The DOI matches its Crossref record; 297 of the 297 references it cites were found in a registry or an index.
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Open accessAbstract
The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Al-Garadi et al. [32] and Pakalapati et al. [33] provide strategic assessments of using AI/ML for threat detection and automated decision-making within DevSecOps pipelines, signalling the start of a broader movement toward intelligent, data-driven security automation.”
From Evolution of DevSecOps and Its Influence on Application Security: A Systematic Literature Review (2025) · cites this paper as [32]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Among the potential surveyed applications, ML has been used mainly for intrusion detection, while DL for malware and anomalies detection.”
From Deepfakes: current and future trends (2024) · cites this paper as Al-Garadi et al. 2020
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The review is primarily focused on IoT environments and does not directly address the integration of ML/DL into DevSecOps pipelines, which may limit its relevance for general DevSecOps practices.”
From Evolution of DevSecOps and Its Influence on Application Security: A Systematic Literature Review (2025) · cites this paper as [32]
The cited source says what the claim attributes to it.
In the citing paper
“As main conclusions, the authors claim the need for diverse datasets within IoT security.”
From Deepfakes: current and future trends (2024) · cites this paper as Al-Garadi et al. 2020
The cited source says what the claim attributes to it.
In the citing paper
“Several security aspects shall be considered in every IoT system (Al-Garadi et al. 2020):”
From Deepfakes: current and future trends (2024) · cites this paper as Al-Garadi et al. 2020
The cited source says what the claim attributes to it.
In the citing paper
“For all of these explanations, IoT systems need to go from simply enabling secure communication between devices to intelligence provided by DL approaches to create robust, all-encompassing security solutions, as noted by the al2020survey.”
From Deepfakes: current and future trends (2024) · cites this paper as Al-Garadi et al. 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
1 retracted
Deng, L., Li, D., Yao, X., & Wang, H. (2018) · Cluster Computing · doi:10.1007/s10586-018-1847-2 · Cited by 124
RetractedBreiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
MapReduce: simplified data processing on large clusters
Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,548
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
The Internet of Things: A survey
Atzori, L., Iera, A., & Morabito, G. (2010) · Computer Networks · doi:10.1016/j.comnet.2010.05.010 · Cited by 15,613
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Algorithm AS 136: A K-Means Clustering Algorithm
Hartigan, J. A., & Wong, M. A. (1979) · Applied Statistics · doi:10.2307/2346830 · Cited by 14,523
Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002) · IEEE Communications Magazine · doi:10.1109/mcom.2002.1024422 · Cited by 13,715
Internet of Things (IoT): A vision, architectural elements, and future directions
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013) · Future Generation Computer Systems · doi:10.1016/j.future.2013.01.010 · Cited by 12,293
Wold, S., Esbensen, K. H., & Geladi, P. L. M. (1987) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(87)80084-9 · Cited by 11,792
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359
Data clustering: 50 years beyond K-means
Jain, A. K. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2009.09.011 · Cited by 10,021
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al. (2010) · Communications of the ACM · doi:10.1145/1721654.1721672 · Cited by 9,464
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Self-citationInternet of Things: A Survey on Enabling Technologies, Protocols, and Applications
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2444095 · Cited by 8,630
Self-citationLearning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,316
The use of the area under the ROC curve in the evaluation of machine learning algorithms
Bradley, A. P. (1997) · Pattern Recognition · doi:10.1016/s0031-3203(96)00142-2 · Cited by 7,217
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965
FCM: The fuzzy c-means clustering algorithm
Bezdek, J. C., Ehrlich, R., & Full, W. E. (1984) · Computers & Geosciences · doi:10.1016/0098-3004(84)90020-7 · Cited by 6,786
Above the Clouds: A Berkeley View of Cloud Computing
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R. H., Konwinski, A., Lee, G., Patterson, D. A., Rabkin, A., Stoica, I. G., et al. (2009) · Cited by 5,838
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,049
Random forests for classification in ecology
Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007) · Ecology · doi:10.1890/07-0539.1 · Cited by 4,950
Membership Inference Attacks Against Machine Learning Models
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754
Combining Pattern Classifiers: Methods and Algorithms
Bagui, S. (2005) · Technometrics · doi:10.1198/tech.2005.s320 · Cited by 4,364
On the difficulty of training recurrent neural networks
Pascanu, R., Mikolov, T., & Bengio, Y. (2012) · arXiv · doi:10.48550/arxiv.1211.5063 · Cited by 4,310
Supervised Machine Learning: A Review of Classification Techniques
Kotsiantis, S. B. (2007) · Informatica · Cited by 4,192
Bayesian Inference in Statistical Analysis.
