intrusion detection systems. IEEE Access. 2020;8:104650\u201375. https:\/\/doi.org\/10.1109\/ACCESS.2020.3000179.","journal-title":"IEEE Access"},{"key":"1926_CR47","unstructured":"Hindy H, Brosset D, Bayne E, Seeam A, Tachtatzis C, Atkinson R, Bellekens X. A taxonomy and survey of intrusion detection system design techniques, network threats and datasets. 2018;1(1), 1\u201335. arXiv:1806.03517v1 [cs.CR]."},{"key":"1926_CR48","doi-asserted-by":"crossref","unstructured":"Hindy H, Hodo E, Bayne E, Seeam A, Atkinson R, Bellekens X. A taxonomy of malicious traffic for intrusion detection systems. In: 2018 international conference on cyber situational awareness, data analytics and assessment (Cyber SA). Glasgow: IEEE; 2018. p. 1\u20134.","DOI":"10.1109\/CyberSA.2018.8551386"},{"key":"1926_CR49","unstructured":"Hodo E, Bellekens X, Hamilton A, Tachtatzis C, Atkinson R. Shallow and deep networks intrusion detection system: a taxonomy and survey. 2017. arXiv:1701.02145."},{"key":"1926_CR50","unstructured":"Hofmeyr SA. Affinity maturation. 1997. https:\/\/www.cs.unm.edu\/~immsec\/html-imm\/affmat.html. Accessed 17 June 2019."},{"issue":"1","key":"1926_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3168446","volume":"13","author":"S Iannucci","year":"2018","unstructured":"Iannucci S, Abdelwahed S. Model-based response planning strategies for autonomic intrusion protection. ACM Trans Auton Adapt Syst (TAAS). 2018;13(1):1\u201323.","journal-title":"ACM Trans Auton Adapt Syst (TAAS)."},{"key":"1926_CR52","doi-asserted-by":"publisher","unstructured":"Iannucci S, Chen Q, Abdelwahed S. High-performance intrusion response planning on many-core architectures. In: 2016 25th international conference on computer communication and networks (ICCCN). Waikoloa: IEEE; 2016. p. 1\u20136. https:\/\/doi.org\/10.1109\/ICCCN.2016.7568529.","DOI":"10.1109\/ICCCN.2016.7568529"},{"key":"1926_CR53","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.jnca.2015.12.006","volume":"62","author":"Z Inayat","year":"2016","unstructured":"Inayat Z, Gani A, Anuar NB, Khan MK, Anwar S. Intrusion response systems: foundations, design, and challenges. J Netw Comput Appl. 2016;62:53\u201374. https:\/\/doi.org\/10.1016\/j.jnca.2015.12.006.","journal-title":"J Netw Comput Appl"},{"key":"1926_CR54","doi-asserted-by":"publisher","first-page":"3299","DOI":"10.1007\/s10462-020-09948-w","volume":"54","author":"MN Injadat","year":"2021","unstructured":"Injadat MN, Moubayed A, Nassif AB, Shami A. Machine learning towards intelligent systems: applications, challenges, and opportunities. Artif Intell Rev. 2021;54:3299\u2013348. https:\/\/doi.org\/10.1007\/s10462-020-09948-w.","journal-title":"Artif Intell Rev"},{"key":"1926_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1088\/1742-6596\/1000\/1\/012049","volume":"1000","author":"S Jose","year":"2018","unstructured":"Jose S, Malathi D, Reddy B, Jayaseeli D. A survey on anomaly based host intrusion detection system. J Phys Conf Ser. 2018;1000:1\u201310. https:\/\/doi.org\/10.1088\/1742-6596\/1000\/1\/012049.","journal-title":"J Phys Conf Ser"},{"key":"1926_CR56","unstructured":"Vigneswaran R, Poornachandran P, Soman KP. A compendium on network and host based intrusion detection systems (ICDSMLA). In: International conference on data science, machine learning & applications. Hyderabad: Springer; 2019. p. 1\u20138."