ENFORCING SECURITY POLICIES WITH DATA PROVENANCE TO ENRICH THE SECURITY OF IOT/ SMART BUILDING SYSTEM

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Farooq, A. A. (2020). ENFORCING SECURITY POLICIES WITH DATA PROVENANCE TO ENRICH THE SECURITY OF IOT/ SMART BUILDING SYSTEM. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Smart Building Management Systems is rapidly growing worldwide mainly to reduceenergy consumption and carbon footprints. Some other benefits that can be achievedthrough this system include lowering operational costs and increasing occupant’s comfort,safety, and increased productivity. Programmable Logic Controllers (PLCs) areused widely for automating smart buildings. The vulnerabilities and attack surfacesof PLCs enable an attacker to control the smart building in order to cause more energyusage, target specific people, and destroy assets. This thesis summarizes the vulnerabilities,threats, and attacks for PLC-based systems. Moreover, the current stateof the art of static and dynamic analysis, threat and attack detection, automation,and conflict analysis of smart buildings are discussed. This thesis aims at detecting,analyzing, and mitigating the attack surfaces that are possible for smart buildings.A formal methods approach is proposed for detecting safety and security propertyviolations for smart buildings. Then, a rule-based method is developed to detect violationsleveraging provenance data collected from the system. These safety violatingincidences are mapped to corresponding variables in a PLC source program. Finally,we implement defeasible reasoning that enforces safety properties in the system. Weverify that not only does the new PLC program adhere to the safety properties thathave been instrumented, but also it does not trigger any new safety property violations.Finally, we outline new directions to be investigated in the future.

Details
Author

Farooq, Abdullah Al

Title

ENFORCING SECURITY POLICIES WITH DATA PROVENANCE TO ENRICH THE SECURITY OF IOT/ SMART BUILDING SYSTEM

Physical Description

1 online resource (134 pages) : PDF

Date

2020

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Smart Building Management Systems is rapidly growing worldwide mainly to reduceenergy consumption and carbon footprints. Some other benefits that can be achievedthrough this system include lowering operational costs and increasing occupant’s comfort,safety, and increased productivity. Programmable Logic Controllers (PLCs) areused widely for automating smart buildings. The vulnerabilities and attack surfacesof PLCs enable an attacker to control the smart building in order to cause more energyusage, target specific people, and destroy assets. This thesis summarizes the vulnerabilities,threats, and attacks for PLC-based systems. Moreover, the current stateof the art of static and dynamic analysis, threat and attack detection, automation,and conflict analysis of smart buildings are discussed. This thesis aims at detecting,analyzing, and mitigating the attack surfaces that are possible for smart buildings.A formal methods approach is proposed for detecting safety and security propertyviolations for smart buildings. Then, a rule-based method is developed to detect violationsleveraging provenance data collected from the system. These safety violatingincidences are mapped to corresponding variables in a PLC source program. Finally,we implement defeasible reasoning that enforces safety properties in the system. Weverify that not only does the new PLC program adhere to the safety properties thathave been instrumented, but also it does not trigger any new safety property violations.Finally, we outline new directions to be investigated in the future.

Genre

doctoral dissertations

Subjects--Topics

Computer science

Degree

Ph.D.

Keywords

Cybersecurity
Data Provenance
Defeasible Reasoning
Formal Method
PLC Security

Subject Area

Computer Science

Advisor(s)

Moyer, Thomas

Committee Members

Wang, Weichao
Lipford, Heather
Dai, Dong
Cukic, Bojan

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2020.

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Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Farooq_uncc_0694D_12530

Permalink

http://hdl.handle.net/20.500.13093/etd:2053