Kadane, J. B., Box, G. E. P., & Tiao, G. C. (1975) · Journal of the American Statistical Association · doi:10.2307/2285412 · Cited by 3,853
Semi-supervised learning using Gaussian fields and harmonic functions
Zhu, X., Zoubin Ghahramani, A., & Lafferty, J. (2003) · Cited by 3,509
Practical Black-Box Attacks against Machine Learning
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., & Swami, A. (2017) · Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security · doi:10.1145/3052973.3053009 · Cited by 3,495
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332
A few useful things to know about machine learning
Domingos, P. (2012) · Communications of the ACM · doi:10.1145/2347736.2347755 · Cited by 3,326
Deep Reinforcement Learning with Double Q-Learning
Van Hasselt, H., Guez, A., & Silver, D. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10295 · Cited by 3,318
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.1078 · Cited by 3,317
A survey of deep neural network architectures and their applications
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239
The Internet of Things for Health Care: A Comprehensive Survey
Islam, S. M. R., Kwak, D., Kabir, M. H., Hossain, M., & Kwak, K.-S. (2015) · IEEE Access · doi:10.1109/access.2015.2437951 · Cited by 3,199
Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2015) · arXiv · doi:10.48550/arxiv.1511.05952 · Cited by 3,120
Kuncheva, L. I. (2004) · Wiley eBooks · doi:10.1002/0471660264 · Cited by 3,007
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961
Context Aware Computing for The Internet of Things: A Survey
Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.042313.00197 · Cited by 2,861 · cited 14×
“For instance, k-means clustering was used for securing WSNs by detecting intrusions [174].”
BackgroundInfluentialA Practical Guide to Training Restricted Boltzmann Machines
Hinton, G. E. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_32 · Cited by 2,852
A survey on security issues in service delivery models of cloud computing
Subashini, S., & Kavitha, V. (2010) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2010.07.006 · Cited by 2,692
The Internet of Things: How the Next Evolution of the Internet Is Changing Everything
Evans, D. (2011) · Cited by 2,687
Decentralizing Privacy: Using Blockchain to Protect Personal Data
Zyskind, G., Nathan, O., & Pentland, A. '. (2015) · 2015 IEEE Security and Privacy Workshops · doi:10.1109/spw.2015.27 · Cited by 2,617
Deep learning applications and challenges in big data analytics
Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015) · Journal of Big Data · doi:10.1186/s40537-014-0007-7 · Cited by 2,594
The Effectiveness of Data Augmentation in Image Classification using Deep Learning
Perez, L., & Wang, J. (2017) · arXiv · doi:10.48550/arxiv.1712.04621 · Cited by 2,499
Deep Learning Based Recommender System
Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019) · ACM Computing Surveys · doi:10.1145/3285029 · Cited by 2,414
Integration of Cloud computing and Internet of Things: A survey
Alessio Botta, B., de Donato, W., Persico, V., & Pescapè, A. (2015) · Future Generation Computer Systems · doi:10.1016/j.future.2015.09.021 · Cited by 2,395
Privacy-preserving deep learning
Shokri, R., & Shmatikov, V. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813687 · Cited by 2,356
Security, privacy and trust in Internet of Things: The road ahead
Sicari, S., Rizzardi, A., Grieco, L. A., & Coen‐Porisini, A. (2014) · Computer Networks · doi:10.1016/j.comnet.2014.11.008 · Cited by 2,341
Zhang, C., & Ma, Y. (2012) · Springer eBooks · doi:10.1007/978-1-4419-9326-7 · Cited by 2,314
Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art
Zhang, L., Zhang, L., & Du, B. (2016) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2016.2540798 · Cited by 2,272
Theano: A Python framework for fast computation of mathematical expressionsPreprint
The Theano Development Team, Al‐Rfou, R., Alain, G., Almahairi, A., Angermueller, C., Bahdanau, D., Ballas, N., Bastien, F., Bayer, J., Belikov, A., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.02688 · Cited by 2,176
cuDNN: Efficient Primitives for Deep LearningPreprint
Chetlur, S., Woolley, C., Vandermersch, P., Cohen, J., Tran, J. J., Catanzaro, B., & Shelhamer, E. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.0759 · Cited by 2,058
(2001) · Cited by 1,932
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,908
DDoS in the IoT: Mirai and Other Botnets
Kolias, C., Kambourakis, G., Stavrou, A., & Voas, J. (2017) · Computer · doi:10.1109/mc.2017.201 · Cited by 1,892
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., & Zhang, Z. (2015) · arXiv · doi:10.48550/arxiv.1512.01274 · Cited by 1,819
Internet of Things: Architectures, Protocols, and Applications
Sethi, P., & Sarangi, S. R. (2017) · Journal of Electrical and Computer Engineering · doi:10.1155/2017/9324035 · Cited by 1,777
