{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T02:36:51Z","timestamp":1775788611354,"version":"3.50.1"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"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":["Int. J. Inf. Secur."],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s10207-023-00787-8","type":"journal-article","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T18:02:02Z","timestamp":1702576922000},"page":"1251-1277","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":79,"title":["TL-BILSTM IoT: transfer learning model for prediction of intrusion detection system in IoT environment"],"prefix":"10.1007","volume":"23","author":[{"given":"Himanshu","family":"Nandanwar","sequence":"first","affiliation":[]},{"given":"Rahul","family":"Katarya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"issue":"3","key":"787_CR1","doi-asserted-by":"publisher","first-page":"1573","DOI":"10.1007\/s11276-019-02043-1","volume":"27","author":"I Cviti\u0107","year":"2021","unstructured":"Cviti\u0107, I., Perakovi\u0107, D., Peri\u0161a, M., Botica, M.: Novel approach for detection of IoT generated DDoS traffic. Wirel. Netw. 27(3), 1573\u20131586 (2021)","journal-title":"Wirel. Netw."},{"issue":"11","key":"787_CR2","doi-asserted-by":"publisher","first-page":"11250","DOI":"10.1109\/JIOT.2020.2996671","volume":"7","author":"MS Abdalzaher","year":"2020","unstructured":"Abdalzaher, M.S., Muta, O.: A game-theoretic approach for enhancing security and data trustworthiness in IoT applications. IEEE Internet Things J. 7(11), 11250\u201311261 (2020)","journal-title":"IEEE Internet Things J."},{"key":"787_CR3","doi-asserted-by":"crossref","unstructured":"M. M. Salim, D. Wang, H. A. El Atty Elsayed, Y. Liu, and M. A. Elaziz, Joint optimization of energy-harvesting-powered two-way relaying D2D communication for IoT: a rate\u2013energy efficiency tradeoff. IEEE Internet Things J., vol. 7, no. 12, pp. 11735\u201311752 (2020)","DOI":"10.1109\/JIOT.2020.2999618"},{"key":"787_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102630","volume":"161","author":"SM Tahsien","year":"2020","unstructured":"Tahsien, S.M., Karimipour, H., Spachos, P.: Machine learning based solutions for security of Internet of Things (IoT): a survey. J. Netw. Comput. Appl. 161, 102630 (2020)","journal-title":"J. Netw. Comput. Appl."},{"key":"787_CR5","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.comnet.2019.01.023","volume":"151","author":"KAP da Costa","year":"2019","unstructured":"da Costa, K.A.P., Papa, J.P., Lisboa, C.O., Munoz, R., de Albuquerque, V.H.C.: Internet of things: a survey on machine learning-based intrusion detection approaches. Comput. Netw. 151, 147\u2013157 (2019)","journal-title":"Comput. Netw."},{"key":"787_CR6","doi-asserted-by":"crossref","unstructured":"Olowononi, F.O., Rawat, D.B. and Liu, C.: Federated learning with differential privacy for resilient vehicular cyber physical systems. In: Proc. IEEE 18th Annu. Consum. Commun. Netw. Conf. (CCNC), pp. 1\u20135 (2021)","DOI":"10.1109\/CCNC49032.2021.9369480"},{"issue":"4","key":"787_CR7","doi-asserted-by":"publisher","first-page":"2233","DOI":"10.1109\/TII.2014.2300753","volume":"10","author":"L Da Xu","year":"2014","unstructured":"Da Xu, L., He, W., Li, S.: Internet of things in industries: a survey. IEEE Trans. Ind. Inf. 10(4), 2233\u20132243 (2014)","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"7","key":"787_CR8","doi-asserted-by":"publisher","first-page":"e4137","DOI":"10.1002\/ett.4137","volume":"32","author":"M Sharma","year":"2021","unstructured":"Sharma, M., Pant, S., KumarSharma, D., DattaGupta, K., Vashishth, V., Chhabra, A.: Enabling security for the Industrial Internet of Things using deep learning, blockchain, and coalitions. Trans. Emerging Telecommun. Technol. 32(7), e4137 (2021)","journal-title":"Trans. Emerging Telecommun. Technol."},{"key":"787_CR9","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.jpdc.2022.01.015","volume":"162","author":"U Farooq","year":"2022","unstructured":"Farooq, U., Tariq, N., Asim, M., Baker, T., Al-Shamma\u2019a, A.: Machine learning and the internet of things security: solutions and open challenges. J. Parallel Distrib. Comput. 