Performance evaluation of deep learning techniques for DoS attacks detection in wireless sensor network

Journal of Big Data

Research

Lahcen Oughdir¹  Salim Salmi¹ 

[1] Engineering, Systems and Applications Laboratory, ENSA, Sidi Mohamed Ben Abdellah University, Fez, Morocco;

关键词: Wireless sensor networks (WSNs);    Intrusion detection system (IDS);    Denial of service (DOS);    Deep learning (DL);   

DOI  :  10.1186/s40537-023-00692-w

 received in 2022-03-08, accepted in 2023-01-21,  发布年份 2023

来源: Springer

【 摘 要 】

Wireless sensor networks (WSNs) are increasingly being used for data monitoring and collection purposes. Typically, they consist of a large number of sensor nodes that are used remotely to collect data about the activities and conditions of a particular area, for example, temperature, pressure, motion. Each sensor node is usually small, inexpensive, and relatively easy to deploy compared to other sensing methods. For this reason, WSNs are used in a wide range of applications and industries. However, WSNs are vulnerable to different kinds of security threats and attacks. This is primarily because they are very limited in resources like power, storage, bandwidth, and processing power that could have been used in developing their defense. To ensure their security, an effective Intrusion detection system (IDS) need to be in place to detect these attacks even under these constraints. Today, traditional IDS are less effective as these malicious attacks are becoming more intelligent, frequent, and complex. Denial of service (DOS) attack is one of the main types of attacks that threaten WSNs. For this reason, we review related works that focus on detecting DoS attacks in WSN. In addition, we developed and implemented several Deep learning (DL) based IDS. These systems were trained on a specialized dataset for WSNs called WSN-DS in detecting four types of DoS attacks that affects WSNs. They include the Blackhole, Grayhole, Flooding, and Scheduling attacks. Finally, we evaluated and compared the results and we discuss possible future works.

【 授权许可】

CC BY   
© The Author(s) 2023

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