},{"key":"1926_CR57","doi-asserted-by":"publisher","unstructured":"Kenkre PS, Pai A, Colaco L. Real time intrusion detection and prevention system. In: Proceedings of the 3rd international conference on frontiers of intelligent computing: theory and applications (FICTA) 2014. Bhubaneswar, Odisha, India: Springer, Cham; 2015. p. 405\u2013411. https:\/\/doi.org\/10.1007\/978-3-319-11933-5_44.","DOI":"10.1007\/978-3-319-11933-5_44"},{"issue":"4","key":"1926_CR58","doi-asserted-by":"publisher","first-page":"583","DOI":"10.3390\/sym11040583","volume":"11","author":"MA Khan","year":"2019","unstructured":"Khan MA, Karim MR, Kim Y. A scalable and hybrid intrusion detection system based on the convolutional-LSTM network. Symmetry. 2019;11(4):583.","journal-title":"Symmetry"},{"key":"1926_CR59","doi-asserted-by":"publisher","unstructured":"Kim K, Aminanto ME, Tanuwidjaja HC. Classical machine learning and its applications to IDS. In: Network intrusion detection using deep learning. Part of the springerbriefs on cyber security systems and networks book series (BRIEFSCSSN). Singapore: Springer; 2018. p. 13\u201326. https:\/\/doi.org\/10.1007\/978-981-13-1444-5_3.","DOI":"10.1007\/978-981-13-1444-5_3"},{"key":"1926_CR60","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1007\/s10586-017-1117-8","volume":"22","author":"D Kwon","year":"2019","unstructured":"Kwon D, Kim H, Kim J, Suh SC, Kim I, Kim KJ. A survey of deep learning-based network anomaly detection. Clust Comput. 2019;22:949\u201361. https:\/\/doi.org\/10.1007\/s10586-017-1117-8.","journal-title":"Clust Comput"},{"key":"1926_CR61","doi-asserted-by":"publisher","unstructured":"Latha S, Prakash SJ. A survey on network attacks and intrusion detection systems. In: 2017 4th international conference on advanced computing and communication systems (ICACCS). Coimbatore: IEEE; 2017. p. 1\u20137. https:\/\/doi.org\/10.1109\/ICACCS.2017.8014614.","DOI":"10.1109\/ICACCS.2017.8014614"},{"key":"1926_CR62","unstructured":"Lee B, Amaresh S, Green C, Engels D. Comparative study of deep learning models for network intrusion detection. SMU Data Sci Rev 2018;1(1):1\u201313. https:\/\/scholar.smu.edu\/datasciencereview."},{"issue":"5","key":"1926_CR63","doi-asserted-by":"publisher","first-page":"823","DOI":"10.14704\/nq.2018.16.5.1391","volume":"16","author":"C Li","year":"2018","unstructured":"Li C, Wang J, Ye X. Using a recurrent neural network and restricted boltzmann machines for malicious traffic detection. NeuroQuantology. 2018;16(5):823\u201331. https:\/\/doi.org\/10.14704\/nq.2018.16.5.1391.","journal-title":"NeuroQuantology"},{"key":"1926_CR64","doi-asserted-by":"publisher","unstructured":"Li F, Xiong F, Li C, Yin L, Shi G, Tian B. SRAM: a state-aware risk assessment model for intrusion response. In: 2017 IEEE second international conference on data science in cyberspace (DSC). Shenzhen: IEEE; 2017. p. 232\u2013237. https:\/\/doi.org\/10.1109\/DSC.2017.9.","DOI":"10.1109\/DSC.2017.9"},{"key":"1926_CR65","doi-asserted-by":"publisher","unstructured":"Li Z, Rios AL, Xu G, Trajkovi\u0107 L. Machine learning techniques for classifying network anomalies and intrusions. In: 2019 IEEE international symposium on circuits and systems (ISCAS). Sapporo: IEEE; 2019. p. 1\u20135. https:\/\/doi.org\/10.1109\/ISCAS.2019.8702583.","DOI":"10.1109\/ISCAS.2019.8702583"},{"issue":"5","key":"1926_CR66","first-page":"98","volume":"51","author":"M Liu","year":"2018","unstructured":"Liu M, Xue Z, Xu X, Zhong C, Chen J. Host-based intrusion detection system with system calls: review and future trends. ACM Comput Surv (CSUR). 