期刊
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
卷 17, 期 1, 页码 45-59出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSM.2020.2966951
关键词
IoT; network; security; privacy; activity detection; anomaly detection; traffic classification
资金
- Academy of Finland [314008]
- Business Finland 5G-FORCE research project
- Doctoral Programme in Computer Sciences (DoCS) at University of Helsinki
IoT devices are notoriously vulnerable even to trivial attacks and can be easily compromised. In addition, resource constraints and heterogeneity of IoT devices make it impractical to secure IoT installations using traditional endpoint and network security solutions. To address this problem, we present IoT-Keeper, a lightweight system which secures the communication of IoT. IoT-Keeper uses our proposed anomaly detection technique to perform traffic analysis at edge gateways. It uses a combination of fuzzy C-means clustering and fuzzy interpolation scheme to analyze network traffic and detect malicious network activity. Once malicious activity is detected, IoT-Keeper automatically enforces network access restrictions against IoT device generating this activity, and prevents it from attacking other devices or services. We have evaluated IoT-Keeper using a comprehensive dataset, collected from a real-world testbed, containing popular IoT devices. Using this dataset, our proposed technique achieved high accuracy (approximate to 0.98) and low false positive rate (approximate to 0.02) for detecting malicious network activity. Our evaluation also shows that IoT-Keeper has low resource footprint, and it can detect and mitigate various network attacks-without requiring explicit attack signatures or sophisticated hardware.
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