Accurate threat hunting in industrial internet of things edge devices

Yazdinejad, 2022, Block hunter: federated learning for cyber threat hunting in blockchain-based iiot networks, IEEE Trans. Ind. Inf., 10.1109/TII.2022.3168011 Gaddam, 2020, Detecting sensor faults, anomalies and outliers in the internet of things: a survey on the challenges and solutions, Electronics, 9, 511, 10.3390/electronics9030511 Yazdinejad, 2019, Energy efficient decentralized authentication in internet of underwater things using blockchain, 1 Al-Abassi, 2020, An ensemble deep learning-based cyber-attack detection in industrial control system, IEEE Access, 8, 83965, 10.1109/ACCESS.2020.2992249 Yazdinejad, 2020, Decentralized authentication of distributed patients in hospital networks using blockchain, IEEE j. biomed. health inf., 24, 2146, 10.1109/JBHI.2020.2969648 Dou, 2015, Optimizing sensor network coverage and regional connectivity in industrial iot systems, IEEE Syst. J., 11, 1351, 10.1109/JSYST.2015.2443045 Zhang, 2019, Multilayer data-driven cyber-attack detection system for industrial control systems based on network, system, and process data, IEEE Trans. Ind. Inf., 15, 4362, 10.1109/TII.2019.2891261 Diro, 2018, Distributed attack detection scheme using deep learning approach for internet of things, Future Generat. Comput. Syst., 82, 761, 10.1016/j.future.2017.08.043 Yazdinejad, 2021, Federated learning for drone authentication, Ad Hoc Netw., 120, 10.1016/j.adhoc.2021.102574 Darabian, 2020, A multiview learning method for malware threat hunting: windows, iot and android as case studies, World Wide Web, 23, 1241, 10.1007/s11280-019-00755-0 Bhardwaj, 2019, A framework for effective threat hunting, Netw. Secur. 2019, 15 Latif, 2020, A novel attack detection scheme for the industrial internet of things using a lightweight random neural network, IEEE Access, 8, 89337, 10.1109/ACCESS.2020.2994079 Dovom, 2019, Fuzzy pattern tree for edge malware detection and categorization in iot, J. Syst. Architect., 97, 1, 10.1016/j.sysarc.2019.01.017 Munir, 2014, Detecting sensor failure using electrical appliances in the home, 73 Jun, 2017, A bayesian network-based approach for fault analysis, Expert Syst. Appl., 81, 332, 10.1016/j.eswa.2017.03.056 Géron, 2019 Cai, 2021, A sharding scheme based many-objective optimization algorithm for enhancing security in blockchain-enabled industrial internet of things, IEEE Trans. Ind. Inf., 17, 7650, 10.1109/TII.2021.3051607 Hassaballah, 2021, A novel image steganography method for industrial internet of things security, IEEE Trans. Ind. Inf., 17, 7743, 10.1109/TII.2021.3053595 Mouratidis, 2018, A security analysis method for industrial internet of things, IEEE Trans. Ind. Inf., 14, 4093, 10.1109/TII.2018.2832853 Deng, 2022, Learning-based joint uav trajectory and power allocation optimization for secure iot networks, Digit. Commun. Netw., 8, 415, 10.1016/j.dcan.2021.07.007 Sfar, 2018, A roadmap for security challenges in the internet of things, Digit. Commun. Netw., 4, 118, 10.1016/j.dcan.2017.04.003 Han, 2021, Anomaly detection based on multidimensional data processing for protecting vital devices in 6g enabled massive iiot, IEEE Internet Things J., 8, 5219, 10.1109/JIOT.2021.3051935 Yazdinejadna, 2021, A kangaroo-based intrusion detection system on software-defined networks, Comput. Network., 184, 1, 10.1016/j.comnet.2020.107688 Sha, 2020, A survey of edge computing-based designs for iot security, Digit. Commun. Netw., 6, 195, 10.1016/j.dcan.2019.08.006 Dibaei, 2020, Attacks and defences on intelligent connected vehicles: a survey, Digit. Commun. Netw., 6, 399, 10.1016/j.dcan.2020.04.007 Yazdinejad, 2020, Cryptocurrency malware hunting: a deep recurrent neural network approach, Appl. Soft Comput., 96, 1, 10.1016/j.asoc.2020.106630 Karimipour, 2019, A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids, IEEE Access, 7, 80778, 10.1109/ACCESS.2019.2920326 Saharkhizan, 2020, An ensemble of deep recurrent neural networks for detecting iot cyber attacks using network traffic, IEEE Internet Things J., 7, 8852, 10.1109/JIOT.2020.2996425 Zhang, 2019, Ann-based outlier detection for wireless sensor networks in smart buildings, IEEE Access, 7, 95987, 10.1109/ACCESS.2019.2929550 Hasan, 2019, Attack and anomaly detection in iot sensors in iot sites using machine learning approaches, Internet. Things, 7, 1, 10.1016/j.iot.2019.100059 Pacheco, 2019, Anomaly behavior analysis for iot network nodes Muna, 2018, Identification of malicious activities in industrial internet of things based on deep learning models, J. Inf. Secur. Appl., 41, 1 Gu, 2020, Towards learning-automation iot attack detection through reinforcement learning Wu, 2021, Multi buffers multi objects optimal matching scheme for edge devices in iiot, IEEE Internet Things J., 8, 11514, 10.1109/JIOT.2021.3053017 Ammar, 2018, Internet of things: a survey on the security of iot frameworks, J. Inf. Secur. Appl., 38, 8 Eskandari, 2020, Passban ids: an intelligent anomaly-based intrusion detection system for iot edge devices, IEEE Internet Things J., 7, 6882, 10.1109/JIOT.2020.2970501 Géron, 2019 Freund, 1997, A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. Syst. Sci., 55, 119, 10.1006/jcss.1997.1504 Pahl, 2018, All eyes on you: distributed multi-dimensional iot microservice anomaly detection Pahl, 2016, Distributed smart space orchestration van Ravenzwaaij, 2019, True and false positive rates for different criteria of evaluating statistical evidence from clinical trials, BMC Med. Res. Methodol., 19, 1, 10.1186/s12874-019-0865-y Farahnakian, 2018, A deep auto-encoder based approach for intrusion detection system, 178 Khalvati, 2018, Intrusion detection based on a novel hybrid learning approach, J. AI and data min., 6, 157 Shone, 2018, A deep learning approach to network intrusion detection, IEEE trans. emerg. topic. comput. intel., 2, 41, 10.1109/TETCI.2017.2772792 Latif, 2020, A novel attack detection scheme for the industrial internet of things using a lightweight random neural network, IEEE Access, 8, 89337, 10.1109/ACCESS.2020.2994079