Sample Selected Extreme Learning Machine Based Intrusion Detection in Fog Computing and MEC
Fog computing, as a new paradigm, has many characteristics that are different from cloud computing. Due to the resources being limited, fog nodes/MEC hosts are vulnerable to cyberattacks. Lightweight intrusion detection system (IDS) is a key technique to solve the problem. Because extreme learning m...
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| Veröffentlicht in: | Wireless communications and mobile computing 2018-01, Vol.2018 (2018), p.1-10 |
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| container_issue | 2018 |
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| container_title | Wireless communications and mobile computing |
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| creator | Lin, Fuhong Lü, Xing Zhou, Xianwei An, Xingshuo Yang, Lei |
| description | Fog computing, as a new paradigm, has many characteristics that are different from cloud computing. Due to the resources being limited, fog nodes/MEC hosts are vulnerable to cyberattacks. Lightweight intrusion detection system (IDS) is a key technique to solve the problem. Because extreme learning machine (ELM) has the characteristics of fast training speed and good generalization ability, we present a new lightweight IDS called sample selected extreme learning machine (SS-ELM). The reason why we propose “sample selected extreme learning machine” is that fog nodes/MEC hosts do not have the ability to store extremely large amounts of training data sets. Accordingly, they are stored, computed, and sampled by the cloud servers. Then, the selected sample is given to the fog nodes/MEC hosts for training. This design can bring down the training time and increase the detection accuracy. Experimental simulation verifies that SS-ELM performs well in intrusion detection in terms of accuracy, training time, and the receiver operating characteristic (ROC) value. |
| doi_str_mv | 10.1155/2018/7472095 |
| format | Article |
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Due to the resources being limited, fog nodes/MEC hosts are vulnerable to cyberattacks. Lightweight intrusion detection system (IDS) is a key technique to solve the problem. Because extreme learning machine (ELM) has the characteristics of fast training speed and good generalization ability, we present a new lightweight IDS called sample selected extreme learning machine (SS-ELM). The reason why we propose “sample selected extreme learning machine” is that fog nodes/MEC hosts do not have the ability to store extremely large amounts of training data sets. Accordingly, they are stored, computed, and sampled by the cloud servers. Then, the selected sample is given to the fog nodes/MEC hosts for training. This design can bring down the training time and increase the detection accuracy. Experimental simulation verifies that SS-ELM performs well in intrusion detection in terms of accuracy, training time, and the receiver operating characteristic (ROC) value.</description><identifier>ISSN: 1530-8669</identifier><identifier>EISSN: 1530-8677</identifier><identifier>DOI: 10.1155/2018/7472095</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Access control ; Algorithms ; Alliances ; Artificial neural networks ; Cloud computing ; Computer simulation ; Forensic sciences ; Internet of Things ; Intrusion detection systems ; Lightweight ; Machine learning ; Neural networks ; Nodes ; Privacy ; Security management ; Smartphones ; Support vector machines ; Training ; Unmanned aerial vehicles</subject><ispartof>Wireless communications and mobile computing, 2018-01, Vol.2018 (2018), p.1-10</ispartof><rights>Copyright © 2018 Xingshuo An et al.</rights><rights>Copyright © 2018 Xingshuo An et al. 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Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c360t-9577203007e2cc0031418fbd3dafb065cbb4ea3e197673664a254cb26a5bdadd3</citedby><cites>FETCH-LOGICAL-c360t-9577203007e2cc0031418fbd3dafb065cbb4ea3e197673664a254cb26a5bdadd3</cites><orcidid>0000-0002-5287-5226</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><contributor>Wang, Shangguang</contributor><contributor>Shangguang Wang</contributor><creatorcontrib>Lin, Fuhong</creatorcontrib><creatorcontrib>Lü, Xing</creatorcontrib><creatorcontrib>Zhou, Xianwei</creatorcontrib><creatorcontrib>An, Xingshuo</creatorcontrib><creatorcontrib>Yang, Lei</creatorcontrib><title>Sample Selected Extreme Learning Machine Based Intrusion Detection in Fog Computing and MEC</title><title>Wireless communications and mobile computing</title><description>Fog computing, as a new paradigm, has many characteristics that are different from cloud computing. 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Due to the resources being limited, fog nodes/MEC hosts are vulnerable to cyberattacks. Lightweight intrusion detection system (IDS) is a key technique to solve the problem. Because extreme learning machine (ELM) has the characteristics of fast training speed and good generalization ability, we present a new lightweight IDS called sample selected extreme learning machine (SS-ELM). The reason why we propose “sample selected extreme learning machine” is that fog nodes/MEC hosts do not have the ability to store extremely large amounts of training data sets. Accordingly, they are stored, computed, and sampled by the cloud servers. Then, the selected sample is given to the fog nodes/MEC hosts for training. This design can bring down the training time and increase the detection accuracy. 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| source | Wiley-Blackwell Open Access Titles; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Alma/SFX Local Collection |
| subjects | Access control Algorithms Alliances Artificial neural networks Cloud computing Computer simulation Forensic sciences Internet of Things Intrusion detection systems Lightweight Machine learning Neural networks Nodes Privacy Security management Smartphones Support vector machines Training Unmanned aerial vehicles |
| title | Sample Selected Extreme Learning Machine Based Intrusion Detection in Fog Computing and MEC |
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