References |
: |
[1]Hussain A, Sharif H, Rehman F, Kirn H, Sadiq A, Khan MS, et al. A systematic review of intrusion detection systems in internet of things using ML and DL. In 4th international conference on computing, mathematics and engineering technologies (iCoMET) 2023 (pp. 1-5). IEEE.
|
[Crossref] |
[Google Scholar] |
[2]Bu T, Huang Z, Zhang K, Wang Y, Song H, Zhou J, et al. Task scheduling in the internet of things: challenges, solutions, and future trends. Cluster Computing. 2023:1-30.
|
[Crossref] |
[Google Scholar] |
[3]Lu Y, Da XL. Internet of things (IoT) cybersecurity research: a review of current research topics. IEEE Internet of Things Journal. 2018; 6(2):2103-15.
|
[Crossref] |
[Google Scholar] |
[4]https://www.cisco.com/c/en/us/solutions/executive-perspectives/annual-internet-report/airhighlights.html. Accessed: 17 March 2022.
|
[5]Jose J, Jose DV. The internet of things architectures and use cases. In enterprise digital transformation 2022 (pp. 101-25). Auerbach Publications.
|
[Google Scholar] |
[6]Lohiya R, Thakkar A. Application domains, evaluation data sets, and research challenges of IoT: a systematic review. IEEE Internet of Things Journal. 2020; 8(11):8774-98.
|
[Crossref] |
[Google Scholar] |
[7]Kaur B, Dadkhah S, Shoeleh F, Neto EC, Xiong P, Iqbal S, et al. Internet of things (IoT) security dataset evolution: challenges and future directions. Internet of Things. 2023:100780.
|
[Crossref] |
[Google Scholar] |
[8]Aljanabi M, Ismail MA, Ali AH. Intrusion detection systems, issues, challenges, and needs. International Journal of Computational Intelligence Systems. 2021; 14(1):560-71.
|
[Crossref] |
[Google Scholar] |
[9]Khraisat A, Alazab A. A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges. Cybersecurity. 2021; 4:1-27.
|
[Crossref] |
[Google Scholar] |
[10]Thakkar A, Lohiya R. A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions. Artificial Intelligence Review. 2022; 55(1):453-563.
|
[Crossref] |
[Google Scholar] |
[11]Malhotra P, Singh Y, Anand P, Bangotra DK, Singh PK, Hong WC. Internet of things: evolution, concerns and security challenges. Sensors. 2021; 21(5):1-33.
|
[Crossref] |
[Google Scholar] |
[12]Hanif S, Ilyas T, Zeeshan M. Intrusion detection in IoT using artificial neural networks on UNSW-15 dataset. In 16th international conference on smart cities: improving quality of life using ICT & IoT and AI 2019 (pp. 152-6). IEEE.
|
[Crossref] |
[Google Scholar] |
[13]Mohamed E. The relation of artificial intelligence with internet of things: a survey. Journal of Cybersecurity and Information Management. 2020; 1(1):30-4.
|
[Crossref] |
[Google Scholar] |
[14]Kuzlu M, Fair C, Guler O. Role of artificial intelligence in the internet of things (IoT) cybersecurity. Discover Internet of Things. 2021; 1:1-4.
|
[Crossref] |
[Google Scholar] |
[15]Awotunde JB, Misra S. Feature extraction and artificial intelligence-based intrusion detection model for a secure internet of things networks. In illumination of artificial intelligence in cybersecurity and forensics 2022 (pp. 21-44). Cham: Springer International Publishing.
|
[Crossref] |
[Google Scholar] |
[16]Al-garadi MA, Mohamed A, Al-ali AK, Du X, Ali I, Guizani M. A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Communications Surveys & Tutorials. 2020; 22(3):1646-85.
|
[Crossref] |
[Google Scholar] |
[17]Anushiya R, Lavanya VS. A comparative study on intrusion detection systems for secured communication in internet of things. ICTACT Journal on Communication Technology. 2021; 6948:2527-37.
|
[Crossref] |
[Google Scholar] |
[18]Baich M, Hamim T, Sael N, Chemlal Y. Machine learning for IoT based networks intrusion detection: a comparative study. Procedia Computer Science. 2022; 215:742-51.
|
[Crossref] |
[Google Scholar] |
[19]Tsimenidis S, Lagkas T, Rantos K. Deep learning in IoT intrusion detection. Journal of Network and Systems Management. 2022; 30:1-40.