Decision trees: a recent overview
Kotsiantis, S. B. (2011) · Artificial Intelligence Review · doi:10.1007/s10462-011-9272-4 · Cited by 1,660
Static Analysis of Shape in TensorFlow ProgramsPreprint
The Theano Development Team, Al‐Rfou, R., Alain, G., et al. (2016) · arXiv (Cornell University) · doi:10.4230/lipics.ecoop.2020.15 · Cited by 1,629
Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition
Scherer, D., Müller, A., & Behnke, S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15825-4_10 · Cited by 1,603
Karlof, C., Sastry, N., & Wagner, D. A. (2004) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/1031495.1031515 · Cited by 1,600
The Internet of Things—A survey of topics and trends
Whitmore, A., Agarwal, A., & Da Xu, L. (2015) · Information Systems Frontiers · doi:10.1007/s10796-014-9489-2 · Cited by 1,568
Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing
Li, H., Ota, K., & Dong, M. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700202 · Cited by 1,566
Internet of Things – New security and privacy challenges
Weber, R. H. (2010) · Computer law & security review · doi:10.1016/j.clsr.2009.11.008 · Cited by 1,501
On the features and challenges of security and privacy in distributed internet of things
Román, R., Zhou, J., & López, J. (2013) · Computer Networks · doi:10.1016/j.comnet.2012.12.018 · Cited by 1,471
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2844341 · Cited by 1,471
Self-citationThe elements of statistical learning: Data mining, inference, and prediction
Franklin, J. (2005) · The Mathematical Intelligencer · doi:10.1007/bf02985802 · Cited by 1,400
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Hitaj, B., Ateniese, G., & Perez-Cruz, F. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134012 · Cited by 1,377
Network Anomaly Detection: Methods, Systems and Tools
Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.052213.00046 · Cited by 1,358
Internet of Things security: A survey
Alaba, F. A., Othman, M., Hashem, M., & Alotaibi, F. A. (2017) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.04.002 · Cited by 1,348
A Survey on Smart Grid Communication Infrastructures: Motivations, Requirements and Challenges
Ye, Y., Qian, Y., Sharif, H., & Tipper, D. (2012) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.021312.00034 · Cited by 1,322
Security of the Internet of Things: perspectives and challenges
Jing, Q., Vasilakos, A. V., Wan, J., Lu, J., & Qiu, D. (2014) · Wireless Networks · doi:10.1007/s11276-014-0761-7 · Cited by 1,284
A multidimensional unfolding method based on Bayes' theorem
D’Agostini, G. (1995) · Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · doi:10.1016/0168-9002(95)00274-x · Cited by 1,272
Big data deep learning: Challenges and perspectives
Chen, X.-W., & Lin, X. (2014) · IEEE Access · doi:10.1109/access.2014.2325029 · Cited by 1,255
Torch7: A Matlab-like Environment for Machine Learning
Collobert, R., Kavukcuoglu, K., & Farabet, C. (2011) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 1,241
Shi, W., & Dustdar, S. (2016) · Computer · doi:10.1109/mc.2016.145 · Cited by 1,241
(2011) · doi:10.5591/978-1-57735-516-8/ijcai11-210 · Cited by 1,229
Machine Learning and Deep Learning Methods for Cybersecurity
Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., & Wang, C. (2018) · IEEE Access · doi:10.1109/access.2018.2836950 · Cited by 1,228
A survey on deep learning for big data
Zhang, Q., Yang, L. T., Chen, Z., & Li, P. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.10.006 · Cited by 1,226
A survey on trust management for Internet of Things
Yan, Z., Zhang, P., & Vasilakos, A. V. (2014) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2014.01.014 · Cited by 1,155
Secrets and lies: digital security in a networked world
Schneier, B. (2001) · Choice Reviews Online · doi:10.5860/choice.38-3941 · Cited by 1,155
Security for the Internet of Things: A Survey of Existing Protocols and Open Research Issues
Granjal, J., Monteiro, E., & Silva, J. S. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2388550 · Cited by 1,133
Distributed attack detection scheme using deep learning approach for Internet of Things
Diro, A. A., & Chilamkurti, N. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.08.043 · Cited by 1,129
Secure integration of IoT and Cloud Computing
Stergiou, C. L., Psannis, K. E., Kim, B., & Gupta, B. B. (2016) · Future Generation Computer Systems · doi:10.1016/j.future.2016.11.031 · Cited by 1,114
Middleware for Internet of Things: A Survey
Razzaque, M. A., Milojevic-Jevric, M., Palade, A., & Clarke, S. (2015) · IEEE Internet of Things Journal · doi:10.1109/jiot.2015.2498900 · Cited by 1,026
A survey of intrusion detection in Internet of Things
Zarpelão, B. B., Miani, R. S., Kawakani, C. T., & de Alvarenga, S. C. (2017) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.02.009 · Cited by 1,022
Big Data technologies: A survey
