Deep learning for cyber threat detection in IoT networks: A review

[1]  X. Tran,et al.  Optimal network intrusion detection assignment in multi-level IoT systems , 2023, Comput. Networks.

[2]  Chhagan Lal,et al.  Anomaly based network intrusion detection for IoT attacks using deep learning technique , 2023, Comput. Electr. Eng..

[3]  R. Mohammad,et al.  Intrusion Detection System Using Feature Extraction with Machine Learning Algorithms in IoT , 2023, J. Sens. Actuator Networks.

[4]  Yixiao Xu,et al.  Efficient intrusion detection toward IoT networks using cloud-edge collaboration , 2023, Comput. Networks.

[5]  M. Alani,et al.  An Intelligent Two-Layer Intrusion Detection System for the Internet of Things , 2023, IEEE Transactions on Industrial Informatics.

[6]  M. Ferrag,et al.  2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT , 2023, Comput. Secur..

[7]  Weiqiang Pan,et al.  Self-Learning Spatial Distribution-Based Intrusion Detection for Industrial Cyber-Physical Systems , 2022, IEEE Transactions on Computational Social Systems.

[8]  M. N. Lima,et al.  A Hybrid CNN-LSTM Model for IIoT Edge Privacy-Aware Intrusion Detection , 2022, 2022 IEEE Latin-American Conference on Communications (LATINCOM).

[9]  Z. Aliouat,et al.  Hybrid Deep Learning-based Intrusion Detection System for Industrial Internet of Things , 2022, 2022 5th International Symposium on Informatics and its Applications (ISIA).

[10]  N. Rakesh,et al.  Implementation of intrusion detection model for DDoS attacks in Lightweight IoT Networks , 2022, Expert Syst. Appl..

[11]  Amir Namavar Jahromi,et al.  An ensemble deep federated learning cyber-threat hunting model for Industrial Internet of Things , 2022, Comput. Commun..

[12]  O. A. Wahab,et al.  Intrusion Detection in the IoT Under Data and Concept Drifts: Online Deep Learning Approach , 2022, IEEE Internet of Things Journal.

[13]  Kevin I-Kai Wang,et al.  Variational Few-Shot Learning for Microservice-Oriented Intrusion Detection in Distributed Industrial IoT , 2022, IEEE Transactions on Industrial Informatics.

[14]  M. Ferrag,et al.  Physical Security and Safety of IoT Equipment: A Survey of Recent Advances and Opportunities , 2022, IEEE Transactions on Industrial Informatics.

[15]  Shohei Shimizu,et al.  Hierarchical Adversarial Attacks Against Graph-Neural-Network-Based IoT Network Intrusion Detection System , 2022, IEEE Internet of Things Journal.

[16]  I. Alsmadi,et al.  A Deep Learning Ensemble Approach to Detecting Unknown Network Attacks , 2022, J. Inf. Secur. Appl..

[17]  Weizhe Zhang,et al.  DMAIDPS: a distributed multi-agent intrusion detection and prevention system for cloud IoT environments , 2022, Cluster Computing.

[18]  Nawab Muhammad Faseeh Qureshi,et al.  A highly secured intrusion detection system for IoT using EXPSO-STFA feature selection for LAANN to detect attacks , 2022, Cluster Computing.

[19]  A. Anwar,et al.  Dependable Intrusion Detection System for IoT: A Deep Transfer Learning Based Approach , 2022, IEEE Transactions on Industrial Informatics.

[20]  H. Janicke,et al.  Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning , 2022, IEEE Access.

[21]  Konstantinos Rantos,et al.  Deep Learning in IoT Intrusion Detection , 2021, Journal of Network and Systems Management.

[22]  Mohamed Amine Ferrag,et al.  Intrusion Detection in Critical Infrastructures: A Literature Review , 2021, Smart Cities.

[23]  Rima Asmar Awad,et al.  JTAG-based PLC memory acquisition framework for industrial control systems , 2021, Digit. Investig..

[24]  Meikang Qiu,et al.  Adversarial Attacks Against Network Intrusion Detection in IoT Systems , 2021, IEEE Internet of Things Journal.

[25]  M. Pushpalatha,et al.  WILS-TRS — a novel optimized deep learning based intrusion detection framework for IoT networks , 2021, Personal and Ubiquitous Computing.

[26]  Benjamin Turnbull,et al.  A Deep Blockchain Framework-Enabled Collaborative Intrusion Detection for Protecting IoT and Cloud Networks , 2021, IEEE Internet of Things Journal.