2018;51(5):98.","journal-title":"ACM Comput Surv (CSUR)"},{"key":"1926_CR67","doi-asserted-by":"publisher","unstructured":"Lopes A, Hutchison A. Experimenting with machine learning in automated intrusion response. In: International symposium on intelligent and distributed computing. Petersburg: Springer; 2019. p. 505\u2013514.https:\/\/doi.org\/10.1007\/978-3-030-32258-8_59.","DOI":"10.1007\/978-3-030-32258-8_59"},{"key":"1926_CR68","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112963","volume":"141","author":"M Lopez-Martin","year":"2020","unstructured":"Lopez-Martin M, Carro B, Sanchez-Esguevillas A. Application of deep reinforcement learning to intrusion detection for supervised problems. Expert Syst Appl. 2020;141: 112963. https:\/\/doi.org\/10.1016\/j.eswa.2019.112963.","journal-title":"Expert Syst Appl"},{"key":"1926_CR69","doi-asserted-by":"publisher","unstructured":"Makani R, Reddy B. Taxonomy of machine leaning based anomaly detection and its suitability. In: International conference on computational intelligence and data science (ICCIDS 2018), vol 132. Procedia Computer Science, Elsevier. 2018. p. 1842\u20131849. https:\/\/doi.org\/10.1016\/j.procs.2018.05.133.","DOI":"10.1016\/j.procs.2018.05.133"},{"key":"1926_CR70","unstructured":"Milan SH, Singh K. Reducing false alarms in intrusion detection systems\u2014a survey. Int Res J Eng Technol (IRJET). 2018;5(2):9\u201312. https:\/\/www.irjet.net\/archives\/V5\/i2\/IRJET-V5I203.pdf."},{"issue":"1","key":"1926_CR71","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2808691","volume":"48","author":"A Milenkoski","year":"2015","unstructured":"Milenkoski A, Vieira M, Kounev S, Avritzer A, Payne BD. Evaluating computer intrusion detection systems: a survey of common practices. ACM Comput Surv. 2015;48(1):1\u201341. https:\/\/doi.org\/10.1145\/2808691.","journal-title":"ACM Comput Surv"},{"key":"1926_CR72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/4943509","volume":"2018","author":"E Min","year":"2018","unstructured":"Min E, Long J, Liu Q, Cui J, Chen W. TR-IDS: anomaly-based intrusion detection through text-convolutional neural network and random forest. Secur Commun Netw. 2018;2018:1\u20139.","journal-title":"Secur Commun Netw"},{"issue":"1","key":"1926_CR73","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1109\/COMST.2018.2847722","volume":"21","author":"P Mishra","year":"2018","unstructured":"Mishra P, Varadharajan V, Tupakula U, Pilli ES. A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Commun Surv Tutor. 2018;21(1):686\u2013728. https:\/\/doi.org\/10.1109\/COMST.2018.2847722.","journal-title":"IEEE Commun Surv Tutor"},{"key":"1926_CR74","first-page":"1","volume":"43","author":"S Mishra","year":"2018","unstructured":"Mishra S, Sagban R, Yakoob A, Gandhi N. Swarm intelligence in anomaly detection systems: an overview. Int J Comput Appl. 2018;43:1\u201310.","journal-title":"Int J Comput Appl"},{"key":"1926_CR75","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.jisa.2018.11.007","volume":"44","author":"S Mohammadi","year":"2019","unstructured":"Mohammadi S, Mirvaziri H, Ghazizadeh-Ahsaee M, Karimipour H. Cyber intrusion detection by combined feature selection algorithm. J Inf Secur Appl. 2019;44:80\u20138. https:\/\/doi.org\/10.1016\/j.jisa.2018.11.007.","journal-title":"J Inf Secur Appl"},{"key":"1926_CR76","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.jnca.2018.12.006","volume":"128","author":"N Moustafa","year":"2019","unstructured":"Moustafa N, Hu J, Slay J. A holistic review of network anomaly detection systems: a comprehensive survey. J Netw Comput Appl. 2019;128:33\u201355. https:\/\/doi.org\/10.1016\/j.jnca.2018.12.006.","journal-title":"J Netw Comput Appl"},{"issue":"1","key":"1926_CR77","doi-asserted-by":"publisher","first-page":"52","DOI":"10.18178\/lnit.3.1.52-55","volume":"3","author":"S Naseer","year":"2015","unstructured":"Naseer S, Mahmood R. Intrusion detection techniques in mobile adhoc networks: a review. Lect Notes Inf Theory. 