Oussous, A., Benjelloun, F.-Z., Lahcen, A. A., & Belfkih, S. (2017) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2017.06.001 · Cited by 997
Fog Computing: Helping the Internet of Things Realize Its Potential
Dastjerdi, A. V., & Buyya, R. (2016) · Computer · doi:10.1109/mc.2016.245 · Cited by 994
Big IoT data analytics: Architecture, opportunities, and open research challenges
(2017) · IEEE Access · doi:10.1109/access.2017.2689040 · Cited by 980
A survey of machine learning for big data processing
Qiu, J., Wu, Q., Ding, G., Xu, Y., & Feng, S. (2016) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-016-0355-x · Cited by 973
Intrusion detection by machine learning: A review
Tsai, C.-F., Hsu, Y.-F., Lin, C.-Y., & Lin, W.-Y. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.05.029 · Cited by 969
Machine learning in wireless sensor networks: Algorithms, strategies, and applications
Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H.-P. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2320099 · Cited by 965
A survey of multiple classifier systems as hybrid systems
Woźniak, M., Graña, M., & Corchado, E. (2013) · Information Fusion · doi:10.1016/j.inffus.2013.04.006 · Cited by 930
Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.056 · Cited by 930
Self-citationMachine Learning DDoS Detection for Consumer Internet of Things Devices
Doshi, R., Apthorpe, N., & Feamster, N. (2018) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw.2018.00013 · Cited by 920
Can blockchain strengthen the internet of things?
Kshetri, N. (2017) · IT Professional · doi:10.1109/mitp.2017.3051335 · Cited by 918
SVELTE: Real-time intrusion detection in the Internet of Things
Raza, S., Wallgren, L., & Voigt, T. (2013) · Ad Hoc Networks · doi:10.1016/j.adhoc.2013.04.014 · Cited by 908
A survey of intrusion detection techniques in Cloud
Modi, C., Patel, D., Borisaniya, B., Patel, H. B., Patel, A., & Rajarajan, M. (2012) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2012.05.003 · Cited by 897
The emerging field of mobile health
Steinhubl, S. R., Muse, E. D., & Topol, E. J. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aaa3487 · Cited by 822
Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2707140 · Cited by 810
Use of K-Nearest Neighbor classifier for intrusion detection
Liao, Y., & Vemuri, V. R. (2002) · Computers & Security · doi:10.1016/s0167-4048(02)00514-x · Cited by 809
Stealing Machine Learning Models via Prediction APIs
Tramèr, F., Zhang, F., Juels, A., Reiter, M. K., & Ristenpart, T. (2016) · arXiv · doi:10.48550/arxiv.1609.02943 · Cited by 796
Security in the Internet of Things: A Review
Suo, H., Wan, J., Zou, C., & Liu, J. (2012) · International Conference on Computer Science and Electronics Engineering (ICCSEE) · doi:10.1109/iccsee.2012.373 · Cited by 792
Internet of things (IoT) security: Current status, challenges and prospective measures
Mahmoud, R., Yousuf, T., Aloul, F., & Zualkernan, I. A. (2015) · International Conference for Internet Technology and Secured Transactions · doi:10.1109/icitst.2015.7412116 · Cited by 778
Cook, D. J., Crandall, A. S., Thomas, B. L., & Krishnan, N. C. (2012) · Computer · doi:10.1109/mc.2012.328 · Cited by 771
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection
Mishra, P., Varadharajan, V., Tupakula, U. K., & Pilli, E. S. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2847722 · Cited by 769
IoT Security: Ongoing Challenges and Research Opportunities
Zhang, Z., Cho, M. C. Y., Wang, C., Hsu, C.-W., Chen, C.-K., & Shieh, S. W. (2014) · IEEE International Conference on Service-Oriented Computing and Applications (SOCA) · doi:10.1109/soca.2014.58 · Cited by 746
Um, T. T., Pfister, F. M. J., Pichler, D., Endo, S., Lang, M., Hirche, S., Fietzek, U., & Kulić, D. (2017) · Proceedings of the 19th ACM International Conference on Multimodal Interaction · doi:10.1145/3136755.3136817 · Cited by 729
IoT SENTINEL: Automated Device-Type Identification for Security Enforcement in IoT
Miettinen, M., Marchal, S., Hafeez, I., Asokan, N., Sadeghi, A., & Tarkoma, S. (2017) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2017.283 · Cited by 721
Cyber Security and the Internet of Things: Vulnerabilities, Threats, Intruders and Attacks
Abomhara, M., & Køien, G. M. (2015) · Journal of Cyber Security and Mobility · doi:10.13052/jcsm2245-1439.414 · Cited by 718
A Survey on the Internet of Things Security
Zhao, K., & Ge, L. (2013) · International Conference on Computational Intelligence and Security (CIS) · doi:10.1109/cis.2013.145 · Cited by 712
Research on the architecture of Internet of Things
Wu, M., Lu, T., Ling, F.-Y., Sun, J., & Du, H. (2010) · International Conference on Advanced Computer Theory and Engineering · doi:10.1109/icacte.2010.5579493 · Cited by 706
Díáz, M., Martín, C., & Rubio, B. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.01.010 · Cited by 689
IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security?
