CNN-LSTM Based Approach for Dos Attacks Detection in Wireless Sensor Networks

Abstract

A denial-of-service (DoS) attack is a coordinated attack by many endpoints, such as computers or networks. These attacks are often performed by a botnet, a network of malware-infected computers controlled by an attacker. The endpoints are instructed to send traffic to a particular target, overwhelming it and preventing legitimate users from accessing its services. In this project, we used a CNN-LSTM network to detect and classify DoS intrusion attacks. Attacks detection is considered a classification problem; the main aim is to clarify the attack as Flooding, Blackhole, Normal, TDMA, or Grayhole. This research study uses a computer- generated wireless sensor network-detection system dataset. The wireless sensor network environment was simulated using network simulator NS-2 based on the LEACH routing protocol to gather data from the network and preprocessed to produce 23 features classifying the state of the respective sensor and simulate five forms of Denial of Service (DoS) attacks. The developed CNN-LSTM model is further evaluated on 25 epochs with accuracy, Precision score, and Recall score of 0.944, 0.959, and 0.922, respectively, all on a scale of 0-1.