Monitoring Real Time Security Attacks for IoT Systems Using DevSecOps: A Systematic Literature Review
Open accessAbstract
In many enterprises and the private sector, the Internet of Things (IoT) has spread globally. The growing number of different devices connected to the IoT and their various protocols have contributed to the increasing number of attacks, such as denial-of-service (DoS) and remote-to-local (R2L) ones. There are several approaches and techniques that can be used to construct attack detection models, such as machine learning, data mining, and statistical analysis. Nowadays, this technique is commonly used because it can provide precise analysis and results. Therefore, we decided to study the previous literature on the detection of IoT attacks and machine learning in order to understand the process of creating detection models. We also evaluated various datasets used for the models, IoT attack types, independent variables used for the models, evaluation metrics for assessment of models, and monitoring infrastructure using DevSecOps pipelines. We found 49 primary studies, and the detection models were developed using seven different types of machine learning techniques. Most primary studies used IoT device testbed datasets, and others used public datasets such as NSL-KDD and UNSW-NB15. When it comes to measuring the efficiency of models, both numerical and graphical measures are commonly used. Most IoT attacks occur at the network layer according to the literature. If the detection models applied DevSecOps pipelines in development processes for IoT devices, they were more secure. From the results of this paper, we found that machine learning techniques can detect IoT attacks, but there are a few issues in the design of detection models. We also recommend the continued use of hybrid frameworks for the improved detection of IoT attacks, advanced monitoring infrastructure configurations using methods based on software pipelines, and the use of machine learning techniques for advanced supervision and monitoring.
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Embedding automated security testing and machine learning into CI/CD pipelines facilitates early attack detection and continuous vulnerability assessment across varied IoT landscapes.
“finding that embedding automated security testing and machine learning into CI/CD pipelines facilitates early attack detection and continuous vulnerability assessment across varied IoT landscapes.”
From Evolution of DevSecOps and Its Influence on Application Security: A Systematic Literature Review (2025) · cites this paper as [34]
Section 3.6 says IoT detection models using DevSecOps pipelines were more secure, and the Abstract says machine learning can detect IoT attacks. The Discussion recommends pipeline-based monitoring and machine learning, but the paper does not establish that embedding automated security testing and machine learning in CI/CD pipelines enables early attack detection or continuous vulnerability assessment across varied IoT settings. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe literature reveals ongoing challenges including a shortage of security experts, limited resources, scalability issues for heterogeneous device ecosystems, high false positive rates, and insufficient standardisation of evaluation metrics.
“nonetheless, the literature reveals ongoing challenges including a shortage of security experts, limited resources, scalability issues for heterogeneous device ecosystems, high false positive rates, and insufficient standardisation of evaluation metrics.”
From Evolution of DevSecOps and Its Influence on Application Security: A Systematic Literature Review (2025) · cites this paper as [34]
The Introduction (page unavailable) links the growing number of different IoT devices and protocols to security risks, and the Discussion (page unavailable) identifies challenges in attack-detection datasets and models. The review also catalogs varied evaluation metrics, but the passages do not establish a shortage of security experts, limited resources, scalability problems, high false-positive rates, or insufficient standardisation of metrics as ongoing challenges. (LLM, conf=0.78)
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Cites
A Vision of IoT: Applications, Challenges, and Opportunities With China Perspective
