Securing Wireless Sensor Networks Using Machine Learning

Wireless Sensor Networks (WSNs) are essential components of the Internet of Things (IoT), yet because of their limited resources and changeable network architectures, they are extremely susceptible to cyberattacks. The ineffectiveness of traditional security measures frequently makes the use of cutting-edge methods like Machine Learning (ML) necessary. This study offers a thorough examination of cybersecurity threats in WSNs, emphasising cutting-edge machine learning-based techniques for attack prevention, anomaly detection, and intrusion detection. The effectiveness of several machine learning methods, such as Random Forest (RF), Decision Tree (DT), Support Vector Machines (SVM), and MultiLayer Perceptron, in identifying cyberthreats while minimising computing cost is assessed. To improve network resilience, a security framework with ML-driven detection and preventive procedures is suggested. Results from experiments show that machine learning approaches are more effective than traditional methods at accurately identifying security threats. The paper also emphasises the necessity for lightweight and optimised machine learning systems for securing WSNs, highlighting important design issues like as energy efficiency, computing limitations, and real-time adaptability