GSM or Mobile based Home Automation System

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Context of the problem

The concept of Home Automation was a topic of interest in the Academic arena since the late 1970s, with time and advancement of technology people’s expectations about Home Automation and how they should access their home has dramatically changed. The affordability and popularity of electronic devices and internet were contributing factors to this change. The modern Home Automation System is a delicate balance of Ubiquitous Computing Devices and Wireless Sensor/Actor Networks.
The added expectations and ‘Convenience of Access’ has brought new security challenges to the Home Automation front. Nowadays most smart homes are connected to the internet; it is one of the main features which adds to the convenience of the smart home users. Connecting the home to the internet allows a home owner to control their home from anywhere in the world but it also opens up the system to attackers from around the globe who otherwise had to be in the range of the home’s wireless network to launch an attack. From an attacker’s perspective, compromising a home automation system from the comforts of their home is far more attractive than physically being near the house and trying to break in.
Most obvious way to improve security would be to deny access to a home over the internet, but that significantly inconveniences the home inhabitants and the way they access their home and services, this defeats the purpose of Home Automation Systems. So, securing access to a Home over the internet is a vital part in Home Automation Security. This could be established by limiting access to a home over the internet; Access should be limited to a fixed number of trusted people using a fixed number of trusted electronic devices. To achieve this, we have to identify the user as well as the device accessing the home over the internet.
Home automation networks present a new set of security challenges unlike other wireless sensor networks. Here, the attacker needs devices in the home automation network to respond to their commands in order to achieve their goals. So unlike other sensor networks, simply disrupting the network communication and preventing individual devices from communicating with each other or with the controller is not the type of attack that is expected in a smart home environment. Home automation networks are an attractive target for an intruder, as most of the existing defensive methods in wireless sensor networks mainly focus on network and routing level attacks.
Ideal way to improve home security and defend against intrusion is to recognize a home’s authorized inhabitants and identify their position inside a home at all times without inconveniencing its inhabitants. This is extremely challenging and complex, given the unpredictable nature of human behaviour and home being occupied by guests and other trusted people. Identifying access points to a home and regulating access to them is the next logical step towards securing a home. The work proposes that, normal user behaviour at access points to a home adhere to a set of predictable behaviours.
These user behaviours when analysed by our novel logical sensing algorithms can differentiate between normal and attack behaviours. The work later goes on to propose a novel behaviour prediction algorithm to identify legitimate users accessing the home in a timely manner to react to unauthorized access, thus improving home security with very little inconvenience to the user. Most advanced smart homes today are interested in predicting user behaviour inside the home to increase efficiency but little importance is given to incorporating logic or behaviour prediction into home security. Considering these issues and the ineffectiveness of the existing defensive techniques in the smart home environment, we propose a new defensive approach based on user behaviour and logic called Intelligent Home Automation Security System (IHASS).

TABLE OF CONTENTS :

• CHAPTER 1 INTRODUCTION
  • 1.1 PROBLEM STATEMENT
  • 1.1.1 Context of the problem
  • 1.1.2 Research gap
  • 1.2 RESEARCH OBJECTIVE AND QUESTIONS
  • 1.3 APPROACH
  • 1.4 RESEARCH GOALS
  • 1.5 RESEARCH CONTRIBUTION
  • 1.6 OVERVIEW OF STUDY
• CHAPTER 2 LITERATURE STUDY
  • 2.1 CHAPTER OBJECTIVES
  • 2.2 LITERATURE ON SMART HOME SECURITY
  • 2.2.1 Context Aware Home Automation Systems
  • 2.2.2 Central Controller Based Home Security System
  • 2.2.3 Bluetooth Based Home Automation System
  • 2.2.4 GSM or Mobile based Home Automation System
  • 2.2.5 SMS (Short Messaging Service) Based Home Automation System
  • 2.2.6 GPRS (General Packet Radio Service) Based Home Automation System
  • 2.2.7 DTMF (Dual Tone Multi Frequency) Based Home Automation System
  • 2.2.8 Internet Based Home Automation System
  • 2.2.9 Decentralized Approach to Home Automation Systems
  • 2.3 LITERATURE ON DEVICE FINGERPRINTING
  • 2.4 LITERATURE ON LOGICAL SENSING
  • 2.5 LITERATURE ON BEHAVIOUR PREDICTION
• CHAPTER 3 METHODOLOGY
  • 3.1 CHAPTER OBJECTIVES
  • 3.2 DEVICE FINGERPRINTING METHODOLOGY
  • 3.2.1 Parameters Considered for Device Fingerprinting
  • 3.2.2 Device Fingerprinting Process
  • 3.2.3 Device Fingerprint Algorithm
  • 3.3 LOGICAL SENSING METHODOLOGY
  • 3.3.1 Primary Access Point
  • 3.3.2 Secondary Access Points
  • 3.3.3 Fire Alarm
  • 3.4 BEHAVIOUR PREDICTION METHODOLOGY
  • 3.4.1 Time Parameters
  • 3.4.2 Light Behaviour
  • 3.4.3 Other User Behaviour
• CHAPTER 4 EXPERIMENT SETUP
  • 4.1 CHAPTER OBJECTIVES
  • 4.2 EXPERIMENT SETUP FOR DEVICE FINGERPRINTING
  • 4.3 EXPERIMENT SETUP AND HARDWARE DESIGN FOR LOGICAL SENSING AND BEHAVIOUR PREDICTION
• CHAPTER 5 RESULTS
  • 5.1 CHAPTER OBJECTIVES
  • 5.2 DEVICE FINGERPRINTING EXPERIMENT RESULT AND MATHAMATICAL MODELLING
  • 5.2.1 Result of the proposed device fingerprint algorithm test
  • 5.2.2 Result of the device fingerprint algorithm test
  • 5.3 LOGICAL SENSING EXPERIMENT RESULT
  • 5.4 MACHINE LEARNING FOR BEHAVIOUR PREDICTION, MATHAMATICAL MODELLING AND EXPERIMENT RESULT
  • 5.4.1 Machine Learning for Behaviour Prediction Algorithm
  • 5.4.2 Mathematical Modelling
  • 5.4.3 Behaviour Prediction Experiment Results
• CHAPTER 6 DISCUSSION
  • 6.1 CHAPTER OBJECTIVES
  • 6.2 DEVICE FINGERPRINTING RESULT DISCUSSION, COMPARISON AND FEATURES
  • 6.2.1 Device Fingerprinting Discussion
  • 6.2.2 Comparison of the Device Fingerprinting Algorithm
  • 6.2.3 Features of the proposed Device Fingerprinting Algorithm
  • 6.3 LOGICAL SENSING RESULT DISCUSSION, COMPARISON AND FEATURES
  • 6.3.1 Logical Sensing Discussion
  • 6.3.2 Features and Comparison of the proposed Logical Sensing Algorithm
  • 6.4 BEHAVIOUR PREDICTION RESULT DISCUSSION, COMPARISON AND FEATURES
  • 6.4.1 Behaviour Prediction Discussion
  • 6.4.2 Features and Comparison
• CHAPTER 7 CONCLUSION
  • REFERENCES

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