|
[Crossref] |
[Google Scholar] |
[20]Bostani H, Sheikhan M. Hybrid of anomaly-based and specification-based IDS for internet of things using unsupervised OPF based on MapReduce approach. Computer Communications. 2017; 98:52-71.
|
[Crossref] |
[Google Scholar] |
[21]Kumari VV, Varma PR. A semi-supervised intrusion detection system using active learning SVM and fuzzy c-means clustering. In international conference on I-SMAC (IoT in social, mobile, analytics and cloud) 2017 (pp. 481-5). IEEE.
|
[Crossref] |
[Google Scholar] |
[22]Bhatt P, Morais A. HADS: hybrid anomaly detection system for IoT environments. In international conference on internet of things, embedded systems and communications 2018 (pp. 191-6). IEEE.
|
[Crossref] |
[Google Scholar] |
[23]Ioulianou P, Vasilakis V, Moscholios I, Logothetis M. A signature-based intrusion detection system for the internet of things. Information and Communication Technology Form. 2018:1-7.
|
[Google Scholar] |
[24]Roopak M, Tian GY, Chambers J. Deep learning models for cyber security in IoT networks. In 9th annual computing and communication workshop and conference 2019 (pp. 452-7). IEEE.
|
[Crossref] |
[Google Scholar] |
[25]Zhang Y, Li P, Wang X. Intrusion detection for IoT based on improved genetic algorithm and deep belief network. IEEE Access. 2019; 7:31711-22.
|
[Crossref] |
[Google Scholar] |
[26]Khan MA, Karim MR, Kim Y. A scalable and hybrid intrusion detection system based on the convolutional-LSTM network. Symmetry. 2019; 11(4):1-14.
|
[Crossref] |
[Google Scholar] |
[27]Khraisat A, Gondal I, Vamplew P, Kamruzzaman J, Alazab A. A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks. Electronics. 2019; 8(11):1-18.
|
[Crossref] |
[Google Scholar] |
[28]Bovenzi G, Aceto G, Ciuonzo D, Persico V, Pescapé A. A hierarchical hybrid intrusion detection approach in IoT scenarios. In GLOBECOM global communications conference 2020 (pp. 1-7). IEEE.
|
[Crossref] |
[Google Scholar] |
[29]Ramadan RA, Yadav K. A novel hybrid intrusion detection system (IDS) for the detection of internet of things (IoT) network attacks. Annals of Emerging Technologies in Computing (AETiC). 2020; 4(5):61-74.
|
[Crossref] |
[Google Scholar] |
[30]Smys S, Basar A, Wang H. Hybrid intrusion detection system for internet of things (IoT). Journal of ISMAC. 2020; 2(4):190-9.
|
[Crossref] |
[Google Scholar] |
[31]Ullah I, Ullah A, Sajjad M. Towards a hybrid deep learning model for anomalous activities detection in internet of things networks. IoT. 2021; 2(3):428-48.
|
[Crossref] |
[Google Scholar] |
[32]Huma ZE, Latif S, Ahmad J, Idrees Z, Ibrar A, Zou Z, et al. A hybrid deep random neural network for cyberattack detection in the industrial internet of things. IEEE Access. 2021; 9:55595-605.
|
[Crossref] |
[Google Scholar] |
[33]Sahu AK, Sharma S, Tanveer M, Raja R. Internet of things attack detection using hybrid deep learning model. Computer Communications. 2021; 176:146-54.
|
[Crossref] |
[Google Scholar] |
[34]Otoum Y, Nayak A. As-ids: anomaly and signature based ids for the internet of things. Journal of Network and Systems Management. 2021; 29:1-26.
|
[Crossref] |
[Google Scholar] |
[35]Ravi V, Chaganti R, Alazab M. Recurrent deep learning-based feature fusion ensemble meta-classifier approach for intelligent network intrusion detection system. Computers and Electrical Engineering. 2022; 102:108156.
|
[Crossref] |
[Google Scholar] |
[36]Mahmoud M, Kasem M, Abdallah A, Kang HS. Ae-LSTM: autoencoder with LSTM-based intrusion detection in IoT. In international telecommunications conference 2022 (pp. 1-6). IEEE.
|
[Crossref] |
[Google Scholar] |
[37]Mushtaq E, Zameer A, Umer M, Abbasi AA. A two-stage intrusion detection system with auto-encoder and LSTMs. Applied Soft Computing. 2022; 121:108768.
|
[Crossref] |
[Google Scholar] |
[38]Issa AS, Albayrak Z. Ddos attack intrusion detection system based on hybridization of CNN and LSTM. Acta Polytechnica Hungarica. 2023; 20(2):1-9.