Chen, S., Xu, H., Liu, D., Hu, B., & Wang, H. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2337336 · Cited by 1,212 · see citing works
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 1,100 · see citing works
Alsaedi, A., Moustafa, N., Tari, Z., Mahmood, A. N., & Anwar, A. (2020) · IEEE Access · doi:10.1109/access.2020.3022862 · Cited by 983 · see citing works
IoT: Internet of Threats? A Survey of Practical Security Vulnerabilities in Real IoT Devices
Meneghello, F., Calore, M., Zucchetto, D., Polese, M., & Zanella, A. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2935189 · Cited by 812 · see citing works
A Supervised Intrusion Detection System for Smart Home IoT Devices
Anthi, E., Williams, L., Slowinska, M., Theodorakopoulos, G., & Burnap, P. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2926365 · Cited by 599 · see citing works
Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things
Zolanvari, M., Teixeira, M. A., Gupta, L., Khan, K. M., & Jain, R. K. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2912022 · Cited by 526 · see citing works
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 · see citing works
Maseer, Z. K., Yusof, R., Bahaman, N., Mostafa, S. A., & Foozy, C. F. M. (2021) · IEEE Access · doi:10.1109/access.2021.3056614 · Cited by 476 · see citing works
Machine Learning Based Intrusion Detection Systems for IoT Applications
Verma, A., & Ranga, V. (2019) · Wireless Personal Communications · doi:10.1007/s11277-019-06986-8 · Cited by 437 · see citing works
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 394 · see citing works
Blockchain Security Attacks, Challenges, and Solutions for the Future Distributed IoT Network
Singh, S., Hosen, A. S. M. S., & Yoon, B. (2021) · IEEE Access · doi:10.1109/access.2021.3051602 · Cited by 373 · see citing works
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 · see citing works
Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things
Thamilarasu, G., & Chawla, S. (2019) · Sensors · doi:10.3390/s19091977 · Cited by 306 · see citing works
Deep Learning-Based Intrusion Detection for IoT Networks
Ge, M., Fu, X., Syed, N., Baig, Z., Teo, G., & Robles-Kelly, A. (2019) · 2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC) · doi:10.1109/prdc47002.2019.00056 · Cited by 269 · see citing works
A Host-Based Intrusion Detection and Mitigation Framework for Smart Home IoT Using OpenFlow
Nobakht, M., Sivaraman, V., & Boreli, R. (2016) · 2016 11th International Conference on Availability, Reliability and Security (ARES) · doi:10.1109/ares.2016.64 · Cited by 237 · see citing works
A hybrid unsupervised clustering-based anomaly detection method
Guo, P., Wang, L., Shen, J., & Dong, F. (2020) · Tsinghua Science & Technology · doi:10.26599/tst.2019.9010051 · Cited by 232 · see citing works
Latif, S., Zou, Z., Idrees, Z., & Ahmad, J. E. (2020) · IEEE Access · doi:10.1109/access.2020.2994079 · Cited by 198 · see citing works
Dynamic attack detection and mitigation in IoT using SDN
Bhunia, S. S., & Gurusamy, M. (2017) · International Telecommunication Networks and Applications Conference (ITNAC) · doi:10.1109/atnac.2017.8215418 · Cited by 174 · see citing works
Hybrid approach to intrusion detection in fog-based IoT environments
de Souza, C. A., Westphall, C. B., Machado, R. B., Sobral, J. B. M., & Vieira, G. D. S. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107417 · Cited by 163 · see citing works
Pulse: an adaptive intrusion detection for the internet of things
Anthi, E., Williams, L., & Burnap, P. (2018) · doi:10.1049/cp.2018.0035 · Cited by 144 · see citing works
An Intrusion Detection System Against DDoS Attacks in IoT Networks
Roopak, M., Tian, G. Y., & Chambers, J. A. (2020) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc47524.2020.9031206 · Cited by 143 · see citing works
ELNIDS: Ensemble Learning based Network Intrusion Detection System for RPL based Internet of Things
Verma, A., & Ranga, V. (2019) · International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU) · doi:10.1109/iot-siu.2019.8777504 · Cited by 140 · see citing works
An Efficient Spam Detection Technique for IoT Devices Using Machine Learning
Makkar, A., Garg, S., Kumar, N., Hossain, M. S., Ghoneim, A., & Alrashoud, M. R. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2968927 · Cited by 140 · see citing works
ML-IDS: A machine learning approach to detect wormhole attacks in Internet of Things
