Robust DDoS Attack Detection Using Piecewise Harris Hawks Optimizer with Deep Learning for a Secure Internet of Things Environment

Abstract

The Internet of Things (IoT) refers to the network of interconnected physical devices that are embedded with software, sensors, etc., allowing them to exchange and collect information. Although IoT devices have several advantages and can improve people’s efficacy, they also pose a security risk. The malicious actor frequently attempts to find a new way to utilize and exploit specific resources, and an IoT device is an ideal candidate for such exploitation owing to the massive number of active devices. Especially, Distributed Denial of Service (DDoS) attacks include the exploitation of a considerable number of devices like IoT devices, which act as bots and transfer fraudulent requests to the services, thereby obstructing them. There needs to be a robust system of detection based on satisfactory methods for detecting and identifying whether these attacks have occurred or not in a network. The most widely used technique for these purposes is artificial intelligence (AI), which includes the usage of Deep Learning (DL) and Machine Learning (ML) to find cyberattacks. The study presents a Piecewise Harris Hawks Optimizer with an Optimal Deep Learning Classifier (PHHO-ODLC) for a secure IoT environment. The fundamental goal of the PHHO-ODLC algorithm is to detect the existence of DDoS attacks in the IoT platform. The PHHO-ODLC method follows a three-stage process. At the initial stage, the PHHO algorithm can be employed to choose relevant features and thereby enhance the classification performance. Next, an attention-based bidirectional long short-term memory (ABiLSTM) network can be applied to the DDoS attack classification process. Finally, the hyperparameter selection of the ABiLSTM network is carried out by the use of a grey wolf optimizer (GWO). A widespread simulation analysis was performed to exhibit the improved detection accuracy of the PHHO-ODLC technique. The extensive outcomes demonstrated the significance of the PHHO-ODLC technique regarding the DDoS attack detection technique in the IoT platform.

Advanced DDos Attack Detection in SD-IoT Using DNFN and Nature-Inspired Optimizations

“…A reliable detection system is necessary to identify and confirm network threats. Extensive results show the importance of the PHHO-ODLC approach for detecting DDoS attacks in IoT platforms [14]. Recent years have seen the rise of DDoS as a very disruptive technique for attackers.…”

Improvement of Distributed Denial of Service Attack Detection through Machine Learning and Data Processing

“…This result was compared to other classifiers, including the Gaussian Naive Bayes (GNB), Gradient Boosting (GB), Multilayer Perceptron (MLP), and Random Forest (RF) algorithms, demonstrating the superiority of the proposed model. Ragab et al [19] proposed a Harris Hawks optimizer called PHHO-ODLC for feature extraction and an Attention-Based Long Short-Term Memory Bidirectional Memory Network (ABiLSTM) for classifying DDoS attacks from the BoT-IoT dataset. The accuracy rate obtained for binary classification was 99.2%, whereas for multiclass classification, it was 98.83%.…”

“…In machine learning, data processing is considered essential to achieve good results using any machine learning model. However, in some studies, the data preprocessing was not detailed, or outliers were not taken into account [16,18,19]. In other studies [1,13,17,20,22], it was not specified how many records were affected after processing the outliers.…”