Network Anomaly Detection inside Consumer Networks—A Hybrid Approach
With an increasing number of Internet of Things (IoT) devices in the digital world, the attack surface for consumer networks has been increasing exponentially. Most of the compromised devices are used as zombies for attacks such as Distributed Denial of Services (DDoS). Consumer networks, unlike mos...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/9/6/923 |
| _version_ | 1797566379521474560 |
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| author | Darsh Patel Kathiravan Srinivasan Chuan-Yu Chang Takshi Gupta Aman Kataria |
| author_facet | Darsh Patel Kathiravan Srinivasan Chuan-Yu Chang Takshi Gupta Aman Kataria |
| author_sort | Darsh Patel |
| collection | DOAJ |
| description | With an increasing number of Internet of Things (IoT) devices in the digital world, the attack surface for consumer networks has been increasing exponentially. Most of the compromised devices are used as zombies for attacks such as Distributed Denial of Services (DDoS). Consumer networks, unlike most commercial networks, lack the infrastructure such as managed switches and firewalls to easily monitor and block undesired network traffic. To counter such a problem with limited resources, this article proposes a hybrid anomaly detection approach that detects irregularities in the network traffic implicating compromised devices by using only elementary network information like Packet Size, Source, and Destination Ports, Time between subsequent packets, Transmission Control Protocol (TCP) Flags, etc. Essential features can be extracted from the available data, which can further be used to detect zero-day attacks. The paper also provides the taxonomy of various approaches to classify anomalies and description on capturing network packets inside consumer networks. |
| first_indexed | 2024-03-10T19:26:56Z |
| format | Article |
| id | doaj.art-929594d342f5454389dae24f276ce373 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T19:26:56Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-929594d342f5454389dae24f276ce3732023-11-20T02:31:32ZengMDPI AGElectronics2079-92922020-06-019692310.3390/electronics9060923Network Anomaly Detection inside Consumer Networks—A Hybrid ApproachDarsh Patel0Kathiravan Srinivasan1Chuan-Yu Chang2Takshi Gupta3Aman Kataria4School of Computing Science and Engineering, Vellore Institute of Technology, Vellore 632014, IndiaSchool of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, IndiaDepartment of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 64002, TaiwanInformation Security Engineering, Soonchunhyang University, Asan-si 31538, KoreaDepartment of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, IndiaWith an increasing number of Internet of Things (IoT) devices in the digital world, the attack surface for consumer networks has been increasing exponentially. Most of the compromised devices are used as zombies for attacks such as Distributed Denial of Services (DDoS). Consumer networks, unlike most commercial networks, lack the infrastructure such as managed switches and firewalls to easily monitor and block undesired network traffic. To counter such a problem with limited resources, this article proposes a hybrid anomaly detection approach that detects irregularities in the network traffic implicating compromised devices by using only elementary network information like Packet Size, Source, and Destination Ports, Time between subsequent packets, Transmission Control Protocol (TCP) Flags, etc. Essential features can be extracted from the available data, which can further be used to detect zero-day attacks. The paper also provides the taxonomy of various approaches to classify anomalies and description on capturing network packets inside consumer networks.https://www.mdpi.com/2079-9292/9/6/923data mininganomaly detectionmachine learningIoT |
| spellingShingle | Darsh Patel Kathiravan Srinivasan Chuan-Yu Chang Takshi Gupta Aman Kataria Network Anomaly Detection inside Consumer Networks—A Hybrid Approach Electronics data mining anomaly detection machine learning IoT |
| title | Network Anomaly Detection inside Consumer Networks—A Hybrid Approach |
| title_full | Network Anomaly Detection inside Consumer Networks—A Hybrid Approach |
| title_fullStr | Network Anomaly Detection inside Consumer Networks—A Hybrid Approach |
| title_full_unstemmed | Network Anomaly Detection inside Consumer Networks—A Hybrid Approach |
| title_short | Network Anomaly Detection inside Consumer Networks—A Hybrid Approach |
| title_sort | network anomaly detection inside consumer networks a hybrid approach |
| topic | data mining anomaly detection machine learning IoT |
| url | https://www.mdpi.com/2079-9292/9/6/923 |
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