The Internet of Things has completely changed the way we interact with our
surroundings. The upsurge in the use of IoT devices has helped us to do and monitor
our day-to-day tasks with ease. With the increasing use of IoTs, there also comes their
own set of security issues that need to be considered. IoT devices generate vast
amounts of data, most of which can be shared or connected to social networking sites
or online platforms. Due to malicious activity or attacks in the network, the data can be
manipulated. Digital deception poses a significant threat in the era of AI-driven social
networks, enabling the spread of misinformation and cyber intrusions at an
unprecedented scale. Here comes the role of the intrusion detection system, which
helps us to detect and prevent security breaches in IoT. In recent years, it has become a
critical issue to secure IoT systems as they are more prone to be hacked. So, it has
become very important to develop and optimize our traditional intrusion detection
systems. Previously used intrusion detection systems suffered from several limitations,
such as centralized data storage, privacy, and security concerns. They require a
significant amount of processed data to work effectively according to our criteria. This
can be challenging to us in case of very few historical attacks that are not enough to
train our systems. To overcome these issues, federated learning-based IDS for IoT
systems has been proposed. Federated Learning is a machine learning technique that
provides a system that can collaboratively learn a model without sharing its data. In
federated learning, the machine learning model is trained on the device itself, and only
the updates are pushed to the central server for improvement of the central model. It
helps us preserve the privacy of the device and, at the same time, enables us to build
and train the central model server that can detect any intrusion attacks. This can also
reduce the computational cost of machine training by distributing the work across
several devices. Federated learning models can adapt to the changes in the environment
as they are designed to continuously learn from the recently generated data from
servers and devices in the network. This way, the model can progress over time to
better detect the changes in the network traffic.
Keywords: Aggregation functions, Federated learning, Intrusion detection, IoTenabled social networks, Machine learning.
