Description:Field of the Invention
[0001] This invention pertains to the cybersecurity of Internet of Things
(IoT) devices within smart home environments, focusing on adaptive
security measures. The unique nature of IoT devices, characterized by
their interconnectedness and diversity, presents significant security
challenges. Many IoT devices lack robust built-in security features,
making them vulnerable to cyber threats. This invention addresses these
challenges by leveraging a combination of cloud and fog computing layers
to create a comprehensive and adaptive security protocol, specifically
designed to safeguard smart home networks against evolving threats.
[0002] Central to this invention is the use of a cloud-fog architecture,
which balances computational efficiency and security effectiveness. The
cloud layer handles centralized data processing and long-term analytics,
enabling the development of predictive models for threat detection.
Meanwhile, the fog layer provides real-time threat detection and response
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capabilities, operating closer to the IoT devices. This layered approach
ensures low latency and high responsiveness, allowing the system to
dynamically adjust its security measures based on continuous data
analysis and machine learning insights. Through this innovative method,
the invention provides a scalable and adaptive security solution tailored to
the specific needs of smart home IoT environments.
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Background
[0003] The proliferation of Internet of Things (IoT) devices has
revolutionized modern living, with smart homes epitomizing the
integration of technology into everyday life. These devices, ranging from
smart thermostats and security cameras to intelligent lighting systems
and connected appliances, offer unparalleled convenience, efficiency, and
automation. However, this rapid adoption of IoT technology has also
introduced significant security vulnerabilities. IoT devices often operate
with minimal security, making them attractive targets for cybercriminals.
As smart homes become more interconnected, the potential for security
breaches increases, necessitating the development of robust security
solutions.
[0004] Traditional cybersecurity measures are often inadequate for IoT
environments due to the unique characteristics of these devices. IoT
devices typically have limited computational power, memory, and battery
life, which constrains the implementation of conventional security
protocols. Additionally, the diverse range of devices and manufacturers
results in a lack of standardization, further complicating the development
of universal security measures. These limitations highlight the need for an
innovative approach to IoT security that can adapt to the dynamic and
resource-constrained nature of smart home ecosystems.
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[0005] Cloud computing has emerged as a powerful tool for addressing
some of these challenges, offering extensive computational resources and
storage capabilities. By offloading data processing and analysis to the
cloud, it is possible to implement more sophisticated security measures
than what would be feasible on individual IoT devices. However, relying
solely on cloud-based solutions can introduce latency issues and may not
provide the real-time responsiveness required for effective threat
mitigation. This has led to the exploration of hybrid architectures that
combine the strengths of cloud computing with edge or fog computing,
which operates closer to the data source.
[0006] Fog computing, positioned between the cloud and IoT devices,
provides a middle layer that enhances real-time data processing and
immediate threat response. By distributing computational tasks between
the cloud and fog layers, it is possible to achieve a balance between
efficiency and responsiveness. This hybrid approach leverages the
extensive analytical capabilities of the cloud while utilizing the low-latency
advantages of fog computing. The proposed adaptive security protocol
utilizes this cloud-fog architecture to continuously monitor, detect, and
respond to threats in real-time. By incorporating machine learning and
predictive analytics, the system can dynamically adjust its security
measures, providing an adaptive and resilient defense mechanism tailored
to the evolving threat landscape of smart home IoT environments.
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[0007] US20190348247A1 This patent describes a system and method
for providing adaptive security to IoT devices. It leverages machine
learning to analyze data patterns and detect anomalies, updating security
protocols in real-time based on detected threats. The system uses a
combination of cloud and edge computing to optimize data processing and
threat response times. The system continuously monitors the behavior of
IoT devices, collecting data on network traffic, device activity, and
environmental factors. Machine learning models analyze this data to
identify deviations from normal patterns, indicating potential security
threats. The cloud layer performs extensive data analysis and model
training, while the edge (or fog) layer handles real-time threat detection
and response. Security protocols are dynamically adjusted based on the
latest threat intelligence.
[0008] US20190348247A1 This patent covers a cloud-based security
management system designed for IoT networks. It includes mechanisms
for monitoring IoT devices, analyzing security data in the cloud, and
deploying security updates to devices. The system aims to enhance the
security of IoT devices through centralized control and adaptive threat
response. The system involves a cloud-based security platform that
aggregates data from various IoT devices across the network. The cloud
platform uses big data analytics and machine learning to detect security
threats and anomalies. When a threat is identified, the system can push
security updates or reconfigure devices in real-time to mitigate the threat.
The fog layer provides localized processing to ensure low-latency
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responses and reduce the load on the cloud infrastructure.
[0009] IN202041030902A: This Indian patent proposes a system and
method for securing communication between IoT devices using a
combination of cloud and fog computing. The system ensures that data
transmitted between devices is encrypted and monitored for potential
threats. The fog layer provides real-time encryption and decryption of
data transmitted between IoT devices, ensuring secure communication
within the local network. The cloud layer oversees the overall security
management, analyzing communication patterns for anomalies and
potential threats. Machine learning models in the cloud are used to predict
and identify sophisticated attacks, updating encryption protocols and
security measures dynamically , Claims:We Claim:
[1] The proposed adaptive security algorithm significantly improves threat
detection accuracy for smart home IoT devices by leveraging machine
learning models and predictive analytics. This ensures timely identification
and mitigation of potential security threats.
[2] By balancing the computational load between cloud and fog computing
layers, the system efficiently utilizes available resources, resulting in
reduced latency and lower computational overhead. This optimization is
crucial for real-time threat detection and response in smart home
environments.
[3] The adaptive security protocol continuously updates its threat detection
models based on new data, ensuring that the system remains effective
against evolving cyber threats. This continuous learning capability allows
the system to adapt to new attack vectors and improve its defense
mechanisms over time.
[4] The combination of cloud and fog computing layers provides
comprehensive security coverage for smart home IoT devices. While the
cloud layer handles centralized data processing and long-term analytics,
the fog layer ensures immediate, localized threat detection and response,
offering a robust and scalable security solution for smart home networks