2015;3(1):52\u20135. https:\/\/doi.org\/10.18178\/lnit.3.1.52-55.","journal-title":"Lect Notes Inf Theory"},{"key":"1926_CR78","doi-asserted-by":"publisher","unstructured":"Neelima D, Karthik J, John KA, Gowthami S, Nayak J. Soft computing-based intrusion detection approaches: an analytical study. In: Soft computing in data analytics. Advances in intelligent systems and computing, vol 758. Singapore: Springer; 2019. p. 635\u2013651. https:\/\/doi.org\/10.1007\/978-981-13-0514-6_61.","DOI":"10.1007\/978-981-13-0514-6_61"},{"issue":"6\u20137","key":"1926_CR79","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1080\/01969722.2017.1319693","volume":"48","author":"SK Nejat","year":"2017","unstructured":"Nejat SK, Kabiri P. An adaptive and cost-based intrusion response system. Cybern Syst. 2017;48(6\u20137):495\u2013509.","journal-title":"Cybern Syst"},{"key":"1926_CR80","doi-asserted-by":"publisher","unstructured":"Ortu\u00f1o SY, Aguilar JA, Taboada B, Ortiz CA, Ram\u00edrez MP, Figueroa GA. The use of artificial intelligence for the intrusion detection system in computer networks. In: Mexican international conference on artificial intelligence. Cham: Springer; 2019. p. 302\u2013312.https:\/\/doi.org\/10.1007\/978-3-030-02837-4_25.","DOI":"10.1007\/978-3-030-02837-4_25"},{"issue":"4","key":"1926_CR81","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1108\/09685221011079199","volume":"18","author":"A Patel","year":"2010","unstructured":"Patel A, Qassim Q, Wills C. A survey of intrusion detection and prevention systems. Inf Manag Comput Secur. 2010;18(4):277\u201390. https:\/\/doi.org\/10.1108\/09685221011079199.","journal-title":"Inf Manag Comput Secur"},{"key":"1926_CR82","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.jnca.2016.06.012","volume":"72","author":"J Peng","year":"2016","unstructured":"Peng J, Choo K-KR, Ashman H. User profiling in intrusion detection: a review. J Netw Comput Appl. 2016;72:14\u201327. https:\/\/doi.org\/10.1016\/j.jnca.2016.06.012.","journal-title":"J Netw Comput Appl"},{"issue":"3","key":"1926_CR83","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/widm.1301","volume":"9","author":"P Probst","year":"2019","unstructured":"Probst P, Wright MN, Boulesteix A-L. Hyperparameters and tuning strategies for random forest. Wiley Interdiscip Rev Data Min Knowl Discov. 2019;9(3):1\u201315. https:\/\/doi.org\/10.1002\/widm.1301.","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"issue":"3","key":"1926_CR84","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3184898","volume":"51","author":"AA Ramaki","year":"2018","unstructured":"Ramaki AA, Rasoolzadegan A, Bafghi AG. A systematic mapping study on intrusion alert analysis in intrusion detection systems. ACM Comput Surv (CSUR). 2018;51(3):1\u201341. https:\/\/doi.org\/10.1145\/3184898.","journal-title":"ACM Comput Surv (CSUR)"},{"key":"1926_CR85","doi-asserted-by":"crossref","unstructured":"Rani M, Gagandeep. A review of intrusion detection system in cloud computing. In: Proceedings of international conference on sustainable computing in science, technology and management (SUSCOM). Jaipur. 