+44-3308224832Joy Winnie Wise DC, Revanth SB*, Sanjay D
The wireless sensor network represents a paradigm of infrastructure-less networking, wherein the absence of centralized access points, routers, or servers is a defining characteristic. Rather than relying on such traditional infrastructure, this network model leverages nodes as the primary agents for transmitting data packets. The contemporary landscape of wireless sensor networks is marked by a myriad of challenges, foremost among them being data security. As the proliferation of these networks continues, they are increasingly susceptible to a diverse array of attacks aimed at compromising data transmission integrity and precipitating data loss. Denial-of-Service (DoS) attacks pose a significant threat by inundating the network with spurious requests or traffic, thereby impeding legitimate communication and disrupting network functionality. Additionally, node compromise attacks exploit vulnerabilities within individual nodes, enabling adversaries to gain unauthorized access and exert control over critical network components. The ability to predict and prevent these attacks is crucial for maintaining a secure network environment. Our study offers a thorough examination of supervised machine learning methods for predicting network attacks. We gather and preprocess data, extracting pertinent features and formatting them for machine learning algorithms. We assess the effectiveness of these algorithms and explore the interpretability of the trained models to uncover insights into the patterns and traits of network attacks. This enables network administrators to grasp the attack landscape and devise tailored defense strategies.
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