Rogue Base Stations Detection for Advanced Metering Infrastructure Based on Signal Strength Clustering

The smart meters and meter collectors in Advanced Metering Infrastructure (AMI), which are installed in every home, rely on wireless Virtual Private Network (VPN) for communicating with Head End System (HES). Therefore, they are prone to suffer from malicious cyber-attack. Usually, based on General Packet Radio Service (GPRS) communicated method is the most popular for meter collectors and consequently they are vulnerable to rogue Base Stations (BS) and get compromised by malicious adversaries further. Thus a Density-based spatial clustering of applications with noise (DBSCAN) method is employed to filter rogue BSs out and prevent meter collectors from attaching to them, because there is a notable difference between Signal Strength (SS) profile of legitimate BSs and rogue BSs, Numerical simulation indicates that the proposed approach is capable of detecting both stationary and moving rogue BSs online within fixed time window effectively. Moreover, the method can be implemented in existing meter collectors with limited computation resource. In conclusion, the proposed approach can enhance the level of cyber security of meter collectors.

[1]  Taskin Koçak,et al.  Smart Grid Technologies: Communication Technologies and Standards , 2011, IEEE Transactions on Industrial Informatics.

[2]  Xianbin Wang,et al.  Green-RPL: An Energy-Efficient Protocol for Cognitive Radio Enabled AMI Network in Smart Grid , 2018, IEEE Access.

[3]  Christos Xenakis Malicious actions against the GPRS technology , 2006, Journal in Computer Virology.

[4]  Sujeet Shenoi,et al.  Security analysis of an advanced metering infrastructure , 2017, Int. J. Crit. Infrastructure Prot..

[5]  Byung-Seo Kim,et al.  Energy and Congestion-Aware Routing Metric for Smart Grid AMI Networks in Smart City , 2017, IEEE Access.

[6]  Elisa Bertino,et al.  LTEInspector: A Systematic Approach for Adversarial Testing of 4G LTE , 2018, NDSS.

[7]  Noureddine Boudriga,et al.  Defending against rogue base station attacks using wavelet based fingerprinting , 2009, 2009 IEEE/ACS International Conference on Computer Systems and Applications.

[8]  Ingmar Baumgart,et al.  Privacy-Aware Smart Metering: A Survey , 2014, IEEE Communications Surveys & Tutorials.

[9]  Zhenzhi Lin,et al.  Model-Free Data Authentication for Cyber Security in Power Systems , 2020, IEEE Transactions on Smart Grid.

[10]  H. Vincent Poor,et al.  BlackIoT: IoT Botnet of High Wattage Devices Can Disrupt the Power Grid , 2018, USENIX Security Symposium.

[11]  Bin Jiang,et al.  Clustering Uncertain Data Based on Probability Distribution Similarity , 2013, IEEE Transactions on Knowledge and Data Engineering.

[12]  Shiyan Hu,et al.  Preventive Maintenance for Advanced Metering Infrastructure Against Malware Propagation , 2016, IEEE Transactions on Smart Grid.

[13]  Qingsheng Zhu,et al.  An Effective Algorithm Based on Density Clustering Framework , 2017, IEEE Access.

[14]  Kan Chen,et al.  A Collaborative Intrusion Detection Mechanism Against False Data Injection Attack in Advanced Metering Infrastructure , 2015, IEEE Transactions on Smart Grid.

[15]  Thorsten Holz,et al.  Breaking LTE on Layer Two , 2019, 2019 IEEE Symposium on Security and Privacy (SP).

[16]  Imane Aly Saroit,et al.  Secure and privacy-preserving AMI-utility communications via LTE-A networks , 2015, 2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).

[17]  Utkarsh Seetha,et al.  Meter Data Acquisition System In PowerUtilities , 2013 .

[18]  Haibo He,et al.  Cyber-physical attacks and defences in the smart grid: a survey , 2016, IET Cyper-Phys. Syst.: Theory & Appl..

[19]  Martin Gilje Jaatun,et al.  GPRS Security for Smart Meters , 2013, CD-ARES.

[20]  Mohammed Arozullah,et al.  Detection and Remediation of Attack by Fake Base Stations in LTE Networks , 2015 .

[21]  John R. Williams,et al.  Data-Stream-Based Intrusion Detection System for Advanced Metering Infrastructure in Smart Grid: A Feasibility Study , 2015, IEEE Systems Journal.

[22]  Michael A. Temple,et al.  Improving ZigBee Device Network Authentication Using Ensemble Decision Tree Classifiers With Radio Frequency Distinct Native Attribute Fingerprinting , 2015, IEEE Transactions on Reliability.

[23]  Vinod Namboodiri,et al.  Toward a Secure Wireless-Based Home Area Network for Metering in Smart Grids , 2014, IEEE Systems Journal.

[24]  Hung-Min Sun,et al.  Eliminating rouge femtocells based on distance bounding protocol and geographic information , 2014, Expert Syst. Appl..