Shukla, P. (2017) · Intelligent Systems Conference (IntelliSys) · doi:10.1109/intellisys.2017.8324298 · Cited by 135 · see citing works
Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning
Illy, P., Kaddoum, G., Moreira, C. M., Kaur, K., & Garg, S. (2019) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc.2019.8885534 · Cited by 122 · see citing works
Internet of Things: Vision, Applications and Challenges
Mehta, R., Sahni, J., & Khanna, K. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.05.042 · Cited by 120 · see citing works
Systematic Literature Review on IoT-Based Botnet Attack
Ali, I., Ahmed, A. I. A., Almogren, A., Raza, M. A., Shah, S. A., Khan, A., & Gani, A. (2020) · IEEE Access · doi:10.1109/access.2020.3039985 · Cited by 118 · see citing works
Wang, Z., Zeng, Y., Liu, Y., & Li, D. (2021) · IEEE Access · doi:10.1109/access.2021.3051074 · Cited by 117 · see citing works
SecDevOps: Is It a Marketing Buzzword? - Mapping Research on Security in DevOps
Mohan, V., & Othmane, L. B. (2016) · 2016 11th International Conference on Availability, Reliability and Security (ARES) · doi:10.1109/ares.2016.92 · Cited by 112 · see citing works
Hybrid Low-Power Wide-Area Mesh Network for IoT Applications
Jiang, X., Zhang, H., Yi, E. B., Raghunathan, N., Mousoulis, C., Chaterji, S., Peroulis, D., Shakouri, A., & Bagchi, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3009228 · Cited by 102 · see citing works
Timely Detection and Mitigation of Stealthy DDoS Attacks via IoT Networks
Doshi, K., Yılmaz, Y., & Uludag, S. (2021) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3049942 · Cited by 101 · see citing works
A Reliable Communication Framework and Its Use in Internet of Things (IoT)Preprint
Alam, T. (2020) · doi:10.36227/techrxiv.12657158.v1 · Cited by 100 · see citing works
Enforcing security in Internet of Things frameworks: A Systematic Literature Review
Aly, M., Khomh, F., Haoues, M., Quintero, A., & Yacout, S. (2019) · Internet of Things · doi:10.1016/j.iot.2019.100050 · Cited by 98 · see citing works
Zero-Bias Deep Learning for Accurate Identification of Internet-of-Things (IoT) Devices
Liu, Y., Wang, J., Li, J., Song, H., Yang, T., Niu, S., & Ming, Z. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3018677 · Cited by 96 · see citing works
Anomaly Detection on IoT Network Intrusion Using Machine Learning
Liu, Z., Thapa, N., Shaver, A., Roy, K., Yuan, X., & Khorsandroo, S. (2020) · International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) · doi:10.1109/icabcd49160.2020.9183842 · Cited by 92 · see citing works
Zhao, S., Li, W., Zia, T. A., & Zomaya, A. Y. (2017) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc-picom-datacom-cyberscitec.2017.141 · Cited by 86 · see citing works
Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices
Liu, Y., Song, H. H., Yang, T. T., Wang, J., Li, J., Niu, S., & Ming, Z. (2021) · Scholarly Commons (Embry–Riddle Aeronautical University) · Cited by 86 · see citing works
An Intelligent End–Edge–Cloud Architecture for Visual IoT-Assisted Healthcare Systems
Yang, Z., Liang, B., & Ji, W. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3052778 · Cited by 86 · see citing works
Unsupervised machine learning for network-centric anomaly detection in IoT
Bhatia, R., Benno, S. A., Esteban, J. O., Lakshman, T. V., & Grogan, J. (2019) · doi:10.1145/3359992.3366641 · Cited by 85 · see citing works
Intrusion Detection In IoT Using Artificial Neural Networks On UNSW-15 Dataset
Hanif, S., Ilyas, T., & Zeeshan, M. (2019) · IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET) · doi:10.1109/honet.2019.8908122 · Cited by 84 · see citing works
Intrusion Detection System for Internet of Things based on a Machine Learning approach
Liang, C. L., Shanmugam, B., Azam, S., Jonkman, M., Boer, F. D., & Samy, G. N. (2019) · International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN) · doi:10.1109/vitecon.2019.8899448 · Cited by 76 · see citing works
Using Deep Learning Techniques for Network Intrusion Detection
Al-Emadi, S. A., Al-Mohannadi, A., & Al-Senaid, F. (2020) · IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) · doi:10.1109/iciot48696.2020.9089524 · Cited by 74 · see citing works
DRaNN: A Deep Random Neural Network Model for Intrusion Detection in Industrial IoT
Latif, S., Idrees, Z., Zou, Z., & Ahmad, J. E. (2020) · International Conference on UK-China Emerging Technologies (UCET) · doi:10.1109/ucet51115.2020.9205361 · Cited by 70 · see citing works