|
[Google Scholar] |
[39]Altunay HC, Albayrak Z. A hybrid CNN+LSTM based intrusion detection system for industrial IoT networks. Engineering Science and Technology, an International Journal. 2023; 38:101322.
|
[Crossref] |
[Google Scholar] |
[40]Calik BE, Koray SO, Dogan B. Deep learning based malware detection for android systems: a comparative analysis. Tehnički Vjesnik. 2023; 30(3):787-96.
|
[Crossref] |
[Google Scholar] |
[41]Khan FA, Gumaei A, Derhab A, Hussain A. A novel two-stage deep learning model for efficient network intrusion detection. IEEE Access. 2019; 7:30373-85.
|
[Crossref] |
[Google Scholar] |
[42]Wang YC, Houng YC, Chen HX, Tseng SM. Network anomaly intrusion detection based on deep learning approach. Sensors. 2023; 23(4):1-21.
|
[Crossref] |
[Google Scholar] |
[43]Xu J, He Z, Zhang Y. CNN-LSTM combined network for IoT enabled fall detection applications. In journal of physics: conference series 2019 (pp. 1-6). IOP Publishing.
|
[Crossref] |
[Google Scholar] |
[44]Praanna K, Sruthi S, Kalyani K, Tejaswi AS. A CNN-LSTM model for intrusion detection system from high dimensional data. Journal of Information and Computational Science. 2020; 10(3):1362-70.
|
[Google Scholar] |
[45]Alferaidi A, Yadav K, Alharbi Y, Razmjooy N, Viriyasitavat W, Gulati K, et al. Distributed deep CNN-LSTM model for intrusion detection method in IoT-based vehicles. Mathematical Problems in Engineering. 2022; 2022:1-8.
|
[Crossref] |
[Google Scholar] |
[46]Alkahtani H, Aldhyani TH. Botnet attack detection by using CNN-LSTM model for internet of things applications. Security and Communication Networks. 2021; 2021:1-23.
|
[Crossref] |
[Google Scholar] |
[47]https://www.unb.ca/cic/datasets/ids-2018.html. Accessed 28 February 2020.
|
[48]Khan MA. HCRNNIDS: hybrid convolutional recurrent neural network-based network intrusion detection system. Processes. 2021; 9(5):1-14.
|
[Crossref] |
[Google Scholar] |
[49]https://ieee-dataport.org/open-access/iot-network-intrusion-dataset. Accessed 16 November 2020.
|
[50]https://ieee-dataport.org/open-access/mqtt-iot-ids2020-mqtt- internet-things-intrusion-detection-dataset. Accessed 16 November 2020.
|
[51]https://research.unsw.edu.au/projects/bot-iot-dataset. Accessed 21 March 2021.
|
[52]Alhowaide A, Alsmadi I, Tang J. Towards the design of real-time autonomous IoT NIDS. Cluster Computing. 2021:1-4.
|
[Crossref] |
[Google Scholar] |
[53]Vujović Ž. Classification model evaluation metrics. International Journal of Advanced Computer Science and Applications. 2021; 12(6):599-606.
|
[Crossref] |
[Google Scholar] |
[54]Kim A, Park M, Lee DH. AI-IDS: application of deep learning to real-time web intrusion detection. IEEE Access. 2020; 8:70245-61.
|
[Crossref] |
[Google Scholar] |
[55]Zhang X, Zhou Y, Pei S, Zhuge J, Chen J. Adversarial examples detection for XSS attacks based on generative adversarial networks. IEEE Access. 2020; 8:10989-96.
|
[Crossref] |
[Google Scholar] |
[56]Sun P, Liu P, Li Q, Liu C, Lu X, Hao R, et al. DL-IDS: extracting features using CNN-LSTM hybrid network for intrusion detection system. Security and Communication Networks. 2020; 2020:1-11.
|
[Crossref] |
[Google Scholar] |
[57]Binbusayyis A, Vaiyapuri T. Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM. Applied Intelligence. 2021; 51(10):7094-108.
|
[Crossref] |
[Google Scholar] |
[58]Wu Z, Zhang H, Wang P, Sun Z. RTIDS: a robust transformer-based approach for intrusion detection system. IEEE Access. 2022; 10:64375-87.
|
[Crossref] |
[Google Scholar] |
[59]Umair MB, Iqbal Z, Faraz MA, Khan MA, Zhang YD, Razmjooy N, et al. A network intrusion detection system using hybrid multilayer deep learning model. Big Data. 2022.
|
[Crossref] |
[Google Scholar] |
|