2019. p. 770\u2013776.","DOI":"10.2139\/ssrn.3355127"},{"issue":"3","key":"1926_CR86","first-page":"48","volume":"51","author":"PA Resende","year":"2018","unstructured":"Resende PA, Drummond AC. A survey of random forest based methods for intrusion detection systems. ACM Comput Surv (CSUR). 2018;51(3):48.","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"2019","key":"1926_CR87","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.cose.2019.06.005","volume":"86","author":"M Ring","year":"2019","unstructured":"Ring M, Wunderlich S, Scheuring D, Landes D, Hotho A. A survey of network-based intrusion detection data sets. Comput Secur. 2019;86(2019):147\u201367. https:\/\/doi.org\/10.1016\/j.cose.2019.06.005.","journal-title":"Comput Secur"},{"key":"1926_CR88","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.procs.2016.09.347","volume":"95","author":"S Rizvi","year":"2016","unstructured":"Rizvi S, Labrador G, Guyan M, Savan J. Advocating for hybrid intrusion detection prevention system and framework improvement. Proc Comput Sci. 2016;95:369\u201374.","journal-title":"Proc Comput Sci"},{"key":"1926_CR89","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/s10462-017-9567-1","volume":"51","author":"AI Saleh","year":"2019","unstructured":"Saleh AI, Fatma FM, Labib LM. A hybrid intrusion detection system (HIDS) based on prioritized k-nearest neighbors and optimized SVM classifiers. Artif Intell Rev. 2019;51:403\u201343. https:\/\/doi.org\/10.1007\/s10462-017-9567-1.","journal-title":"Artif Intell Rev"},{"key":"1926_CR90","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.comnet.2018.11.010","volume":"148","author":"F Salo","year":"2019","unstructured":"Salo F, Nassif AB, Essex A. Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection. Comput Netw. 2019;148:164\u201375. https:\/\/doi.org\/10.1016\/j.comnet.2018.11.010.","journal-title":"Comput Netw"},{"key":"1926_CR91","doi-asserted-by":"publisher","unstructured":"S\u00e1nchez JF, Parra OJ, S\u00e1nchez LC. A game theory approach for intrusion prevention systems. Applied computer sciences in engineering. WEA 2018, vol 915. p. 218\u2013229. Medell\u00edn: Springer; 2018. https:\/\/doi.org\/10.1007\/978-3-030-00350-0_19.","DOI":"10.1007\/978-3-030-00350-0_19"},{"key":"1926_CR92","unstructured":"Sandhu UA, Haider S, Naseer S, Ateeb OU. A survey of intrusion detection & prevention techniques. In: 2011 international conference on information communication and management. Singapore: IACSIT Press; 2011. p. 66\u201371."},{"key":"1926_CR93","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-020-00213-z","volume":"1","author":"A Sar\u0131kaya","year":"2020","unstructured":"Sar\u0131kaya A, K\u0131l\u0131\u00e7 BG. A class-specific intrusion detection model: hierarchical multi-class ids model. SN Comput Sci. 2020;1:1\u201311. https:\/\/doi.org\/10.1007\/s42979-020-00213-z.","journal-title":"SN Comput Sci"},{"key":"1926_CR94","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00535-6","volume":"2","author":"IH Sarker","year":"2021","unstructured":"Sarker IH. Deep cybersecurity: a comprehensive overview from neural network and deep learning perspective. SN Comput Sci. 2021;2:1\u201316. https:\/\/doi.org\/10.1007\/s42979-021-00535-6.","journal-title":"SN Comput Sci"},{"key":"1926_CR95","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.eswa.2016.03.042","volume":"60","author":"P