Xiao, L., Wan, X., Lu, X., Zhang, Y., & Wu, D. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2018.2825478 · Cited by 681
Security and Privacy for Cloud-Based IoT: Challenges
Zhou, J., Cao, Z., Dong, X., & Vasilakos, A. V. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.1600363cm · Cited by 667
Machine Learning Methods for Attack Detection in the Smart Grid
Özay, M., Esnaola, I., Vural, F. T. Y., Kulkarni, S. R., & Poor, H. V. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2404803 · Cited by 645
(2014) · International Conference on Learning Representations · Cited by 634
The role of big data analytics in Internet of Things
Ahmed, E., Yaqoob, I., Hashem, I. A. T., Khan, I., Ahmed, A. I. A., Imran, M., & Vasilakos, A. V. (2017) · Computer Networks · doi:10.1016/j.comnet.2017.06.013 · Cited by 629
Data Mining for Internet of Things: A Survey
Tsai, C.-W., Lai, C.-F., Chiang, M.-C., & Yang, L. T. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.103013.00206 · Cited by 620 · cited 2×
“insights from these data, researchers [51]–[57] have discussed different methods of integrating big data analytical methods with IoT design.”
BackgroundMethodBotnets and Internet of Things Security
Bertino, E., & Islam, N. (2017) · Computer · doi:10.1109/mc.2017.62 · Cited by 618
RFID Technology for IoT-Based Personal Healthcare in Smart Spaces
Amendola, S., Lodato, R., Manzari, S., Occhiuzzi, C., & Marrocco, G. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2313981 · Cited by 617
Internet of things security: A top-down survey
Kouicem, D. E., Bouabdallah, A., & Lakhlef, H. (2018) · Computer Networks · doi:10.1016/j.comnet.2018.03.012 · Cited by 612
Efficient kNN classification algorithm for big data
Deng, Z., Zhu, X., Cheng, D., Zong, M., & Zhang, S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.08.112 · Cited by 603
Um, T. T. (n.d.) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · Cited by 602
Intelligent 5G: When Cellular Networks Meet Artificial Intelligence
Li, R., Zhao, Z., Zhou, X., Ding, G., Chen, Y., Wang, Z., & Zhang, H. (2017) · IEEE Wireless Communications · doi:10.1109/mwc.2017.1600304wc · Cited by 601
Survey on Anomaly Detection using Data Mining Techniques
Agrawal, S., & Agrawal, J. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.08.220 · Cited by 595
A novel hybrid intrusion detection method integrating anomaly detection with misuse detection
Kim, G., Lee, S. M., & Kim, S. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.08.066 · Cited by 593
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Alsheikh, M. A., Lin, S., Niyato, D. T., et al. (2014) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · doi:10.48550/arxiv.1405.4463 · Cited by 581
A Survey on Machine-Learning Techniques in Cognitive Radios
Bkassiny, M., Li, Y., & Jayaweera, S. K. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.100412.00017 · Cited by 580
Breaking Cryptographic Implementations Using Deep Learning Techniques
Maghrebi, H., Portigliatti, T., & Prouff, E. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49445-6_1 · Cited by 579
Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures
Komninos, N., Philippou, E., & Pitsillides, A. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2320093 · Cited by 574
How to Construct Deep Recurrent Neural NetworksPreprint
Pascanu, R., Gülçehre, Ç., Cho, K., & Bengio, Y. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.6026 · Cited by 574
Android Security: A Survey of Issues, Malware Penetration, and Defenses
Faruki, P., Bharmal, A., Laxmi, V., Ganmoor, V., Gaur, M. S., Conti, M., & Rajarajan, M. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2386139 · Cited by 560
Cognitive internet of things: A new paradigm beyond connection
Wu, Q., Ding, G., Xu, Y., Feng, S., Du, Z., Wang, J., & Long, K. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2311513 · Cited by 550
Zhou, W., Jia, Y., Peng, A., Zhang, Y., & Liu, P. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2847733 · Cited by 531
From "smart objects" to "social objects": The next evolutionary step of the internet of things