A Machine Learning-Based Lightweight Intrusion Detection System for the Internet of Things
Fenanir, S., Semchedine, F., & Baadache, A. (2019) · Revue d intelligence artificielle · doi:10.18280/ria.330306 · Cited by 66 · see citing works
Classifying Security Attacks in IoT Networks Using Supervised Learning
Ioannou, C., & Vassiliou, V. (2019) · International Conference on Distributed Computing in Sensor Systems (DCOSS) · doi:10.1109/dcoss.2019.00118 · Cited by 65 · see citing works
Hybrid Machine Learning for Network Anomaly Intrusion Detection
Chkirbene, Z., Eltanbouly, S., Bashendy, M., Al‐Naimi, N., & Erbad, A. (2020) · IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) · doi:10.1109/iciot48696.2020.9089575 · Cited by 65 · see citing works
Self-Service Cybersecurity Monitoring as Enabler for DevSecOps
Díaz, J., Pérez, J., Peña, M. Á. L., Mena, G. A., & Yagüe, A. (2019) · IEEE Access · doi:10.1109/access.2019.2930000 · Cited by 64 · see citing works
Adaptive Multivariate Data Compression in Smart Metering Internet of Things
Chowdhury, M. R., Tripathi, S., & De, S. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2981382 · Cited by 62 · see citing works
Intrusion Detection in IoT-Based Smart Grid Using Hybrid Decision Tree
Taghavinejad, S. M., Taghavinejad, M., Shahmiri, L., Zavvar, M. H., & Zavvar, M. H. (2020) · International Conference on Web Research (ICWR) · doi:10.1109/icwr49608.2020.9122320 · Cited by 58 · see citing works
READ-IoT: Reliable Event and Anomaly Detection Framework for the Internet of Things
Yahyaoui, A., Abdellatif, T., Yangui, S., & Attia, R. (2021) · IEEE Access · doi:10.1109/access.2021.3056149 · Cited by 54 · see citing works
Prioritization based taxonomy of DevOps security challenges using PROMETHEE
Rafi, S., Yu, W., Akbar, M. A., Alsanad, A., & Gumaei, A. (2020) · IEEE Access · doi:10.1109/access.2020.2998819 · Cited by 50 · see citing works
A Systematic Literature Review of Authentication in Internet of Things for Heterogeneous Devices
Kavianpour, S., Shanmugam, B., Azam, S., Zamani, M., Samy, G. N., & Boer, F. D. (2019) · Journal of Computer Networks and Communications · doi:10.1155/2019/5747136 · Cited by 47 · see citing works
DevOps for IoT Systems: Fast and Continuous Monitoring Feedback of System Availability
Peña, M. Á. L., Díaz, J., Pérez, J., & Humanes, H. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3012763 · Cited by 46 · see citing works
Indre, I., & Lemnaru, C. (2016) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp.2016.7737142 · Cited by 43 · see citing works
Hierarchical anomaly based intrusion detection and localization in IoT
Yahyaoui, A., Abdellatif, T., & Attia, R. (2019) · doi:10.1109/iwcmc.2019.8766574 · Cited by 43 · see citing works
Supervised Machine Learning Based Network Intrusion Detection System for Internet of Things
Rani, D., & Kaushal, N. C. (2020) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt49239.2020.9225340 · Cited by 43 · see citing works
Carter, K. (2017) · IEEE Software · doi:10.1109/ms.2017.3571578 · Cited by 42 · see citing works
IoT Attack Detection with Deep Learning Analysis
Pecori, R., Tayebi, A., Vannucci, A., & Veltri, L. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207171 · Cited by 41 · see citing works
A study of IoT malware activities using association rule learning for darknet sensor data
Ozawa, S., Ban, T., Hashimoto, N., Nakazato, J., & Shimamura, J. (2019) · International Journal of Information Security · doi:10.1007/s10207-019-00439-w · Cited by 37 · see citing works
eMUD: Enhanced Manufacturer Usage Description for IoT Botnets Prevention on Home WiFi Routers
Sajjad, S. M., Yousaf, M., Afzal, H., & Mufti, M. R. (2020) · IEEE Access · doi:10.1109/access.2020.3022272 · Cited by 36 · see citing works
A Lightweight IDS Based on J48 Algorithm for Detecting DoS Attacks on IoT Middleware
Bakhtiar, F. A., Pramukantoro, E. S., & Nihri, H. (2019) · IEEE Global Conference on Life Sciences and Technologies (LifeTech) · doi:10.1109/lifetech.2019.8884057 · Cited by 35 · see citing works
Security Analysis and Prevention of Attacks on IoT Devices
Raghuprasad, A., Padmanabhan, S., Babu, M. A., & Binu, P. K. (2020) · doi:10.1109/iccsp48568.2020.9182055 · Cited by 33 · see citing works
HADS: Hybrid Anomaly Detection System for IoT Environments
Bhatt, P., & Morais, A. (2018) · International Conference on Internet of Things, Embedded Systems and Communications (IINTEC) · doi:10.1109/iintec.2018.8695303 · Cited by 32 · see citing works