Saurabh","year":"2016","unstructured":"Saurabh P, Verma B. An efficient proactive artificial immune system based anomaly detection and prevention system. Expert Syst Appl. 2016;60:311\u201320. https:\/\/doi.org\/10.1016\/j.eswa.2016.03.042.","journal-title":"Expert Syst Appl"},{"key":"1926_CR96","doi-asserted-by":"publisher","unstructured":"Sawant A. A comparative study of different intrusion prevention systems. In: 2018 fourth international conference on computing communication control and automation (ICCUBEA). Pune: IEEE; 2018. p. 1\u20135. https:\/\/doi.org\/10.1109\/ICCUBEA.2018.8697500.","DOI":"10.1109\/ICCUBEA.2018.8697500"},{"issue":"9","key":"1926_CR97","first-page":"936","volume":"4","author":"R Sekhar","year":"2015","unstructured":"Sekhar R, Perumal K, Rani SV. Analysis of next generation intrusion prevention system using sensor fusion and fuzzy logic. Int J Sci Res Eng Technol (IJSRET). 2015;4(9):936\u20138.","journal-title":"Int J Sci Res Eng Technol (IJSRET)"},{"key":"1926_CR98","doi-asserted-by":"publisher","unstructured":"Sen S. A survey of intrusion detection systems using evolutionary computation. In: Bio-inspired computation in telecommunications. Morgan Kaufmann; 2015. p. 73\u201394. https:\/\/doi.org\/10.1016\/B978-0-12-801538-4.00004-5.","DOI":"10.1016\/B978-0-12-801538-4.00004-5"},{"key":"1926_CR99","unstructured":"Shameli-Sendi A, Ezzati-jivan N, Jabbarifar M, Dagenais M. Intrusion response systems: survey and taxonomy. Int J Comput Sci Netw Secur. 2012;12(1):1\u201314. https:\/\/www.researchgate.net\/profile\/Alireza_Shameli-Sendi\/publication\/267917501_Intrusion_Response_Systems_Survey_and_Taxonomy\/links\/54da21270cf2970e4e7dc67c.pdf."},{"key":"1926_CR100","doi-asserted-by":"publisher","first-page":"52427","DOI":"10.1109\/ACCESS.2019.2912114","volume":"7","author":"RK Sharma","year":"2019","unstructured":"Sharma RK, Issac B, Kalita HK. Intrusion detection and response system inspired by the defense mechanism of plants. IEEE Access. 2019;7:52427\u201339. https:\/\/doi.org\/10.1109\/ACCESS.2019.2912114.","journal-title":"IEEE Access"},{"issue":"3","key":"1926_CR101","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.cose.2011.12.012","volume":"31","author":"A Shiravi","year":"2012","unstructured":"Shiravi A, Shiravi H, Tavallaee M. Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput Secur. 2012;31(3):357\u201374. https:\/\/doi.org\/10.1016\/j.cose.2011.12.012.","journal-title":"Comput Secur"},{"issue":"1","key":"1926_CR102","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/TETCI.2017.2772792","volume":"2","author":"N Shone","year":"2018","unstructured":"Shone N, Ngoc TN, Phai VD, Shi Q. A deep learning approach to network intrusion detection. IEEE Trans Emerg Top Comput Intell. 2018;2(1):41\u201350. https:\/\/doi.org\/10.1109\/TETCI.2017.2772792.","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"key":"1926_CR103","doi-asserted-by":"crossref","unstructured":"Siregar B, Purba RF, Seniman, Fahmi F. Intrusion prevention system against denial of service attacks using genetic algorithm. In: 2018 IEEE international conference on communication, networks and satellite (Comnetsat). Medan: IEEE; 2018. p. 55\u201359.","DOI":"10.1109\/COMNETSAT.2018.8684039"},{"key":"1926_CR104","doi-asserted-by":"crossref","unstructured":"Solomon IA, Jatain A, Bajaj SB. Neural network based intrusion detection: state of the art. In: Proceedings of international conference on sustainable computing in science, technology and management (SUSCOM). Jaipur: Elsevier SSRN; 2019. p. 1390\u20131396.","DOI":"10.2139\/ssrn.3356505"},{"key":"1926_CR105","unstructured":"Stakhanova N, Basu S, Wong JS. A taxonomy of intrusion response systems. Iowa State University. 