162, 89\u2013104 (2022)","journal-title":"J. Parallel Distrib. Comput."},{"issue":"4","key":"787_CR10","doi-asserted-by":"publisher","first-page":"3071","DOI":"10.1109\/JIOT.2021.3097951","volume":"10","author":"S Pant","year":"2023","unstructured":"Pant, S., Sharma, M., Sharma, D.K., Gupta, D., Rodrigues, J.J.P.C.: Enforcing intelligent learning-based security in internet of everything. IEEE Internet Things J. 10(4), 3071\u20133078 (2023). https:\/\/doi.org\/10.1109\/JIOT.2021.3097951","journal-title":"IEEE Internet Things J."},{"issue":"15","key":"787_CR11","doi-asserted-by":"publisher","first-page":"11935","DOI":"10.1109\/JIOT.2021.3063497","volume":"8","author":"K Zhang","year":"2021","unstructured":"Zhang, K., Ying, H., Dai, H.N., Li, L., Peng, Y., Guo, K., Yu, H.: Compacting deep neural networks for internet of things: methods and applications. IEEE Internet Things J. 8(15), 11935\u201311959 (2021)","journal-title":"IEEE Internet Things J."},{"key":"787_CR12","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.comcom.2021.04.002","volume":"173","author":"K Mao","year":"2021","unstructured":"Mao, K., Srivastava, G., Parizi, R.M., Khan, M.S.: Multi-source fusion for weak target images in the Industrial Internet of Things. Comput. Commun. 173, 150\u2013159 (2021)","journal-title":"Comput. Commun."},{"issue":"4","key":"787_CR13","doi-asserted-by":"publisher","first-page":"2545","DOI":"10.1109\/JIOT.2021.3077803","volume":"9","author":"V Mothukuri","year":"2021","unstructured":"Mothukuri, V., Khare, P., Parizi, R.M., Pouriyeh, S., Dehghantanha, A., Srivastava, G.: Federated-learning-based anomaly detection for iot security attacks. IEEE Internet Things J. 9(4), 2545\u20132554 (2021)","journal-title":"IEEE Internet Things J."},{"issue":"11","key":"787_CR14","doi-asserted-by":"publisher","first-page":"4724","DOI":"10.1109\/TII.2018.2852491","volume":"14","author":"E Sisinni","year":"2018","unstructured":"Sisinni, E., Saifullah, A., Han, S., Jennehag, U., Gidlund, M.: Industrial internet of things: challenges, opportunities, and directions. IEEE Trans. Ind. Inf. 14(11), 4724\u20134734 (2018)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"787_CR15","doi-asserted-by":"crossref","unstructured":"Namasudra, S.: An improved attribute\u2010based encryption technique towards the data security in cloud computing. Concurrency and Computation: Practice and Experience 31, no. 3: e4364 (2019)","DOI":"10.1002\/cpe.4364"},{"issue":"4","key":"787_CR16","doi-asserted-by":"publisher","first-page":"2289","DOI":"10.1109\/TSC.2020.3046471","volume":"15","author":"S Namasudra","year":"2020","unstructured":"Namasudra, S.: Fast and secure data accessing by using DNA computing for the cloud environment. IEEE Trans. Serv. Comput. 15(4), 2289\u20132300 (2020)","journal-title":"IEEE Trans. Serv. Comput."},{"key":"787_CR17","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.comcom.2019.12.041","volume":"151","author":"S Namasudra","year":"2020","unstructured":"Namasudra, S., Devi, D., Kadry, S., Sundarasekar, R., Shanthini, A.: Towards DNA based data security in the cloud computing environment. Comput. Commun. 151, 539\u2013547 (2020)","journal-title":"Comput. Commun."},{"key":"787_CR18","doi-asserted-by":"crossref","unstructured":"Balan, K., Abdulrazak, L.F., Khan, A.S., Julaihi, A.A., Tarmizi, S., Pillay, K.S., Sallehudin, H.: RSSI and public key infrastructure based secure communication in autonomous vehicular networks. Int. J. Adv. Comput. Sci. Appl. 9(12) (2018)","DOI":"10.14569\/IJACSA.2018.091243"},{"issue":"16","key":"787_CR19","doi-asserted-by":"publisher","first-page":"4372","DOI":"10.3390\/s20164372","volume":"20","author":"YN Soe","year":"2020","unstructured":"Soe, Y.N., Feng, Y., Santosa, P.I., Hartanto, R., Sakurai, K.: Machine learning-based IoT-botnet attack detection with sequential architecture. Sensors 20(16), 4372 (2020).","journal-title":"Sensors"},{"key":"787_CR20","doi-asserted-by":"publisher","first-page":"3255","DOI":"10.1007\/s10462-019-09762-z","volume":"53","author":"MR Gauthama Raman","year":"2020","unstructured":"Gauthama Raman, M.R., Somu, N., Jagarapu, S., Manghnani, T., Selvam, T., Krithivasan, K., Shankar Sriram, V.S.: An efficient intrusion detection technique based on support vector machine and improved binary gravitational search algorithm. Artif. Intell. Rev. 53, 3255\u20133286 (2020)","journal-title":"Artif. Intell. Rev."