Machine Learning Techniques for IoT Intrusions Detection in Aerospace Cyber-Physical Systems
Maleh, Y. (2019) · Studies in computational intelligence · doi:10.1007/978-3-030-20212-5_11 · Cited by 32 · see citing works
MEML: Resource-aware MQTT-based Machine Learning for Network Attacks Detection on IoT Edge Devices
Shalaginov, A., Semeniuta, O., & Alazab, M. (2019) · IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion) · doi:10.1145/3368235.3368876 · Cited by 29 · see citing works
Jadhav, A. R., R., S. K. M. P., & Rajalakshmi, P. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3028437 · Cited by 26 · see citing works
Intrusion Detection and Classification with Autoencoded Deep Neural Network
Rezvy, S., Petridis, M., Lasebae, A., & Zebin, T. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-12942-2_12 · Cited by 25 · see citing works
Zhang, J., Gong, L.-R., Yu, K., Qi, X., Wen, Z., Hua, Q., & Myint, S. H. (2020) · IEEE Access · doi:10.1109/access.2020.3007024 · Cited by 24 · see citing works
A Multi-Class Neural Network Model for Rapid Detection of IoT Botnet Attacks
Alzahrani, H., Abulkhair, M., & Alkayal, E. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110783 · Cited by 22 · see citing works
A Darknet Traffic Analysis for IoT Malwares Using Association Rule Learning
Hashimoto, N., Ozawa, S., Ban, T., Nakazato, J., & Shimamura, J. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.10.511 · Cited by 21 · see citing works
Continuous Delivery of Customized SaaS Edge Applications in Highly Distributed IoT Systems
López-Viana, R., Díaz, J., Diaz, V. H., & Martínez-Ortega, J.-F. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3009633 · Cited by 21 · see citing works
DevOps for IoT Applications Using Cellular Networks and Cloud
Karapantelakis, A., Liang, H., Wang, K., Vandikas, K., Inam, R., Fersman, E., Viela, I. M. M., Seyvet, N., & Giannokostas, V. (2016) · International Conference on Future Internet of Things and Cloud (FiCloud) · doi:10.1109/ficloud.2016.55 · Cited by 18 · see citing works
Machine Learning for the Internet of Things Security: A Systematic Review
Andročec, D., & Vrček, N. (2018) · International Conference on Software Technologies (ICSOFT) · doi:10.5220/0006841205630570 · Cited by 15 · see citing works
Distributed Denial-of-Service Prediction on IoT Framework by Learning Techniques
Dwivedi, S., Vardhan, M., & Tripathi, S. (2020) · Open Computer Science · doi:10.1515/comp-2020-0009 · Cited by 15 · see citing works
An SLR-tool: search process in practice
Hinderks, A., Mayo, F. J. D., Thomaschewski, J., & Escalona, M. J. (2020) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3377812.3382137 · Cited by 15 · see citing works
Experimentation with Local Intrusion Detection in IoT Networks Using Supervised Learning
Ioannou, C., & Vassiliou, V. (2020) · International Conference on Distributed Computing in Sensor Systems (DCOSS) · doi:10.1109/dcoss49796.2020.00073 · Cited by 14 · see citing works
On Lightweight Method for Intrusions Detection in the Internet of Things
Владимирович, Ш. В., Jan, S. U., Ahmed, S., & Koo, I. (2019) · IEEE International Black Sea Conference on Communications and Networking · doi:10.1109/blackseacom.2019.8812813 · Cited by 10 · see citing works
A Hybrid Approach for Anomaly Detection in the Internet of Things
Hosseini, M., & Borojeni, H. R. S. (2018) · doi:10.1145/3269961.3269975 · Cited by 9 · see citing works
Sharma, P., Kherajani, M., Jain, D., & Patel, D. (2020) · International Conference on Data, Engineering and Applications (IDEA) · doi:10.1109/idea49133.2020.9170741 · Cited by 9 · see citing works
Security Attacks in Internet of Things
Nair, R., Sharma, P., & Singh, D. K. (2020) · doi:10.1002/9781119670087.ch14 · Cited by 9 · see citing works
An Intrusion Detection System for the OneM2M Service Layer Based on Edge Machine Learning
Chaabouni, N., Mosbah, M., Zemmari, A., & Sauvignac, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-31831-4_35 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Bipraneel, R.; Cheung, H. A deep learning approach for intrusion detectionin internet of things using BI-directional long short- term memory recurrent neural network. In Proceedings of the 28th International Telecommunication Networks and Applications Conference (ITNAC), Sydney, NSW, Australia, 21-23 November 2018.