2006."},{"key":"1926_CR106","doi-asserted-by":"publisher","unstructured":"Strasburg C, Stakhanova N, Basu S, Wong JS. Intrusion response cost assessment methodology. In: ASIACCS '09: proceedings of the 4th international symposium on information, computer, and communications security. Sydney: ACM, New York; 2009. p. 388\u2013391. https:\/\/doi.org\/10.1145\/1533057.1533112.","DOI":"10.1145\/1533057.1533112"},{"key":"1926_CR107","doi-asserted-by":"publisher","unstructured":"Suliman SI, Shukor MS, Kassim M, Mohamad R, Shahbudin S. Network intrusion detection system using artificial immune system (AIS). In: 2018 3rd international conference on computer and communication systems (ICCCS). Nagoya: IEEE; 2018. p. 178\u2013182. https:\/\/doi.org\/10.1109\/CCOMS.2018.8463274.","DOI":"10.1109\/CCOMS.2018.8463274"},{"key":"1926_CR108","doi-asserted-by":"publisher","unstructured":"Tabatabaefar M, Miriestahbanati M, Gr\u00e9goire J-C. Network intrusion detection through artificial immune system. In: 2017 annual IEEE international systems conference (SysCon). Montreal: IEEE; 2017. p. 1\u20136. https:\/\/doi.org\/10.1109\/SYSCON.2017.7934751.","DOI":"10.1109\/SYSCON.2017.7934751"},{"key":"1926_CR109","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-10037-9","author":"A Thakkar","year":"2021","unstructured":"Thakkar A, Lohiya R. A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions. Artif Intell Rev. 2021. https:\/\/doi.org\/10.1007\/s10462-021-10037-9.","journal-title":"Artif Intell Rev"},{"key":"1926_CR110","unstructured":"Timmis J. Basic immune inspired algorithms. Retrieved from AISWeb The Online Home of Artificial Immune Systems. 2013. http:\/\/www.artificial-immune-systems.org\/."},{"issue":"2","key":"1926_CR111","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.1109\/TVT.2021.3053015","volume":"70","author":"S Tu","year":"2021","unstructured":"Tu S, Waqas M, Rehman SU, Mir T, Abbas G, Abbas ZH, Ahmad I. Reinforcement learning assisted impersonation attack detection in device-to-device communications. IEEE Trans Veh Technol. 2021;70(2):1474\u20139. https:\/\/doi.org\/10.1109\/TVT.2021.3053015.","journal-title":"IEEE Trans Veh Technol"},{"key":"1926_CR112","unstructured":"Ugochukwu CJ, Bennett EO. An intrusion detection system using machine learning algorithm. Int J Comput Sci Math Theory. 2018;4(1):39\u201347. https:\/\/www.iiardpub.org\/get\/IJCSMT\/VOL.%204%20NO.%201%202018\/An%20Intrusion%20Detection.pdf."},{"key":"1926_CR113","doi-asserted-by":"publisher","unstructured":"Varma PK, Kumari VV, Kumar SS. A survey of feature selection techniques in intrusion detection system: a soft computing perspective. In: Progress in computing, analytics and networking. Advances in intelligent systems and computing. 2018;710:785\u2013793. Bhubaneshwar: Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-10-7871-2_75.","DOI":"10.1007\/978-981-10-7871-2_75"},{"key":"1926_CR114","doi-asserted-by":"publisher","unstructured":"Vasudeo SH, Patil P, Kumar RV. IMMIX-intrusion detection and prevention system. In: 2015 international conference on smart technologies and management for computing, communication, controls, energy and materials (ICSTM). Chennai: IEEE; 2015. p. 96\u2013101. https:\/\/doi.org\/10.1109\/ICSTM.2015.7225396.","DOI":"10.1109\/ICSTM.2015.7225396"},{"key":"1926_CR115","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.swevo.2017.07.002","volume":"38","author":"JM Vidal","year":"2018","unstructured":"Vidal JM, Orozco AL, Villalba LJ. Adaptive artificial immune networks for mitigating DoS flooding attacks. Swarm Evolut Comput. 