Robust DDoS Attack Detection Using Piecewise Harris Hawks Optimizer with Deep Learning for a Secure Internet of Things Environment
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/21/4448 |
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| author | Mahmoud Ragab Sultanah M. Alshammari Louai A. Maghrabi Dheyaaldin Alsalman Turki Althaqafi Abdullah AL-Malaise AL-Ghamdi |
| author_facet | Mahmoud Ragab Sultanah M. Alshammari Louai A. Maghrabi Dheyaaldin Alsalman Turki Althaqafi Abdullah AL-Malaise AL-Ghamdi |
| author_sort | Mahmoud Ragab |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T11:25:20Z |
| format | Article |
| id | doaj.art-63c269b6aa024047aac1d980d6d52d3f |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T11:25:20Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-63c269b6aa024047aac1d980d6d52d3f2023-11-10T15:07:55ZengMDPI AGMathematics2227-73902023-10-011121444810.3390/math11214448Robust DDoS Attack Detection Using Piecewise Harris Hawks Optimizer with Deep Learning for a Secure Internet of Things EnvironmentMahmoud Ragab0Sultanah M. Alshammari1Louai A. Maghrabi2Dheyaaldin Alsalman3Turki Althaqafi4Abdullah AL-Malaise AL-Ghamdi5Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaCenter of Excellence in Smart Environment Research, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Software Engineering, College of Engineering, University of Business and Technology, Jeddah, Saudi ArabiaDepartment of Cybersecurity, School of Engineering, Computing and Design, Dar Al-Hekma University, Jeddah 22246, Saudi ArabiaInformation Systems Department, School of Engineering, Computing and Design, Dar Al-Hekma University, Jeddah 22246, Saudi ArabiaInformation Systems Department, School of Engineering, Computing and Design, Dar Al-Hekma University, Jeddah 22246, Saudi ArabiaThe 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.https://www.mdpi.com/2227-7390/11/21/4448cybersecurityDDoS attacksnetwork securityInternet of Thingsartificial intelligencemetaheuristics |
| spellingShingle | Mahmoud Ragab Sultanah M. Alshammari Louai A. Maghrabi Dheyaaldin Alsalman Turki Althaqafi Abdullah AL-Malaise AL-Ghamdi Robust DDoS Attack Detection Using Piecewise Harris Hawks Optimizer with Deep Learning for a Secure Internet of Things Environment Mathematics cybersecurity DDoS attacks network security Internet of Things artificial intelligence metaheuristics |
| title | Robust DDoS Attack Detection Using Piecewise Harris Hawks Optimizer with Deep Learning for a Secure Internet of Things Environment |
| title_full | Robust DDoS Attack Detection Using Piecewise Harris Hawks Optimizer with Deep Learning for a Secure Internet of Things Environment |
| title_fullStr | Robust DDoS Attack Detection Using Piecewise Harris Hawks Optimizer with Deep Learning for a Secure Internet of Things Environment |
| title_full_unstemmed | Robust DDoS Attack Detection Using Piecewise Harris Hawks Optimizer with Deep Learning for a Secure Internet of Things Environment |
| title_short | Robust DDoS Attack Detection Using Piecewise Harris Hawks Optimizer with Deep Learning for a Secure Internet of Things Environment |
| title_sort | robust ddos attack detection using piecewise harris hawks optimizer with deep learning for a secure internet of things environment |
| topic | cybersecurity DDoS attacks network security Internet of Things artificial intelligence metaheuristics |
| url | https://www.mdpi.com/2227-7390/11/21/4448 |
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