Atzori, L., Iera, A., & Morabito, G. (2014) · IEEE Communications Magazine · doi:10.1109/mcom.2014.6710070 · Cited by 530
Deep Android Malware Detection
McLaughlin, N., del Rincón, J. M., Kang, B., Yerima, S. Y., Miller, P. C., Sezer, S., Safaei, Y., Trickel, E., Zhao, Z., Doupé, A., et al. (2017) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/3029806.3029823 · Cited by 515
Pajouh, H. H., Javidan, R., Khayami, R., Dehghantanha, A., & Choo, K. R. (2016) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2016.2633228 · Cited by 504
A novel SVM-kNN-PSO ensemble method for intrusion detection system
Aburomman, A. A., & Reaz, M. B. I. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.10.011 · Cited by 500
A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks
Klaine, P. V., Imran, M. A., Onireti, O., & Souza, R. D. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2727878 · Cited by 494
A survey towards an integration of big data analytics to big insights for value-creation
Saggi, M. K., & Jain, S. (2018) · Information Processing & Management · doi:10.1016/j.ipm.2018.01.010 · Cited by 494
A roadmap for security challenges in the Internet of Things
Sfar, A. R., Natalizio, E., Challal, Y., & Chtourou, Z. (2017) · Digital Communications and Networks · doi:10.1016/j.dcan.2017.04.003 · Cited by 490
Training and Analysing Deep Recurrent Neural Networks
Hermans, M., & Schrauwen, B. (2013) · neural information processing systems · Cited by 489
Intrusion Detection using Naive Bayes Classifier with Feature Reduction
Mukherjee, S., & Sharma, N. (2012) · Procedia Technology · doi:10.1016/j.protcy.2012.05.017 · Cited by 477
IoT Goes Nuclear: Creating a ZigBee Chain Reaction
Ronen, E., Adi Shamir, Z., Weingarten, A.-O., & O’Flynn, C. (2017) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2017.14 · Cited by 468
Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey
Sezer, Ö. B., Doğdu, E., & Özbayoğlu, A. M. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2773600 · Cited by 468
Khan, A. A., Rehmani, M. H., & Rachedi, A. (2017) · IEEE Wireless Communications · doi:10.1109/mwc.2017.1600404 · Cited by 461
DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices
Lane, N. D., Bhattacharya, S., Georgiev, P., Forlivesi, C., Jiao, L., Qendro, L., & Kawsar, F. (2016) · 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1109/ipsn.2016.7460664 · Cited by 450
Cloud Computing Applications for Smart Grid: A Survey
Bera, S., Misra, S., & Rodrigues, J. J. P. C. (2014) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2014.2321378 · Cited by 434
Meidan, Y., Bohadana, M., Shabtai, A., Guarnizo, J., Ochoa, M., Tippenhauer, N. O., & Elovici, Y. (2017) · ACM Symposium on Applied Computing · doi:10.1145/3019612.3019878 · Cited by 424
A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View
Liu, Q., Li, P., Zhao, W., Cai, W., Yu, S., & Leung, V. C. M. (2018) · IEEE Access · doi:10.1109/access.2018.2805680 · Cited by 421
Serving at the Edge: A Scalable IoT Architecture Based on Transparent Computing
Ren, J., Guo, H., Xu, C., & Zhang, Y. (2017) · IEEE Network · doi:10.1109/mnet.2017.1700030 · Cited by 417
Data Fusion and IoT for Smart Ubiquitous Environments: A Survey
Alam, F., Mehmood, R., Katib, I. A., Albogami, N., & Albeshri, A. A. (2017) · IEEE Access · doi:10.1109/access.2017.2697839 · Cited by 400
Deep Learning: The Frontier for Distributed Attack Detection in Fog-to-Things Computing
Diro, A. A., & Chilamkurti, N. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700332 · Cited by 398
Network anomaly detection with the restricted Boltzmann machine
Fiore, U., Palmieri, F., Castiglione, A., & De Santis, A. (2013) · Neurocomputing · doi:10.1016/j.neucom.2012.11.050 · Cited by 376
A Survey on Internet of Things: Security and Privacy Issues
Sathishkumar, J., & Patel, D. (2014) · International Journal of Computer Applications · doi:10.5120/15764-4454 · Cited by 368
Autoencoder-based feature learning for cyber security applications