},{"issue":"5","key":"787_CR21","doi-asserted-by":"publisher","first-page":"754","DOI":"10.3390\/sym12050754","volume":"12","author":"IH Sarker","year":"2020","unstructured":"Sarker, I.H., Abushark, Y.B., Alsolami, F., Khan, A.I.: IntruDTree: a machine learning based cyber security intrusion detection model. Symmetry 12(5), 754 (2020). https:\/\/doi.org\/10.3390\/sym12050754","journal-title":"Symmetry"},{"key":"787_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2021.100393","volume":"14","author":"IH Sarker","year":"2021","unstructured":"Sarker, I.H.: CyberLearning: effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks. Internet Things 14, 100393 (2021)","journal-title":"Internet Things"},{"issue":"11","key":"787_CR23","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.3390\/electronics10111341","volume":"10","author":"A Alharbi","year":"2021","unstructured":"Alharbi, A., Alosaimi, W., Alyami, H., Rauf, H.T., Dama\u0161evi\u010dius, R.: Botnet attack detection using local global best bat algorithm for industrial internet of things. Electronics 10(11), 1341 (2021)","journal-title":"Electronics"},{"key":"787_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/4586875","volume":"2020","author":"S Rajagopal","year":"2020","unstructured":"Rajagopal, S., Kundapur, P.P., Hareesha, K.S.: A stacking ensemble for network intrusion detection using heterogeneous datasets. Secur. Commun. Netw. 2020, 1\u20139 (2020)","journal-title":"Secur. Commun. Netw."},{"key":"787_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2020.103814","volume":"82","author":"P Bedi","year":"2021","unstructured":"Bedi, P., Mewada, S., Vatti, R.A., Singh, C., Dhindsa, K.S., Ponnusamy, M., Sikarwar, R.: Detection of attacks in IoT sensors networks using machine learning algorithm. Microprocess. Microsyst. 82, 103814 (2021)","journal-title":"Microprocess. Microsyst."},{"key":"787_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2019.102031","volume":"101","author":"M Almiani","year":"2020","unstructured":"Almiani, M., AbuGhazleh, A., Al-Rahayfeh, A., Atiewi, S., Razaque, A.: Deep recurrent neural network for IoT intrusion detection system. Simul. Model. Pract. Theory 101, 102031 (2020)","journal-title":"Simul. Model. Pract. Theory"},{"key":"787_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102662","volume":"163","author":"GDLT Parra","year":"2020","unstructured":"Parra, G.D.L.T., Rad, P., Choo, K.K.R., Beebe, N.: Detecting Internet of Things attacks using distributed deep learning. J. Netw. Comput. Appl. 163, 102662 (2020)","journal-title":"J. Netw. Comput. Appl."},{"key":"787_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2021.100435","volume":"16","author":"A Alhowaide","year":"2021","unstructured":"Alhowaide, A., Alsmadi, I., Tang, J.: Ensemble detection model for IoT IDS. Internet Things 16, 100435 (2021)","journal-title":"Internet Things"},{"issue":"15","key":"787_CR29","doi-asserted-by":"publisher","first-page":"7050","DOI":"10.3390\/app11157050","volume":"11","author":"Z Ahmad","year":"2021","unstructured":"Ahmad, Z., Shahid Khan, A., Nisar, K., Haider, I., Hassan, R., Haque, M.R., Tarmizi, S., Rodrigues, J.J.: Anomaly detection using deep neural network for IoT architecture. Appl. Sci. 11(15), 7050 (2021)","journal-title":"Appl. Sci."},{"issue":"1","key":"787_CR30","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/jsan11010018","volume":"11","author":"Q Abu Al-Haija","year":"2022","unstructured":"Abu Al-Haija, Q., Al-Dala\u2019ien, M.A.: ELBA-IoT: an ensemble learning model for botnet attack detection in IoT networks. J. Sens. Actuator Netw. 11(1), 18 (2022)","journal-title":"J. Sens. Actuator Netw."