Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things
Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end us...
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| Vydáno v: | Sensors (Basel, Switzerland) Ročník 19; číslo 9; s. 1977 |
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| Jazyk: | angličtina |
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27.04.2019
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| ISSN: | 1424-8220, 1424-8220 |
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| Abstract | Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively. |
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| AbstractList | Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively. Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively.Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively. |
| Author | Chawla, Shiven Thamilarasu, Geethapriya |
| AuthorAffiliation | School of STEM, University of Washington Bothell, Bothell, WA 98011, USA; chawls@uw.edu |
| AuthorAffiliation_xml | – name: School of STEM, University of Washington Bothell, Bothell, WA 98011, USA; chawls@uw.edu |
| Author_xml | – sequence: 1 givenname: Geethapriya surname: Thamilarasu fullname: Thamilarasu, Geethapriya – sequence: 2 givenname: Shiven orcidid: 0000-0002-0583-6494 surname: Chawla fullname: Chawla, Shiven |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31035611$$D View this record in MEDLINE/PubMed |
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| Keywords | Intrusion-Detection System (IDS) deep learning Internet of Things (IoT) security machine learning |
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| StartPage | 1977 |
| SubjectTerms | Algorithms Deep learning International conferences Internet of Things Internet of Things (IoT) Intrusion detection systems Intrusion-Detection System (IDS) machine learning Malware Privacy security Security systems Wireless networks |
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| Title | Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things |
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