Cited by
Sorted by citations
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Yazdinejad, A., Dehghantanha, A., Parizi, R. M., Srivastava, G., & Karimipour, H. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103801 · Cited by 229 · see citing works
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Bhavsar, M., Roy, K., Kelly, J. C., & Odeyomi, O. T. (2023) · Discover Internet of Things · doi:10.1007/s43926-023-00034-5 · Cited by 187 · see citing works
Adil, M., & Khan, M. K. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103311 · Cited by 78 · see citing works
AI for DevSecOps: A Landscape and Future Opportunities
Fu, M. C., Pasuksmit, J., & Tantithamthavorn, C. K. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3712190 · Cited by 83 · see citing works
Data Transformation Schemes for CNN-Based Network Traffic Analysis: A Survey
Krupski, J., Graniszewski, W., & Marcin Iwanowski, C. (2021) · Electronics · doi:10.3390/electronics10162042 · Cited by 48 · see citing works
Benmalek, M., & Seddiki, A. (2025) · Data Science and Management · doi:10.1016/j.dsm.2025.02.005 · Cited by 55 · see citing works
Kadri, M. R., Abdelli, A., Ben‐Othman, J., & Mokdad, L. (2023) · Internet of Things · doi:10.1016/j.iot.2023.101021 · Cited by 42 · see citing works
Jabar, T., & Singh, M. M. (2022) · Sensors · doi:10.3390/s22134662 · Cited by 40 · see citing works
Mukhaini, G. A., Anbar, M., Manickam, S., Al-Amiedy, T. A., & Momani, A. A. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101866 · Cited by 42 · see citing works
Boisrond, K., Tardif, P.-M., & Jaafar, F. (2024) · IEEE Access · doi:10.1109/access.2024.3434618 · Cited by 27 · see citing works
Petrillo, A., Rehman, M., & Baffo, I. (2024) · Applied Sciences · doi:10.3390/app14135380 · Cited by 25 · see citing works
Development methodologies for IoT-based systems: challenges and research directions
Hornos, M. J., & Quinde, M. J. (2024) · Journal of Reliable Intelligent Environments · doi:10.1007/s40860-024-00229-9 · Cited by 24 · see citing works
Passive Rule-Based Approach to Detect Sinkhole Attack in RPL-Based Internet of Things Networks
Al-Sarawi, S., Anbar, M., Alabsi, B. A., Aladaileh, M. A., & Rihan, S. D. A. (2023) · IEEE Access · doi:10.1109/access.2023.3310242 · Cited by 21 · see citing works
Caniglia, A., Dentamaro, V., Galantucci, S., & Impedovo, D. (2025) · Information and Software Technology · doi:10.1016/j.infsof.2024.107605 · Cited by 18 · see citing works
Bahaa, A., Sayed, A., ElFangary, L. M., & Fahmy, H. (2022) · PLoS ONE · doi:10.1371/journal.pone.0278493 · Cited by 18 · see citing works
Self-citationA systematic review of lightweight cryptographic schemes for security and privacy in IoT
Ansari, S. A., & Ali, S. (2025) · Discover Computing · doi:10.1007/s10791-025-09755-3 · Cited by 15 · see citing works
GATEKEEPER Platform: Secure Processing Environment for European Health Data Space
Gaeta, E., Haleem, M. S., Lopez-Perez, L., Manea, M., Cabrera-Umpiérrez, M. F., Arredondo, M. T., Pecchia, L., & Fico, G. (2025) · IEEE Access · doi:10.1109/access.2025.3539559 · Cited by 11 · see citing works
Model to reduce DevOps pipeline execution time using SAST
Saurabh, S. K., & Kumar, D. (2024) · International Journal of System Assurance Engineering and Management · doi:10.1007/s13198-024-02262-6 · Cited by 10 · see citing works
Binbeshr, F., & Imam, M. M. (2025) · International Conference on Evaluation and Assessment in Software Engineering (EASE) · doi:10.1145/3727967.3756841 · Cited by 8 · see citing works
