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Examining the Suitability of NetFlow Features in Detecting IoT Network Intrusions
| Content Provider | MDPI |
|---|---|
| Author | Awad, Mohammed Fraihat, Salam Salameh, Khouloud Redhaei, Aneesa Al |
| Copyright Year | 2022 |
| Description | The past few years have witnessed a substantial increase in cyberattacks on Internet of Things (IoT) devices and their networks. Such attacks pose a significant threat to organizational security and user privacy. Utilizing Machine Learning (ML) in Intrusion Detection Systems (NIDS) has proven advantageous in countering novel zero-day attacks. However, the performance of such systems relies on several factors, one of which is prediction time. Processing speed in anomaly-based NIDS depends on a few elements, including the number of features fed to the ML model. NetFlow, a networking industry-standard protocol, offers many features that can be used to predict malicious attacks accurately. This paper examines NetFlow features and assesses their suitability in classifying network traffic. Our paper presents a model that detects attacks with (98–100%) accuracy using as few as 13 features. This study was conducted using a large dataset of over 16 million records released in 2021. |
| Starting Page | 6164 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s22166164 |
| Journal | Sensors |
| Issue Number | 16 |
| Volume Number | 22 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-17 |
| Access Restriction | Open |
| Subject Keyword | Sensors Information and Library Science Internet of Things Cyber Security Network Intrusion Detection System Machine Learning Feature Selection |
| Content Type | Text |
| Resource Type | Article |
