Machine Learning Algorithms to Detect Attacks in Wireless Sensor Networks
Keywords:
Wireless Sensor Networks, Sensor Nodes, Denial of Service, Probing Attack, Remote to Local, User to RootAbstract
A network of independent and interconnected sensor nodes that communicate with each other wirelessly to collect, process, and transmit data from the environment they are deployed in is referred to as Wireless Sensor Network. These nodes are equipped with varied types of sensors, such as temperature, humidity, light, motion, and gas, enabling them to monitor and gather information about their surroundings. The data collected by these nodes can be utilized for various applications, making WSNs an integral part of modern technological advancements. Wireless Sensor Networks are subject to various types of attacks due to their inherent characteristics, limited resources, dynamic topologies, and wireless communication. These attacks can compromise the network's integrity, confidentiality, availability, and overall functionality. In this paper, we have focused on DOS, Probe, R2L and U2R attacks. Many Machine Learning algorithms have been applied to detect these attacks in WSNs. ML algorithms have also been compared after applying a balancing technique called SMOTE. Binary and multi-class classifications have been performed to detect the attacks in WSN. The algorithms are compared based on performance matrices like MCC, CS, ROC, weighted and macro average scores, recall precision, and F-1 scores.Top of Form
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