Home Security Systems — Technology Overview
An Internet of Things (IoT) device is a collection of hardware, software, network connectivity, and sensors that form a network of things. As a result, IoT device architecture consists of four primary components: sensor, network, data processing, and application layers.
Sensor-based threats in IoT devices can be divided into four major categories: 1) Information Leakage 2) False Sensor Data Injection 3) Transmitting Malicious Sensor Commands 4) Denial-of-Service (DoS)
Home security systems are networks of incorporated digital gadgets operating collectively with a relevant central control panel to guard against burglars and different ability domestic intruders. Doors, locks, alarm systems, lighting, motion detectors, and security camera systems are examples of security hardware. According to an FBI report, forcible entry was involved in 58.3 percent of burglaries in the United States. According to the most recent figures, the typical burglary takes 90 seconds to 12 minutes in the United States, and a burglar will break into a residence in 60 seconds on average. Most people go after cash first, then jewellery, drugs, and then electronics. Marie Van Brittan Brown was an African-American inventor who, with her husband Albert Brown, devised one of the first home security systems (US Patent 3,482,037) in 1966. They sought a patent together, and it was awarded in 1969.
Restraints in Home Security Systems Market Growth
The high cost of installation and maintenance of home security systems is projected to be a major stumbling block to the market’s expansion. Customers are becoming more aware of the advantages of home security systems, but they are wary of making investments because security system prices are still out of reach for many consumers. The cost of installation is increased by expensive hardware and consultation on how to operate these systems. Furthermore, the total cost of ownership will be higher because it will include maintenance fees, monitoring subscription fees, part replacement expenses, and installation costs. Third-party monitoring players charge monthly membership fees ranging from $150 to $1500. Furthermore, the cost of installing home security systems varies depending on the level of customization required to include extra features and services such as two-way communication, smart video surveillance (with AI and other advanced image sensors), and cellular backup. Furthermore, the advanced analytics software installed in home security systems is costly, which adds to the overall cost of security gadgets.
Architecture of IoT
The Internet of Things (IoT) is a network of interconnected physical devices that can communicate and share data without requiring human involvement. Integrating electronic hardware such as sensors, software, and networking gear inside any object in the physical world that can be given an IP address to facilitate data transfer across a network can become a component of the IoT system. Because IoT allows us to collect information from all kinds of mediums, such as humans, animals, vehicles, and household appliances, it has been explicitly defined as an “Infrastructure of Information Society.”
An Internet of Things (IoT) device is a collection of hardware, software, network connectivity, and sensors that form a network of things. As a result, IoT device architecture consists of four primary components: sensor, network, data processing, and application layers. Below is a thorough description of these layers:
1. Sensing Layer: The sensing layer’s main goal is to detect any anomalies in the device’s peripheral and collect data from the actual world. This layer is made up of a number of sensors. One of the most important aspects of IoT devices is the ability to use several sensors for different applications. Sensor hubs are commonly used to connect sensors in IoT devices. A sensor hub is a single point of connection for several sensors that collect and transfer sensor data to the device’s processing unit. For data transfer between sensors and applications, a sensor hub employs numerous transport technologies (Inter-Integrated Circuit (I2C) or Serial Peripheral Interface (SPI)). These transport mechanisms rely on IoT devices to acquire sensor data by establishing a communication channel between sensors and applications. Sensors in IoT devices are divided into three groups, as outlined below:
- Motion Sensors: The orientation of the devices, as well as the change in motion, are measured by motion sensors. In a device, there are two sorts of motions that can be observed: linear and angular motions. The linear motion of an IoT device refers to its linear displacement, whereas the angular motion refers to its rotating displacement.
- Environmental Sensors: Light sensors, pressure sensors, and other sensors are incorporated in IoT devices to detect changes in ambient conditions in the device’s peripheral. Environmental sensors in IoT devices are primarily used to assist devices in making autonomous decisions in response to changes in the device’s periphery. Environment sensors, for example, are utilized in a variety of applications to improve the user experience (e.g., home automation systems, smart locks, smart lights, etc.).
- Position Sensors: IoT device position sensors track the device’s physical position and location. Magnetic sensors and Global Positioning System (GPS) sensors are the most prevalent position sensors used in IoT devices. Magnetic sensors are commonly utilized as a digital compass to help with device display orientation. In IoT devices, on the other hand, GPS is employed for navigation.
2. Network Layer: The network layer serves as a conduit for data acquired in the sensing layer to be sent to other connected devices. The network layer of IoT devices is implemented utilizing a variety of communication technologies (e.g., Wi-Fi, Bluetooth, Zigbee, Z-Wave, LoRa, cellular network, etc.) to allow data to flow between devices on the same network.
3. Data Processing Layer: The core data processing unit of IoT devices makes up the data processing layer. The data processing layer analyses the data acquired in the sensing layer and makes decisions based on the findings. The data processing layer in various IoT devices (e.g., smartwatches, smart home hubs, etc.) also saves the results of earlier analyses to improve the user experience. The network layer allows this layer to exchange the results of data processing with other linked devices.
4. Application Layer: To accomplish various applications of IoT devices, the application layer implements and delivers the results of the data processing layer. The application layer is a user-centric layer that performs a variety of functions for users. Smart transportation, smart homes, personal care, and healthcare are just a few examples of IoT applications.
Sensor-Based Threats to IoT Devices
Sensor-based threats are harmful behaviours that strive to achieve their destructive goals by abusing sensors, either passively or actively. Sensor-based attacks can be passive, such as watching the device’s activity without interfering with its usual operations, or active, such as injecting fake sensor data or uploading malicious sensor code to the device.