Evolution of DevSecOps and Its Influence on Application Security: A Systematic Literature Review
Mohammed, K., Shanmugam, B., & El-Den, J. (2025) · Technologies · doi:10.3390/technologies13120548 · Cited by 8 · see citing works
AI Empowered DevSecOps Security for Next Generation Development
Yadav, B., Choudhary, G., Shandilya, S. K., & Dragoni, N. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-93135-3_3 · Cited by 7 · see citing works
Blockchain Technology and Cybersecurity in Fintech: Opportunities and Vulnerabilities
Ajayi, O. O., Alozie, C. E., Abieba, O. A., Akerele, J. I., & Collins, A. (2025) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit25111210 · Cited by 7 · see citing works
Yilmaz, M. T., Polat, O., Algül, E., & Doğan, F. (2026) · Scientific Reports · doi:10.1038/s41598-026-37146-1 · Cited by 6 · see citing works
El-Sayed, E., Yassin, A. S., & Fahmy, H. (2025) · Information · doi:10.3390/info16121103 · Cited by 4 · see citing works
Self-citationA Hybrid Graph-Based Risk Assessment and Attack Path Detection Model for IoT Systems
Arat, F., Akleylek, S., & Tok, Z. Y. (2025) · IEEE Access · doi:10.1109/access.2025.3558985 · Cited by 4 · see citing works
Şenol, A. (2023) · International Journal of Pure and Applied Sciences · doi:10.29132/ijpas.1278880 · Cited by 4 · see citing works
Toward a Robust and Enhanced DevSecOps Framework
Ramos, R. C. B., & Yoo, S. G. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-08366-1_28 · Cited by 3 · see citing works
Senapati, B., Naeem, A. B., Matam, P., Upadhyay, C., Achanta, A., Gudur, V., Patel, A. R., & Tiwari, S. (2025) · International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · doi:10.1109/iceccme64568.2025.11277865 · Cited by 3 · see citing works
Unweighted Voting Method to Detect Sinkhole Attack in RPL-Based Internet of Things Networks
Al-Sarawi, S., Anbar, M., Alabsi, B. A., Aladaileh, M. A., & Rihan, S. D. A. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.041108 · Cited by 3 · see citing works
Cybersecurity for Battlefield of Things — A Comprehensive Review
Singh, A., Sharma, G., Krishnamurthi, R., Kumar, A., Bhatia, S., & Mashat, A. (2022) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126622300100 · Cited by 3 · see citing works
How to secure the IoT-based surveillance systems in an ELEGANT way
Pawlicka, A., Puchalski, D., Pawlicki, M., Kozik, R., & Choraś, M. (2023) · IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr57506.2023.10224938 · Cited by 2 · see citing works
A Fuzzy-Bayesian belief network approach to compute efficiency as a metric for IoT systems
Pandey, R. D., & Snigdh, I. (2025) · Soft Computing · doi:10.1007/s00500-025-10510-5 · Cited by 1 · see citing works
Implementation of a Decentralized Intrusion Detection System for IoT Networks
Venugopal, B., & Rani, A. J. M. (2025) · International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE) · doi:10.1109/rmkmate64874.2025.11042386 · Cited by 1 · see citing works
Anomaly Detection in IoT Networks Using WGAN-GP
Singh, P., Pranav, P., Dutta, S., Dubey, P., & Parimalam, P. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6348-4_19 · Cited by 1 · see citing works
Crypto Warfare Techniques- Status, Challenges, and Future Trends
Sadkhan, S. B., & AlRefaai, N. (2021) · International Conference on Advance of Sustainable Engineering and its Application (ICASEA) · doi:10.1109/icasea53739.2021.9733008 · Cited by 1 · see citing works
Evaluation Metrics in DoS Attacks Detection Approaches in IoT: A Survey and a Taxonomy