},{"issue":"16","key":"787_CR31","doi-asserted-by":"publisher","first-page":"7721","DOI":"10.1007\/s00500-022-06750-4","volume":"26","author":"MY Alzahrani","year":"2022","unstructured":"Alzahrani, M.Y., Bamhdi, A.M.: Hybrid deep-learning model to detect botnet attacks over internet of things environments. Soft. Comput. 26(16), 7721\u20137735 (2022)","journal-title":"Soft. Comput."},{"key":"787_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108693","volume":"204","author":"V Rey","year":"2022","unstructured":"Rey, V., S\u00e1nchez, P.M.S., Celdr\u00e1n, A.H., Bovet, G.: Federated learning for malware detection in iot devices. Comput. Netw. 204, 108693 (2022)","journal-title":"Comput. Netw."},{"key":"787_CR33","doi-asserted-by":"publisher","first-page":"18042","DOI":"10.1109\/ACCESS.2017.2747560","volume":"5","author":"M Lopez-Martin","year":"2017","unstructured":"Lopez-Martin, M., Carro, B., Sanchez-Esguevillas, A., Lloret, J.: Network traffic classifier with convolutional and recurrent neural networks for Internet of Things. IEEE Access 5, 18042\u201318050 (2017)","journal-title":"IEEE Access"},{"issue":"1","key":"787_CR34","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1109\/TSUSC.2018.2809665","volume":"4","author":"A Azmoodeh","year":"2018","unstructured":"Azmoodeh, A., Dehghantanha, A., Choo, K.K.R.: Robust malware detection for internet of (battlefield) things devices using deep eigenspace learning. IEEE Trans. Sustain. Comput. 4(1), 88\u201395 (2018)","journal-title":"IEEE Trans. Sustain. Comput."},{"key":"787_CR35","doi-asserted-by":"publisher","first-page":"94497","DOI":"10.1109\/ACCESS.2019.2928048","volume":"7","author":"BA Tama","year":"2019","unstructured":"Tama, B.A., Comuzzi, M., Rhee, K.H.: TSE-IDS: a two-stage classifier ensemble for intelligent anomaly-based intrusion detection system. IEEE access 7, 94497\u201394507 (2019)","journal-title":"IEEE access"},{"key":"787_CR36","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s10586-020-03153-8","volume":"24","author":"AJ Siddiqui","year":"2021","unstructured":"Siddiqui, A.J., Boukerche, A.: TempoCode-IoT: temporal codebook-based encoding of flow features for intrusion detection in Internet of Things. Clust. Comput. 24, 17\u201335 (2021)","journal-title":"Clust. Comput."},{"issue":"1","key":"787_CR37","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s11277-022-09548-7","volume":"125","author":"Y Labiod","year":"2022","unstructured":"Labiod, Y., Amara Korba, A., Ghoualmi, N.: Fog computing-based intrusion detection architecture to protect iot networks. Wirel. Pers. Commun. 125(1), 231\u2013259 (2022)","journal-title":"Wirel. Pers. Commun."},{"key":"787_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.107450","volume":"154","author":"Y Li","year":"2020","unstructured":"Li, Y., Xu, Y., Liu, Z., Hou, H., Zheng, Y., Xin, Y., Zhao, Y., Cui, L.: Robust detection for network intrusion of industrial IoT based on multi-CNN fusion. Measurement 154, 107450 (2020)","journal-title":"Measurement"},{"issue":"1","key":"787_CR39","first-page":"47","volume":"10","author":"JA Alzubi","year":"2019","unstructured":"Alzubi, J.A., Manikandan, R., Alzubi, O.A., Gayathri, N., Patan, R.: A survey of specific IoT applications. Int. J. Emerging Technol. 10(1), 47\u201353 (2019)","journal-title":"Int. J. Emerging Technol."},{"key":"787_CR40","doi-asserted-by":"publisher","first-page":"8566","DOI":"10.1007\/s11227-020-03144-x","volume":"76","author":"OA Alzubi","year":"2020","unstructured":"Alzubi, O.A., Alzubi, J.A., Dorgham, O., Alsayyed, M.: Cryptosystem design based on Hermitian curves for IoT security. J. Supercomput. 76, 8566\u20138589 (2020)","journal-title":"J. Supercomput."},{"key":"787_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.future.2021.01.028","volume":"123","author":"M Gheisari","year":"2021","unstructured":"Gheisari, M., Najafabadi, H.E., Alzubi, J.A., Gao, J., Wang, G., Abbasi, A.A., Castiglione, A.: OBPP: an ontology-based framework for privacy-preserving in IoT-based smart city. Fut. Gen. Comput. Syst. 123, 1\u201313 (2021)","journal-title":"Fut. Gen. Comput. Syst."