Yousefi‐Azar, M., Varadharajan, V., Hamey, L., & Tupakula, U. K. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966342 · Cited by 366
Semi-supervised learning based distributed attack detection framework for IoT
Rathore, S., & Park, J. H. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.05.049 · Cited by 355
Building decision tree classifier on private data
Du, W. K., & Zhan, Z. (2002) · Syracuse University Libraries (Syracuse University) · Cited by 353
Practical Evasion of a Learning-Based Classifier: A Case Study
Šrndić, N., & Laskov, P. (2014) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2014.20 · Cited by 353
Association Rules Mining: A Recent Overview
Kotsiantis, S. B., & Kanellopoulos, D. N. (2008) · Cited by 350
A lightweight attribute-based encryption scheme for the Internet of Things
Yao, X., Chen, Z., & Tian, Y. (2014) · Future Generation Computer Systems · doi:10.1016/j.future.2014.10.010 · Cited by 349
Detection of known and unknown DDoS attacks using Artificial Neural Networks
Saied, A., Overill, R. E., & Radzik, T. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.04.101 · Cited by 330
Suthaharan, S. (2014) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/2627534.2627557 · Cited by 320
Ensuring Safety, Security, and Sustainability of Mission-Critical Cyber–Physical Systems
Banerjee, A., Venkatasubramanian, K. K., Mukherjee, T., & Gupta, S. K. S. (2011) · Proceedings of the IEEE · doi:10.1109/jproc.2011.2165689 · Cited by 318
A New Intrusion Detection System Based on KNN Classification Algorithm in Wireless Sensor Network
Li, W., Yi, P., Wu, Y., Pan, L., & Li, J. (2014) · Journal of Electrical and Computer Engineering · doi:10.1155/2014/240217 · Cited by 297
Security and privacy issues in implantable medical devices: A comprehensive survey
Cámara, C., Lopez, P. P., & Tapiador, J. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.04.007 · Cited by 296
Internet of Things (IoT): Taxonomy of security attacks
Nawir, M., Amir, A., Yaakob, N., & Lynn, O. B. (2016) · International Conference on Electronic Design · doi:10.1109/iced.2016.7804660 · Cited by 294
Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT
Shi, C., Liu, J., Liu, H., & Chen, Y. J. (2017) · ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) · doi:10.1145/3084041.3084061 · Cited by 294
A survey: Attacks on RPL and 6LoWPAN in IoT
Pongle, P., & Chavan, G. T. (2015) · doi:10.1109/pervasive.2015.7087034 · Cited by 286
Moosavi, S. R., Gia, T. N., Rahmani, A.-M., Nigussie, E., Virtanen, S., Isoaho, J., & Tenhunen, H. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.05.013 · Cited by 280
Proposed Security Model and Threat Taxonomy for the Internet of Things (IoT)
Babar, S. D., Mahalle, P. N., Stango, A., Prasad, N. R., & Prasad, R. (2010) · Communications in computer and information science · doi:10.1007/978-3-642-14478-3_42 · Cited by 280
The Web of Things: A Survey (Invited Paper)
Zeng, D., Guo, S., & Cheng, Z. (2011) · Journal of Communications · doi:10.4304/jcm.6.6.424-438 · Cited by 279
Implicit Authentication through Learning User Behavior
Shi, E., Niu, Y., Jakobsson, M., & Chow, R. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-18178-8_9 · Cited by 269
The rise of ransomware and emerging security challenges in the Internet of Things
Yaqoob, I., Ahmed, E., Rehman, M. H. U., Ahmed, A. I. A., Al-Garadi, M. A., Imran, M. A., & Guizani, M. (2017) · Computer Networks · doi:10.1016/j.comnet.2017.09.003 · Cited by 269
Self-citationDetection of Unauthorized IoT Devices Using Machine Learning TechniquesPreprint
Meidan, Y., Bohadana, M., Shabtai, A., Ochoa, M., Tippenhauer, N. O., Guarnizo, J., & Elovici, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.04647 · Cited by 269
Security Issues and Challenges for the IoT-based Smart Grid
Bekara, C. (2014) · Procedia Computer Science · doi:10.1016/j.procs.2014.07.064 · Cited by 266
Internet of Things and Big Data Technologies for Next Generation Healthcare
Bhatt, C., Dey, N., & Ashour, A. S. (2017) · Studies in big data · doi:10.1007/978-3-319-49736-5 · Cited by 258
Learning Intrusion Detection: Supervised or Unsupervised?