[27]  Mohamed Amine Ferrag,et al.  EASBF: An efficient authentication scheme over blockchain for fog computing-enabled internet of vehicles , 2021, J. Inf. Secur. Appl..

[28]  Zhaolong Ning,et al.  Intrusion Detection in Green Internet of Things: A Deep Deterministic Policy Gradient-Based Algorithm , 2021, IEEE Transactions on Green Communications and Networking.

[29]  Mohammad Mehdi Faghih,et al.  APAE: an IoT intrusion detection system using asymmetric parallel auto-encoder , 2021, Neural Computing and Applications.

[30]  R. Jurdak,et al.  Exploring Edge TPU for Network Intrusion Detection in IoT , 2021, J. Parallel Distributed Comput..

[31]  Ammar Alazab,et al.  A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges , 2021, Cybersecurity.

[32]  Xiaojiang Du,et al.  CorrAUC: A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine-Learning Techniques , 2021, IEEE Internet of Things Journal.

[33]  Mohamed Abdel-Basset,et al.  Semi-Supervised Spatiotemporal Deep Learning for Intrusions Detection in IoT Networks , 2021, IEEE Internet of Things Journal.

[34]  Qasem Abu Al-Haija,et al.  An Efficient Deep Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks , 2020, Electronics.

[35]  S. M. Riazul Islam,et al.  An Overview of UPnP-based IoT Security: Threats, Vulnerabilities, and Prospective Solutions , 2020, 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON).

[36]  Adnan Shahid Khan,et al.  Network intrusion detection system: A systematic study of machine learning and deep learning approaches , 2020, Trans. Emerg. Telecommun. Technol..

[37]  Zhaolong Ning,et al.  Data-Driven Intrusion Detection for Intelligent Internet of Vehicles: A Deep Convolutional Neural Network-Based Method , 2020, IEEE Transactions on Network Science and Engineering.

[38]  Nour Moustafa,et al.  A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions , 2020, Electronics.

[39]  Lei Shu,et al.  Security and Privacy for Green IoT-Based Agriculture: Review, Blockchain Solutions, and Challenges , 2020, IEEE Access.

[40]  Mohamed Amine Ferrag,et al.  Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study , 2020, J. Inf. Secur. Appl..

[41]  Zaffar Haider Janjua,et al.  Passban IDS: An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices , 2020, IEEE Internet of Things Journal.

[42]  Antonio Robles-Kelly,et al.  Deep Learning-Based Intrusion Detection for IoT Networks , 2019, 2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC).

[43]  Dandan Liu,et al.  DL-IDS: a deep learning-based intrusion detection framework for securing IoT , 2019, Trans. Emerg. Telecommun. Technol..

[44]  Mohamed Amine Ferrag,et al.  Teaching the process of building an Intrusion Detection System using data from a small‐scale SCADA testbed , 2019, Internet Technol. Lett..

[45]  Houbing Song,et al.  Security of the Internet of Things: Vulnerabilities, Attacks, and Countermeasures , 2019, IEEE Communications Surveys & Tutorials.

[46]  Ali A. Ghorbani,et al.  Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy , 2019, 2019 International Carnahan Conference on Security Technology (ICCST).

[47]  Nima Jafari Navimipour,et al.  Intrusion detection systems in the Internet of things: A comprehensive investigation , 2019, Comput. Networks.

[48]  Wenjuan Li,et al.  Designing collaborative blockchained signature-based intrusion detection in IoT environments , 2019, Future Gener. Comput. Syst..

[49]  M. Omair Shafiq,et al.  Analyzing Adversarial Attacks against Deep Learning for Intrusion Detection in IoT Networks , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[50]  Geethapriya Thamilarasu,et al.  Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things , 2019, Sensors.

[51]  Dimitri Konstantas,et al.  A Comprehensive Study of Security and Privacy Guidelines, Threats, and Countermeasures: An IoT Perspective , 2019, J. Sens. Actuator Networks.

[52]  Theophilus A. Benson,et al.  Detecting Volumetric Attacks on loT Devices via SDN-Based Monitoring of MUD Activity , 2019, SOSR.

[53]  João Paulo Papa,et al.  Internet of Things: A survey on machine learning-based intrusion detection approaches , 2019, Comput. Networks.

[54]  Ying Zhang,et al.  Intrusion Detection for IoT Based on Improved Genetic Algorithm and Deep Belief Network , 2019, IEEE Access.