},{"key":"787_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.107077","volume":"150","author":"JA Alzubi","year":"2020","unstructured":"Alzubi, J.A., Manikandan, R., Alzubi, O.A., Qiqieh, I., Rahim, R., Gupta, D., Khanna, A.: Hashed Needham Schroeder industrial IoT based cost optimized deep secured data transmission in cloud. Measurement 150, 107077 (2020)","journal-title":"Measurement"},{"key":"787_CR43","doi-asserted-by":"crossref","unstructured":"Shaikh, S., Rupa, C., Srivastava, G., Gadekallu, T.R.: Botnet attack intrusion detection in IoT enabled automated guided vehicles. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 6332\u20136336. IEEE (2022)","DOI":"10.1109\/BigData55660.2022.10020355"},{"key":"787_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2023.104935","volume":"103","author":"TR Gadekallu","year":"2023","unstructured":"Gadekallu, T.R., Kumar, N., Baker, T., Natarajan, D., Boopathy, P., Maddikunta, P.K.R.: Moth flame optimization based ensemble classification for intrusion detection in intelligent transport system for smart cities. Microprocess. Microsyst. 103, 104935 (2023)","journal-title":"Microprocess. Microsyst."},{"issue":"1","key":"787_CR45","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1109\/TII.2022.3189170","volume":"19","author":"JA Alzubi","year":"2022","unstructured":"Alzubi, J.A., Alzubi, O.A., Singh, A., Ramachandran, M.: Cloud-IIoT-based electronic health record privacy-preserving by CNN and blockchain-enabled federated learning. IEEE Trans. Ind. Inf. 19(1), 1080\u20131087 (2022)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"787_CR46","doi-asserted-by":"publisher","first-page":"165130","DOI":"10.1109\/ACCESS.2020.3022862","volume":"8","author":"A Alsaedi","year":"2020","unstructured":"Alsaedi, A., Moustafa, N., Tari, Z., Mahmood, A., Anwar, A.: TON_IoT telemetry dataset: a new generation dataset of IoT and IIoT for data-driven intrusion detection systems. IEEE Access 8, 165130\u2013165150 (2020)","journal-title":"IEEE Access"},{"issue":"7","key":"787_CR47","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.3390\/electronics9071151","volume":"9","author":"W Jo","year":"2020","unstructured":"Jo, W., Kim, S., Lee, C., Shon, T.: Packet preprocessing in CNN-based network intrusion detection system. Electronics 9(7), 1151 (2020)","journal-title":"Electronics"},{"issue":"2","key":"787_CR48","doi-asserted-by":"publisher","first-page":"626","DOI":"10.3390\/s21020626","volume":"21","author":"R Yao","year":"2021","unstructured":"Yao, R., Wang, N., Liu, Z., Chen, P., Sheng, X.: Intrusion detection system in the advanced metering infrastructure: a cross-layer feature-fusion CNN-LSTM-based approach. Sensors 21(2), 626 (2021)","journal-title":"Sensors"},{"key":"787_CR49","doi-asserted-by":"publisher","unstructured":"Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), pp. 1\u20136 (2017). https:\/\/doi.org\/10.1109\/ICEngTechnol.2017.8308186","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"787_CR50","doi-asserted-by":"crossref","unstructured":"Mirsky, Y., Doitshman, T., Elovici, Y., Shabtai, A.: Kitsune: an ensemble of autoencoders for online network intrusion detection. In: Network and Distributed System Security (NDSS) Symposium, San Diego, CA, USA (2018)","DOI":"10.14722\/ndss.2018.23204"},{"issue":"1","key":"787_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0219-y","volume":"6","author":"IH Sarker","year":"2019","unstructured":"Sarker, I.H., Kayes, A.S.M., Watters, P.: Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage. J. Big Data 6(1), 1\u201328 (2019)","journal-title":"J. Big Data"},{"issue":"4","key":"787_CR52","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1016\/j.jksus.2018.03.018","volume":"31","author":"M Mazini","year":"2019","unstructured":"Mazini, M., Shirazi, B., Mahdavi, I.: Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms. J. King Saud Univer.-Comput. Inf. Sci. 31(4), 541\u2013553 (2019)","journal-title":"J. King Saud Univer.-Comput. Inf. Sci."