Laskov, P., Düssel, P., Schäfer, C., & Rieck, K. (2005) · Lecture notes in computer science · doi:10.1007/11553595_6 · Cited by 253
Bostani, H., & Sheikhan, M. (2016) · Computer Communications · doi:10.1016/j.comcom.2016.12.001 · Cited by 253
Network Anomaly Detection by Cascading K-Means Clustering and C4.5 Decision Tree algorithm
Muniyandi, A. P., Rajeswari, R., & Rajaram, R. (2012) · Procedia Engineering · doi:10.1016/j.proeng.2012.01.849 · Cited by 249
Study and application on the architecture and key technologies for IOT
Yang, Z., Yue, Y., Yang, Y., Peng, Y., Wang, X., & Liu, W. (2011) · International Conference on Multimedia Technology (ICMT) · doi:10.1109/icmt.2011.6002149 · Cited by 249
Role Of Middleware For Internet Of Things: A Study
Bandyopadhyay, S., Sengupta, M., Maiti, S., & Dutta, S. (2011) · International Journal of Computer Science & Engineering Survey · doi:10.5121/ijcses.2011.2307 · Cited by 246
Flagged first, then by citations
Research works printed but not linked: 17
- lakhotia, 2004, Are metamorphic viruses really invincible, Virus Bull, 12, 57
- zheng, 2018, IoT Malware Evolves to Harvest Bots by Exploiting a Zero-Day Home Router Vulnerability
- baba, 2015, Current issues in ensemble methods and its applications, J Theor Appl Inf Technol, 81, 266
- münz, 2007, Traffic anomaly detection using k-means clustering, Proc GI/ITG Workshop MMBnet, 1
- adetunmbi, 2008, Network intrusion detection based on rough set and k-nearest neighbor, Int J Comput ICT Res, 2, 60
- 2017, Internet of medical things forecast to 2021
- 10.1007/BF03219883 · DOI
- goodfellow, 2016, Deep Learning
- zhu, 2005, Semi-supervised learning literature survey
- clevert, 2015, Fast and accurate deep network learning by exponential linear units (ELUs)
- mohammadi, 2017, Deep Learning for IoT Big Data and Streaming Analytics A Survey
- goodfellow, 2014, Generative adversarial nets, Proc Adv Neural Inf Process Syst, 2672
- tong, 2001, Support vector machine active learning with applications to text classification, J Mach Learn Res, 2, 45
- nguyen, 2019, Secure computation offloading in blockchain based IoT networks with deep reinforcement learning
- dulac-arnold, 2019, Challenges of Real-World Reinforcement Learning
- nguyen, 2019, Deep reinforcement learning for cyber security
- 2018, Mobile Apps Leveraging the Internet of Things (IoT)
Cited by
Sorted by citations
On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds
Wang, C., You, X., Gao, X., Zhu, X., Li, Z., Zhang, C., Wang, H., Huang, Y., Chen, Y., Haas, H., et al. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3249835 · Cited by 2,613
The Future of Healthcare Internet of Things: A Survey of Emerging Technologies
Qadri, Y. A., Nauman, A., Zikria, Y. B., Vasilakos, A. V., & Kim, S. W. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2973314 · Cited by 880
Applications of artificial intelligence and machine learning in smart cities
Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.02.069 · Cited by 756
Aldweesh, A., Derhab, A., & Emam, A. Z. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105124 · Cited by 703
Javed, A. R., Shahzad, F., Rehman, S. U., Zikria, Y. B., Razzak, I., Jalil, Z., & Xu, G. (2022) · Cities · doi:10.1016/j.cities.2022.103794 · Cited by 645
Metadata
- Authors
- Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M.
- Affiliations
- Qatar University (QA)
- Temple University (US)
- University of Malaya (MY)
- ORCID
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2020
- Volume
- 22
- Issue
- 3
- Pages
- 1646-1685
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W2885012953
- Semantic Scholar
- da8a949f9c9f1df3a38f12c2cac97b789c705465
- Cited by
- 1,392
- References
- 294
- Citations by year
- 2026: 170 · 2025: 234 · 2024: 232 · 2023: 237 · 2022: 213 · 2021: 217 · 2020: 75
- FWCI
- 87.17
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Qatar University QSpace (Qatar University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- NPRP from the Qatar National Research Fund (8-408-2-172, 12S-0305-190231)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- Internet of Things security, machine learning for security, deep learning for security, IoT security threats, attack surface, IoT vulnerabilities, intrusion detection, anomaly detection, authentication, access control, network security
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
