Adversarial Learning for Intrusion Detection in Wireless Sensor Networks: A GAN Approach

Adversarial Learning for Intrusion Detection in Wireless Sensor Networks: A GAN Approach

DOI: 10.4018/979-8-3693-3597-0.ch004
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Abstract

In the vital domain of wireless sensor networks (WSNs), which play an essential role in monitoring and data collection across diverse environments, safeguarding against cyber threats is of paramount importance. To address this challenge, this study unveils a pioneering system named “SecureNet.” This system employs the innovative technique of adversarial learning through Generative Adversarial Networks (GANs), enriched with a cutting-edge machine learning method known as deep learning, to bolster intrusion detection capabilities. Essentially, SecureNet operates by initiating a continuous competitive scenario between two deep learning models: one is designed to generate synthetic data that simulates cyber-attacks, while its counterpart focuses on identifying and distinguishing these simulated attacks from real threats. This relentless competition not only enhances SecureNet's proficiency in recognizing actual cyber-attacks.
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Introduction

Wireless Sensory Networks, or WSNs, are a fascinating blend of tiny, smart devices called sensors, which work together to monitor and record conditions in a variety of environments, from the vastness of natural habitats to the intricate workings of industrial machines kalyanaraman et.al(2024). These networks are a bit like a team of detectives, each with their own area to watch over, gathering clues (or data) to send back to a central hub for analysis. The beauty of WSNs lies in their ability to operate wirelessly, meaning they can be deployed in places where traditional, wired systems might struggle to reach or survive swaminathan et.al(2023).

At the heart of a WSN's operation is the process of data packet transmission, a method that might sound complex but is essentially about packaging and sending information from one point to another. Imagine you've written a letter and you want to send it to a friend; you put it in an envelope (the packaging) and post it (the sending). In WSNs, the “letter” is the data collected by the sensors, such as temperature readings, motion detection, or chemical concentrations, and the “envelope” is the data packet, a digital container that secures and organizes the data for its journey. The journey of a data packet begins at the sensor node, which collects the environmental data. This node has a small, yet powerful, computer that processes the raw data, converting it into a format suitable for transmission swaminathan et.al(2023). This is akin to translating your thoughts into words and sentences in a letter. Once the data is ready, it's packed into a data packet Benaddi, H et.al (2022) In Sending the packet on its journey is the next step. Because these sensors are wireless, they rely on radio waves to communicate. The packet is broadcasted out, hopping from sensor to sensor, or node to node, toward a central collection point known as the base station or gateway. This part of the process can be likened to a relay race where the baton is the data packet being passed from runner to runner. Each sensor node in the network acts as both a collector of data and a courier, receiving and forwarding packets towards their destination. One of the clever aspects of WSNs is how they manage to send these packets efficiently, despite the sensors often being limited in power, computational capacity, and memory. The networks use various strategies to minimize energy consumption and maximize the lifespan of the sensor nodes. For example, data packets might take the shortest possible route, or nodes might enter a low-power mode when not actively collecting or transmitting data senthil Kumar et.al (2023).

Finally, the data packet reaches the base station, the end of its journey. Here, the collected data is unpacked, analyzed, and potentially acted upon. This could mean adjusting the conditions within a greenhouse, triggering an alarm in response to detected motion, or simply recording the data for future study. The process of data packet transmission in WSNs is a testament to the power of collective effort and smart design. It allows us to gather vital information from the most remote or challenging environments, enhancing our understanding of the world and our ability to interact with it effectively.

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