Application Layer DDoS Attack Detection Using Cuckoo Search Algorithm-Trained Radial Basis Function

Abstract

In an application-layer distributed denial of service (App-DDoS) attack, zombie computers bring down the victim server with valid requests. Intrusion detection systems (IDS) cannot identify these requests since they have legal forms of standard TCP connections. Researchers have suggested several techniques for detecting App-DDoS traffic. There is, however, no clear distinction between legitimate and attack traffic. In this paper, we go a step further and propose a Machine Learning (ML) solution by combining the Radial Basis Function (RBF) neural network with the cuckoo search algorithm to detect App-DDoS traffic. We begin by collecting training data and cleaning them, then applying data normalizing and finding an optimal subset of features using the Genetic Algorithm (GA). Next, an RBF neural network is trained by the optimal subset of features and the optimizer algorithm of cuckoo search. Finally, we compare our proposed technique to the well-known k-nearest neighbor (k-NN), Bootstrap Aggregation (Bagging), Support Vector Machine (SVM), Multi-layer Perceptron) MLP, and (Recurrent Neural Network) RNN methods. Our technique outperforms previous standard and well-known ML techniques as it has the lowest error rate according to error metrics. Moreover, according to standard performance metrics, the results of the experiments demonstrate that our proposed technique detects App-DDoS traffic more accurately than previous techniques.

A Genetic Algorithm- and t-Test-Based System for DDoS Attack Detection in IoT Networks

“…They evaluated the efficacy of their proposed IDS in detecting attacks targeting the widely employed IIoT protocols such as Modbus, Message Queuing Telemetry Transport (MQTT), and Building Automation Controls Network (BACnet). Beitollahi et al [22] proposed a technique for detecting Application Layer DDoS Attacks using a Cuckoo Search Algorithm (CSA)-trained Radial Basis Function (RBF) Neural Network, emphasizing GA for feature selection using Mean Square Error (MSE) from the NSL-KDD dataset. This technique demonstrated better performance compared to methods like k-NN and Bagging, with improvements in error and standard performance metrics.…”

Detection of Application-Layer DDoS Attacks Produced by Various Freely Accessible Toolkits Using Machine Learning

“…Reference [13] suggests an approach for detecting DDoS attacks at the application layer that combines the Cuckoo Search Algorithm (CSA) and Radial Basis Function (RBF). The suggested approach uses a Genetic Algorithm (GA) for feature selection and a CSA to train an RBF neural network to detect DDoS, where the NSL-KDD dataset was used.…”

Bayesian Regularization (BR) Optimization Based DDoS Detection for SDN and Next Generation Communication Network

“…In this paper, we go a step further and propose a Machine Learning (ML) solution by combining the Radial Basis Function (RBF) neural network with the cuckoo search algorithm to detect App-DDoS traffic. We begin by collecting training data and cleaning them, then applying data normalizing and finding an optimal subset of features using the Genetic Algorithm (GA) [4]. Yungaicela-Naula et.al.…”

“…This method leverages Bayesian statistics to model and predict network traffic behavior while simultaneously applying regularization techniques to improve the reliability of these predictions. The combination of probabilistic modeling and regularization aims to distinguish between normal and malicious traffic effectively [4] [5].…”