Description:Title: NeuralNetGuard: An Invention for Deep Learning Models
Safeguarding Industrial IoT Against Malicious Attacks.
Field of the Invention
[0001] This innovation relates to the growing topic of cybersecurity in the
Industrial Internet of Things (IIoT). As companies continue to incorporate
IoT devices into their operational frameworks to improve efficiency and
automation, the susceptibility to cyber-attacks becomes a significant
problem. Conventional security measures frequently prove inadequate in
identifying and minimising advanced assaults aimed at IIoT systems. Thus,
the innovation aims to create sophisticated deep learning models that are
particularly designed to detect and mitigate malicious attack actions in the
IIoT ecosystem.
[0002] Through the introduction of this groundbreaking method, the area
of innovation seeks to completely transform IIoT security protocols by
providing strong defence mechanisms to counter a diverse array of cyber
threats. By utilising complex neural network topologies and advanced
algorithms, this invention not only improves the ability to identify threats
but also enables stakeholders in the Industrial Internet of Things (IIoT) to
actively protect their vital infrastructure from possible security breaches.
In the end, the act of inventing drives the development of IIoT security
towards a more proactive and adaptable approach, guaranteeing the
strength and honesty of industrial networks in response to constantly
changing cyber threats.
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Background
[0003] The context in which this innovation arises is characterised by the
widespread and fast-growing presence of IoT devices in industrial
environments, which is popularly known as the Industrial Internet of Things
(IIoT). This shift in paradigm has resulted in unparalleled levels of
connection and automation in industrial operations, enabling improved
productivity and efficiency. Nevertheless, increased interconnectivity also
brings up noteworthy cybersecurity obstacles, as IIoT systems emerge as
primary objectives for malevolent individuals aiming to disrupt operations,
pilfer confidential information, or inflict physical damage.
[0004] Conventional cybersecurity methods frequently have difficulties in
keeping up with the ever-changing nature of cyber threats in the Industrial
Internet of Things (IIoT) environment. Legacy systems are not agile or
adaptable enough to effectively identify and respond to sophisticated
threats, which makes industrial networks vulnerable to exploitation. In
light of this situation, the innovation aims to meet the urgent requirement
for sophisticated security solutions that are specifically designed for the
distinct features of IIoT settings. Through the utilisation of deep learning,
this innovation aims to provide a new period of proactive identification and
reduction of potential dangers, strengthening the ability of industrial
infrastructure to withstand harmful actions.
[0005]
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Furthermore, the background of this invention is shaped by the increasing
integration of artificial intelligence (AI) and machine learning (ML)
techniques in various domains, including cybersecurity. Deep learning, a
subset of ML, has demonstrated remarkable capabilities in pattern
recognition and anomaly detection, making it particularly well-suited for
addressing the complex and dynamic nature of cyber threats. Building
upon this foundation, the invention harnesses the potential of deep
learning models to analyze vast amounts of data generated by IIoT
devices in real-time, enabling timely and accurate identification of harmful
attack activities. By amalgamating cutting-edge technologies with the
imperative of securing industrial systems, the invention aims to fortify IIoT
environments against emerging cyber threats, ensuring the continued
reliability and safety of critical infrastructure.
[0006] Modern industry and infrastructure depend on the Industrial
Internet of Things (IIoT), but it also confronts growing cyber security risks.
The development of deep learning models for the IIoT ecosystem's
identification of dangerous attack activities is the main emphasis of this
work. Protecting IIoT networks from cyber-attacks is essential as they grow
more interconnected. A possible strategy is the use of deep learning,
particularly recurrent neural networks (RNNs) along with artificial neural
networks (ANNs). These algorithms are able to identify and prevent
assaults by analyzing enormous datasets containing network traffic,
device behavior, and system abnormalities.
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[0007] WO2021087443A1 Embodiments can offer strong protection for
IoT devices against illegal activities, such as information theft and privacy
invasion. A technique for identifying abnormal network traffic involves
monitoring an operational IoT network to collect data on network events,
extracting information about various characteristics of these events from
the collected data, training a machine learning model to categorise the
events based on the extracted information, continuing to monitor the
operational IoT network to gather additional network traffic data, extracting
additional information about the characteristics of these new events,
categorising the new events using the extracted additional information, and
identifying an abnormal event based on the categorization of the new
events.
[0008] US10338555B2 The system typically comprises a crosspoint
switch inside the local data collecting system, which has several inputs and
outputs. These include a first input connected to the first sensor and a
second input connected to the second sensor. The multiple outputs consist
of a first and second output that can be switched between two conditions.
In the first condition, the first output switches between delivering the first
sensor signal and the second sensor signal. In the second condition, both
the first output and the second output simultaneously deliver the first
sensor signal and the second sensor signal, respectively. Each input can be
allocated to any output independently. Unassigned outputs are
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programmed to deactivate, resulting in a high-impedance condition. The
local data collecting system is set up to handle data gathering bands. The
local data collecting system incorporates a neural network expert system
that employs intelligent management of the data gathering bands.
[0009] INA202211030237 The subsequent comprehensive explanation
refers to the attached illustrations, which are an integral component of this
document. The illustrations depict particular examples that demonstrate
how the invention can be implemented. The described embodiments
provide enough information for skilled individuals to implement the
invention. It should be noted that these embodiments can be combined or
other embodiments can be used, and modifications can be made without
deviating from the essence and extent of the present invention. The
subsequent comprehensive explanation should not be interpreted as
restrictive, since the extent of the current invention is determined by the
attached claims and their equivalents , Claims:We Claim:
[1] It comprising: data collection, preprocessing, Deep Learning model
training, deployment, and continuous monitoring for potential threats.
[2] A system for IoT attack detection, including IoT devices, a preprocessing
module, a Deep Learning model trained for attack detection, deployment
mechanisms, and a monitoring system.
[3] A computer-readable storage medium storing instructions for IoT attack
detection, involving data preprocessing, Deep Learning model training,
deployment, and continuous monitoring.
[4] A method for optimizing IoT attack detection workflows, integrating data
collection, preprocessing, model training, deployment, and monitoring to
enhance efficiency and accuracy in detecting and responding to threats.