Deep Learning for DoS Attack Detection in Wireless Sensor Networks

In many different fields, wireless sensor networks, or WSNs, are being used more and more for data collecting and monitoring. WSNs, which are often made up of a large number of tiny, inexpensive, and readily deployable sensor nodes, are used to collect data on environmental variables including motion, temperature, and pressure. They are perfect for many different applications and sectors because of their scalability and simplicity. WSNs, however, are intrinsically susceptible to a range of security risks and attacks because of their restricted power, storage, connectivity, and processing capacity-factors that also make it difficult to put strong defenses in place. The protection of these networks depends on an efficient Intrusion Detection System (IDS). But in the face of more complex, frequent, and intelligent threats, traditional IDS techniques are losing their effectiveness. Denial of Service (DoS) attacks are one of the most dangerous of these for WSNs. This paper examines the body of research on DoS attack detection in WSNs. Additionally, using a customized dataset called WSN-DS, we trained a number of Deep Learning (DL)-based intrusion detection systems (IDS) models to identify four distinct DoS attack types: flooding, scheduling, grayhole, and blackhole attacks. After assessing and contrasting these systems' performances, we had a discussion about the findings and possible future research avenues.