Deep learning for cyber threat detection in IoT networks: A review
The Internet of Things (IoT) has revolutionized modern tech with interconnected smart devices. While these innovations offer unprecedented opportunities, they also introduce complex security challenges. Cybersecurity is a pivotal concern for intrusion detection systems (IDS). Deep Learning has shown...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | Internet of Things and Cyber-Physical Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667345223000512 |
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| author | Alyazia Aldhaheri Fatima Alwahedi Mohamed Amine Ferrag Ammar Battah |
| author_facet | Alyazia Aldhaheri Fatima Alwahedi Mohamed Amine Ferrag Ammar Battah |
| author_sort | Alyazia Aldhaheri |
| collection | DOAJ |
| description | The Internet of Things (IoT) has revolutionized modern tech with interconnected smart devices. While these innovations offer unprecedented opportunities, they also introduce complex security challenges. Cybersecurity is a pivotal concern for intrusion detection systems (IDS). Deep Learning has shown promise in effectively detecting and preventing cyberattacks on IoT devices. Although IDS is vital for safeguarding sensitive information by identifying and mitigating suspicious activities, conventional IDS solutions grapple with challenges in the IoT context. This paper delves into the cutting-edge intrusion detection methods for IoT security, anchored in Deep Learning. We review recent advancements in IDS for IoT, highlighting the underlying deep learning algorithms, associated datasets, types of attacks, and evaluation metrics. Further, we discuss the challenges faced in deploying Deep Learning for IoT security and suggest potential areas for future research. This survey will guide researchers and industry experts in adopting Deep Learning techniques in IoT security and intrusion detection. |
| format | Article |
| id | doaj-art-b11bbe920e174505ab6491867e0cc0dd |
| institution | Kabale University |
| issn | 2667-3452 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Internet of Things and Cyber-Physical Systems |
| spelling | doaj-art-b11bbe920e174505ab6491867e0cc0dd2025-01-27T04:22:34ZengKeAi Communications Co., Ltd.Internet of Things and Cyber-Physical Systems2667-34522024-01-014110128Deep learning for cyber threat detection in IoT networks: A reviewAlyazia Aldhaheri0Fatima Alwahedi1Mohamed Amine Ferrag2Ammar Battah3Technology Innovation Institute, 9639, Masdar City, Abu Dhabi, United Arab EmiratesTechnology Innovation Institute, 9639, Masdar City, Abu Dhabi, United Arab EmiratesCorresponding author.; Technology Innovation Institute, 9639, Masdar City, Abu Dhabi, United Arab EmiratesTechnology Innovation Institute, 9639, Masdar City, Abu Dhabi, United Arab EmiratesThe Internet of Things (IoT) has revolutionized modern tech with interconnected smart devices. While these innovations offer unprecedented opportunities, they also introduce complex security challenges. Cybersecurity is a pivotal concern for intrusion detection systems (IDS). Deep Learning has shown promise in effectively detecting and preventing cyberattacks on IoT devices. Although IDS is vital for safeguarding sensitive information by identifying and mitigating suspicious activities, conventional IDS solutions grapple with challenges in the IoT context. This paper delves into the cutting-edge intrusion detection methods for IoT security, anchored in Deep Learning. We review recent advancements in IDS for IoT, highlighting the underlying deep learning algorithms, associated datasets, types of attacks, and evaluation metrics. Further, we discuss the challenges faced in deploying Deep Learning for IoT security and suggest potential areas for future research. This survey will guide researchers and industry experts in adopting Deep Learning techniques in IoT security and intrusion detection.http://www.sciencedirect.com/science/article/pii/S2667345223000512Cyber threatsDeep learningIntrusion detectionIoTMachine learning |
| spellingShingle | Alyazia Aldhaheri Fatima Alwahedi Mohamed Amine Ferrag Ammar Battah Deep learning for cyber threat detection in IoT networks: A review Internet of Things and Cyber-Physical Systems Cyber threats Deep learning Intrusion detection IoT Machine learning |
| title | Deep learning for cyber threat detection in IoT networks: A review |
| title_full | Deep learning for cyber threat detection in IoT networks: A review |
| title_fullStr | Deep learning for cyber threat detection in IoT networks: A review |
| title_full_unstemmed | Deep learning for cyber threat detection in IoT networks: A review |
| title_short | Deep learning for cyber threat detection in IoT networks: A review |
| title_sort | deep learning for cyber threat detection in iot networks a review |
| topic | Cyber threats Deep learning Intrusion detection IoT Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2667345223000512 |
| work_keys_str_mv | AT alyaziaaldhaheri deeplearningforcyberthreatdetectioniniotnetworksareview AT fatimaalwahedi deeplearningforcyberthreatdetectioniniotnetworksareview AT mohamedamineferrag deeplearningforcyberthreatdetectioniniotnetworksareview AT ammarbattah deeplearningforcyberthreatdetectioniniotnetworksareview |