Hybrid Feature Selection for Efficient Detection of DDoS Attacks in IoT

Liu, Hong; Wehbi, Khadijeh; Al-Salah, Tulha Hasan

doi:10.1145/3556677.3556687

Cited by 2 publications

(1 citation statement)

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“…With the help of accessible digital data, it assists in the conversion of internet-connected devices from any position and at any time into a connected ecosystem. The physical gadgets which range in size from compact to tremendous equipment interrelate among them without human interference via the internet [2]. Various supporting technologies are available, which include cloud computing, radio frequency identification and wireless sensor networks that have developed as important components of the IoT paradigm's growth [3].…”

Section: Introductionmentioning

confidence: 99%

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

Ragab,

M. Alshammari,

Maghrabi

et al. 2023

Mathematics

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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.

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Section: Introductionmentioning

confidence: 99%