[55]  Parvez Faruki,et al.  Network Intrusion Detection for IoT Security Based on Learning Techniques , 2019, IEEE Communications Surveys & Tutorials.

[56]  Wonjun Lee,et al.  Exploiting JTAG and Its Mitigation in IOT: A Survey , 2018, Future Internet.

[57]  Hesham F. A. Hamed,et al.  Intrusion detection systems for IoT-based smart environments: a survey , 2018, Journal of Cloud Computing.

[58]  Elena Sitnikova,et al.  Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset , 2018, Future Gener. Comput. Syst..

[59]  Marc-Oliver Pahl,et al.  All Eyes on You: Distributed Multi-Dimensional IoT Microservice Anomaly Detection , 2018, 2018 14th International Conference on Network and Service Management (CNSM).

[60]  B. B. Zaidan,et al.  A review of smart home applications based on Internet of Things , 2017, J. Netw. Comput. Appl..

[61]  Hyoungshick Kim,et al.  Security and Privacy Challenges in the Internet of Things [Security and Privacy Matters] , 2017, IEEE Consumer Electronics Magazine.

[62]  B. B. Gupta,et al.  Security in Internet of Things: issues, challenges, taxonomy, and architecture , 2017, Telecommunication Systems.

[63]  Sean Carlisto de Alvarenga,et al.  A survey of intrusion detection in Internet of Things , 2017, J. Netw. Comput. Appl..

[64]  Robert C. Atkinson,et al.  Threat analysis of IoT networks using artificial neural network intrusion detection system , 2016, 2016 International Symposium on Networks, Computers and Communications (ISNCC).

[65]  Nour Moustafa,et al.  UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set) , 2015, 2015 Military Communications and Information Systems Conference (MilCIS).

[66]  Aref Meddeb,et al.  A secure authentication mechanism for resource constrained devices , 2015, 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA).

[67]  M. Bhuyan,et al.  Network attacks: Taxonomy, tools and systems , 2014, J. Netw. Comput. Appl..

[68]  Mourad Debbabi,et al.  Cyber Scanning: A Comprehensive Survey , 2014, IEEE Communications Surveys & Tutorials.

[69]  Thiemo Voigt,et al.  SVELTE: Real-time intrusion detection in the Internet of Things , 2013, Ad Hoc Networks.

[70]  R.B. Ahmad,et al.  Detecting TCP SYN Flood Attack Based on Anomaly Detection , 2010, 2010 Second International Conference on Network Applications, Protocols and Services.

[71]  Ali A. Ghorbani,et al.  A detailed analysis of the KDD CUP 99 data set , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.

[72]  A. Perrig,et al.  The Sybil attack in sensor networks: analysis & defenses , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[73]  Ahsan Rafiq,et al.  Towards SDN-Enabled, Intelligent Intrusion Detection System for Internet of Things (IoT) , 2022, IEEE Access.

[74]  F. Taher,et al.  A Novel Tunicate Swarm Algorithm With Hybrid Deep Learning Enabled Attack Detection for Secure IoT Environment , 2022, IEEE Access.

[75]  Azizur Rahim,et al.  Protocol-Based Deep Intrusion Detection for DoS and DDoS Attacks Using UNSW-NB15 and Bot-IoT Data-Sets , 2022, IEEE Access.

[76]  Songfeng Lu,et al.  IoT Intrusion Detection System Using Deep Learning and Enhanced Transient Search Optimization , 2021, IEEE Access.

[77]  Ahmed A. Nashat,et al.  Intrusion Detection System Using Machine Learning for Vehicular Ad Hoc Networks Based on ToN-IoT Dataset , 2021, IEEE Access.

[78]  Q. Mahmoud,et al.  Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks , 2021, IEEE Access.

[79]  Zahir Tari,et al.  TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems , 2020, IEEE Access.

[80]  Ing-Ray Chen,et al.  Attack and Defense Strategies for Intrusion Detection in Autonomous Distributed IoT Systems , 2020, IEEE Access.

[81]  Ali A. Ghorbani,et al.  Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization , 2018, ICISSP.

[82]  B. Balamurugan,et al.  Security in Network Layer of IoT: Possible Measures to Preclude , 2017 .

[83]  Alina M. Chircu,et al.  Issues in Information Systems , 2016 .

[84]  Christoph P. Mayer.  Electronic Communications of the EASST Volume 17 ( 2009 ) Workshops der Wissenschaftlichen Konferenz Kommunikation in Verteilten Systemen 2009 ( WowKiVS 2009 ) Security and Privacy Challenges in the Internet of Things , 2008 .