Furthermore, based on the goal and type of the threats, sensor-based threats in IoT devices can be divided into four major categories:
1. Information Leakage: Information leakage is the most common sensor-based threat in the context of IoT devices and applications. Sensors on IoT devices can reveal sensitive data like passwords, secret keys of a cryptographic system, credit card information, etc. This information can be used directly to violate user privacy or to build a database for future attacks. Only one sensor can be enough for information leakage (e.g., eavesdropping using microphone), or multiple sensors can be exploited to create a more complex attack (e.g., keystroke inference using gyroscope and audio sensors). In general, information leakage can be accomplished for (1) keystroke inference, (2) task inference, (3) location inference, or (4) eavesdropping.
- Task inference: In an IoT device, task inference is a sort of attack that provides information about an ongoing task or application. Task inference discloses information about the status of the device, and attackers might use this information to start an attack without triggering the device’s security measures. Sensors on IoT devices exhibit a variation in readings for various processes that are running on the devices. This difference in reading can be utilized to deduce the device’s operating process and application.
- Location Inference: A new location-privacy attack based on acoustic side-channels was devised by the researchers. The assault is based on acoustic data embedded in foreground noise transmitted in a close context (e.g., a conference room). The researchers looked at how encrypted messaging clients in voice-call mode can be exploited to establish a location fingerprint using audio. The assault relies on a pattern of acoustic reflections of the human voice at the user’s location rather than any specific background sounds. The attack can be used to violate the location privacy of participants in an anonymous VoIP session, a Field-programmable gate array, or even confirm attacks that check if two audio recordings came from the same area regardless of the speakers.
- Keystroke Inference: On IoT devices, keystroke inference is a common threat. The touchscreen, touchpad, and keyboard (external or built-in virtual or real) are used by the majority of IoT devices. When a user types or inputs data into a device, the device tilts and twists, causing data captured by sensors (e.g., accelerometer, gyroscope, microphone, light sensor, and so on) to deviate. In an IoT device, these discrepancies in sensor data can be utilized to infer keystrokes. Keystroke inference can be done on the device or on a nearby device utilizing an IoT device’s sensors.
- Eavesdropping: Many IoT devices employ audio sensors to make calls, record audio messages, and respond to voice commands, among other things. Eavesdropping is a sort of attack in which a malicious programme secretly captures a conversation using audio sensors and extracts information from it. An attacker can either save the recorded chat to a device or listen to it live.
2. Transmitting Malicious Sensor Commands: Sensors in IoT devices can be exploited to send malicious sensor patterns or trigger commands, which can be used to launch malware that has been placed in a victim’s device. Sensors can be used to open up new communication channels between device peripherals. These channels can be used to alter crucial sensor characteristics (such as device motion, light intensity, magnetic field, and so on) or to send malicious orders.
3. False Sensor Data Injection: The data generated by sensors on IoT devices is critical to the devices’ applications. One can control the applications of IoT devices by manipulating sensor data. False sensor data injection is a type of attack in which the sensor data utilized in IoT applications is fabricated or modified for malicious purposes. False sensor data can be fed into devices through physical access or covert use of various communication media (Bluetooth, Wi-Fi, cellular network, etc.). Furthermore, IoT device sensors can be utilized to change data written or stored on the devices.
4. Denial-of-Service (DoS): By definition, a denial-of-service (DoS) attack is one in which the usual functionality of a device or application is purposefully denied. Active DoS assaults, in which an application or task is forcibly refused, or passive DoS attacks, in which one application is used to interrupt another on-going task on the device.
The term “smart house” refers to a subset of the Internet of Things (IoT) paradigm that intends to combine home automation and security. When devices in a typical household are connected to the Internet, homeowners can monitor and manage them from afar. Smart homes have established a niche in the consumer market, ranging from timer-controlled lamps that turn off at a specified time of day to smart thermostats that adjust temperatures in a home and generate thorough data on energy usage. The widespread availability of low-cost smartphones, microcontrollers, and other open-source hardware, as well as the growing use of cloud services, has enabled the development of low-cost smart home security systems.
Opportunities and Possible Technology Enhancements in Home Security System
According to Statista, the demand for home security systems in the US has been increasing consistently, and revenue generated is estimated to be 8.2 billion dollars in the year 2025. According to Technavio, market growth will accelerate at a CAGR of 17.07%. The growth estimated from 2020 to 2025 is estimated to be around 13.31 billion dollars, of which 33% is predicted to be contributed by North America alone. This growth is being driven by the emerging Internet of Things (IoT) and wireless technologies for home security systems.
The DeepinView Camera Series, for example, was introduced by Hangzhou Hikvision Digital Technology (China), and it uses deep learning to provide trustworthy and consistent video content analytics (VCA) results. Artificial intelligence (AI) and deep learning are used in today’s surveillance cameras to boost security’s dependability and accuracy. As a result of the advent of the Internet of Things, miniaturized sensors and actuators, as well as low-power wireless communication technologies, have evolved. Recent advancements in smart sensing and actuation devices, as well as essential communication technologies such as Bluetooth Low Energy (BLE), ZigBee, and ANT, have made IoT integration in home security systems easier.
A smart home security system can improve your house’s security and provide you with peace of mind. However, by combining different smart systems, you can do a variety of functions remotely, including managing appliances and controlling lights, doors, and window coverings. Indeed, as technology advances and becomes more integrated into our daily lives, people are beginning to embrace a culture of convenience in which smart home technologies are connected, programmable, and controllable through a smartphone app.