Kadri, M. R., Abdelli, A., & Mokdad, L. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-18516-8_4 · Cited by 1 · see citing works
Security Policy Enforcement and Behavioral Threat Detection in DevSecOps Pipelines
Kamaluddin, K. (2022) · European Journal of Technology · doi:10.47672/ejt.2723 · Cited by 1 · see citing works
Blockchain in Data Security and Transparency in Business Transactions
Verma, R., & Dhanda, N. (2024) · Wiley eBooks · doi:10.1002/9781394205127.ch9 · Cited by 1 · see citing works
A Survey Study of Common Security Failures and Mitigations for the Internet of Things (IoT)
Scott, M. (2023) · Journal of Telecommunication Electronic and Computer Engineering (JTEC) · doi:10.54554/jtec.2023.15.02.002
Convergence of Blockchain Enabled Internet of Things (IoT) Framework: A Survey
Upadhyay, V., Vaish, A., & Kokila, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-62488-9_9
Systematic Review of Fuzzing in IoT: Evaluating Techniques, Vulnerabilities, and Research GapsPreprint
Touqir, A., Iradat, F., Rakib, A., Taşkın, N., Jadidbonab, H., Asif, Z., & Haas, O. C. L. (2024) · Research Square · doi:10.21203/rs.3.rs-4963553/v1
Enhancing DevSecOps with Generative AI for Secure Code Review and Threat Modeling
Bhardwaj, A. K. (2026) · International Conference on Image Processing and Capsule Networks (ICIPCN) · doi:10.1109/icipcn67432.2026.11438709
DevSecOps in Practice: A Systematic Review of Challenges, Best Practices and Tools
Mohamed, O. E., Elmedany, W. M., Mothanna, Y. M., & Sharif, M. S. (2025) · International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) · doi:10.1109/3ict68299.2025.11442153
Secure and Resilient DevOps Automation Using AI-Based Threat Detection
Bojja, K. (2026) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl67539.2026.11451916
Implementing DevSecOps: A Systematic Literature Review on Integrating Security into DevOps
Guyong, J. A., Balaybay, R. M., Macario, J. M., Pangan, E. J., & Clemente, J. R. D. (2025) · World Symposium on Software Engineering (WSSE) · doi:10.1145/3779657.3779664
CONCEPTUAL MODEL FOR THE USE OF DEVOPS AND AI TOOLS IN AGILE PROJECTSPreprint
Feitosa, D. N. M., Penha, R., Filho, J. R. D. C., Galvão, G. S., & da Silva, V. F. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6956551
Secure Financial Analytics Platforms for Regulatory Compliance and Risk Management
Kola, J. N. (2026) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn70047.2026.11594328
Study of Emergence and Nonlinearity in IIoT Cybersecurity: A Pretest
Viswakumar, P., & Halawi, L. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1017531
- Authors
- Bahaa, A., Abdelaziz, A., Sayed, A., Elfangary, L., & Fahmy, H.
- Affiliations
- Beni-Suef University (EG)
- Higher Technological Institute (EG)
- Helwan University (EG)
- Corresponding author
- Abdelaziz, Ahmed
- Venue
- Information
- Publisher
- MDPI AG
- Published
- 2021-04-07
- Volume
- 12
- Issue
- 4
- Pages
- 154
- ISSN
- 2078-2489 (ISSN-L)
- OpenAlex
- W3150051937
- Cited by
- 48
- References
- 81
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 8 · 2023: 8 · 2022: 6 · 2021: 4
- FWCI
- 1.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Information (publishedVersion, cc-by)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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, attack detection, DevSecOps, machine learning, denial-of-service attacks, remote-to-local attacks, network layer attacks, NSL-KDD dataset, UNSW-NB15, security monitoring, software pipeline, hybrid detection system
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