2018;38:94\u2013108. https:\/\/doi.org\/10.1016\/j.swevo.2017.07.002.","journal-title":"Swarm Evolut Comput"},{"key":"1926_CR116","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2019.2945555","author":"K Vieira","year":"2019","unstructured":"Vieira K, Koch FL, Sobral JB, Westphall CB, Le\u00e3o JL. Autonomic intrusion detection and response using big data. IEEE Syst J. 2019. https:\/\/doi.org\/10.1109\/JSYST.2019.2945555.","journal-title":"IEEE Syst J"},{"key":"1926_CR117","doi-asserted-by":"publisher","first-page":"41525","DOI":"10.1109\/ACCESS.2019.2895334","volume":"7","author":"R Vinayakumar","year":"2019","unstructured":"Vinayakumar R, Alazab M, Soman KP, Poornachandran P, Al-Nemrat A, Venkatraman S. Deep learning approach for intelligent intrusion detection system. IEEE Access. 2019;7:41525\u201350. https:\/\/doi.org\/10.1109\/ACCESS.2019.2895334.","journal-title":"IEEE Access"},{"key":"1926_CR118","doi-asserted-by":"publisher","unstructured":"Wei X. Design and implementation of a lightweight intrusion detection and prevention system. In: International conference on security and privacy in new computing environments. Cham: Springer; 2019. p. 433\u2013439. https:\/\/doi.org\/10.1007\/978-3-030-21373-2_34.","DOI":"10.1007\/978-3-030-21373-2_34"},{"key":"1926_CR119","doi-asserted-by":"publisher","first-page":"35365","DOI":"10.1109\/ACCESS.2018.2836950","volume":"6","author":"Y Xin","year":"2018","unstructured":"Xin Y, Kong L, Liu Z, Chen Y, Li Y, Zhu H, Wang C. Machine learning and deep learning methods for cybersecurity. IEEE Access. 2018;6:35365\u201381. https:\/\/doi.org\/10.1109\/ACCESS.2018.2836950.","journal-title":"IEEE Access"},{"key":"1926_CR120","doi-asserted-by":"crossref","unstructured":"Yang J-N, Zhang H-Q, Zhang C-F. Intrusion response decision-making method based on reinforcement learning. In: 2018 international conference on communication, network and artificial intelligence (CNAI 2018). Beijing. 2018. p. 1\u20139.","DOI":"10.12783\/dtcse\/cnai2018\/24149"},{"key":"1926_CR121","doi-asserted-by":"publisher","unstructured":"Zheng L, Yuan H, Peng X, Zhu G, Guo Y, Xu H, Deng G. Research on distributed high speed network intrusion prevention system. In: Cyber security intelligence and analytics. CSIA 2019. Advances in intelligent systems and computing. Shenyang: Springer, Cham; 2020. p. 1118\u20131126. https:\/\/doi.org\/10.1007\/978-3-030-15235-2_148.","DOI":"10.1007\/978-3-030-15235-2_148"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-023-01926-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-023-01926-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-023-01926-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T11:13:48Z","timestamp":1686395628000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-023-01926-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,10]]},"references-count":121,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["1926"],"URL":"https:\/\/doi.org\/10.1007\/s42979-023-01926-7","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,10]]},"assertion":[{"value":"6 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"439"}}