},{"key":"787_CR53","doi-asserted-by":"crossref","unstructured":"Baby, R., Pooranian, Z., Shojafar, M., Tafazolli, R.: A heterogenous IoT attack detection through deep reinforcement learning: a dynamic ML approach. In: ICC 2023-IEEE International Conference on Communications, pp. 479\u2013484. IEEE (2023)","DOI":"10.1109\/ICC45041.2023.10278685"},{"key":"787_CR54","doi-asserted-by":"crossref","unstructured":"CU, O.K., Pranavi, D., Laxmi, B.A., Devasena, R.: Variational autoencoder for IoT botnet detection. In: Using Computational Intelligence for the Dark Web and Illicit Behavior Detection, pp. 74\u201388. IGI Global (2022)","DOI":"10.4018\/978-1-6684-6444-1.ch005"},{"key":"787_CR55","first-page":"1","volume":"2022","author":"U Shafiq","year":"2022","unstructured":"Shafiq, U., Shahzad, M.K., Anwar, M., Shaheen, Q., Shiraz, M., Gani, A.: Transfer learning auto-encoder neural networks for anomaly detection of DDoS generating IoT devices. Secur Commun Networks 2022, 1\u201313 (2022)","journal-title":"Secur Commun Networks"},{"key":"787_CR56","doi-asserted-by":"crossref","unstructured":"Cunha, A.A., Borges, J.B., Loureiro, A.A.F.: Classification of botnet attacks in IoT using a convolutional neural network. In: Proceedings of the 18th ACM International Symposium on QoS and Security for Wireless and Mobile Networks, pp. 63\u201370 (2022)","DOI":"10.1145\/3551661.3561374"},{"key":"787_CR57","doi-asserted-by":"crossref","unstructured":"Hezam, A.A., Mostafa, S.A., Ramli, A.A., Mahdin, H., Khalaf, B.A.: Deep learning approach for detecting botnet attacks in IoT environment of multiple and heterogeneous sensors. In: Advances in Cyber Security: Third International Conference, ACeS 2021, Penang, Malaysia, August 24\u201325, 2021, Revised Selected Papers 3, pp. 317-328. Springer, Singapore (2021)","DOI":"10.1007\/978-981-16-8059-5_19"},{"key":"787_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/3806459","volume":"2021","author":"H Alkahtani","year":"2021","unstructured":"Alkahtani, H., Aldhyani, T.H.: Botnet attack detection by using CNN-LSTM model for Internet of Things applications. Secur. Commun. Netw. 2021, 1\u201323 (2021)","journal-title":"Secur. Commun. Netw."},{"key":"787_CR59","doi-asserted-by":"publisher","first-page":"2952","DOI":"10.1109\/TNSE.2022.3168533","volume":"10","author":"T Hasan","year":"2022","unstructured":"Hasan, T., Malik, J., Bibi, I., Khan, W.U., Al-Wesabi, F.N., Dev, K., Huang, G.: Securing industrial internet of things against botnet attacks using hybrid deep learning approach. IEEE Trans. Netw. Sci. Eng. 10, 2952\u20132963 (2022)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"5","key":"787_CR60","doi-asserted-by":"publisher","first-page":"1159","DOI":"10.3390\/electronics12051159","volume":"12","author":"MA Haq","year":"2023","unstructured":"Haq, M.A.: DBoTPM: a deep neural network-based botnet prediction model. Electronics 12(5), 1159 (2023)","journal-title":"Electronics"}],"container-title":["International Journal of Information Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-023-00787-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10207-023-00787-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-023-00787-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T07:41:22Z","timestamp":1711525282000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10207-023-00787-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,14]]},"references-count":60,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["787"],"URL":"https:\/\/doi.org\/10.1007\/s10207-023-00787-8","relation":{},"ISSN":["1615-5262","1615-5270"],"issn-type":[{"value":"1615-5262","type":"print"},{"value":"1615-5270","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,14]]},"assertion":[{"value":"9 November 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2023","order":2,"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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Dataset is publicly available on the repository.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research data policy and data availability statements"}}]}}