VBQ-Net: A Novel Vectorization-Based Boost Quantized Network Model for Maximizing the Security Level of IoT System to Prevent Intrusions
Data sharing with additional devices across wireless networks is made simple and advantageous by the Internet of Things (IoT), an emerging technology. However, IoT systems are more susceptible to cyberattacks because of their continued growth and technological advances, which could lead to powerful...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/11/8/436 |
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| author | Ganeshkumar Perumal Gopalakrishnan Subburayalu Qaisar Abbas Syed Muhammad Naqi Imran Qureshi |
| author_facet | Ganeshkumar Perumal Gopalakrishnan Subburayalu Qaisar Abbas Syed Muhammad Naqi Imran Qureshi |
| author_sort | Ganeshkumar Perumal |
| collection | DOAJ |
| description | Data sharing with additional devices across wireless networks is made simple and advantageous by the Internet of Things (IoT), an emerging technology. However, IoT systems are more susceptible to cyberattacks because of their continued growth and technological advances, which could lead to powerful assaults. An intrusion detection system is one of the key defense mechanisms for information and communications technology. The primary shortcomings that plague current IoT security frameworks are their inability to detect intrusions properly, their substantial latency, and their prolonged processing time and delay. Therefore, this work develops a clever and innovative security architecture called Vectorization-Based Boost Quantized Network (VBQ-Net) for protecting IoT networks. Here, a Vector Space Bag of Words (VSBW) methodology is used to reduce the dimensionality of features and identify a key characteristic from the featured data. In addition, a brand-new classification technique, called Boosted Variance Quantization Neural Networks (BVQNNs), is used to classify the different types of intrusions using a weighted feature matrix. A Multi-Hunting Reptile Search Optimization (MH-RSO) algorithm is employed during categorization to calculate the probability value for selecting the right choices while anticipating intrusions. In this study, the most well-known and current datasets, such as IoTID-20, IoT-23, and CIDDS-001, are used to validate and evaluate the effectiveness of the proposed methodology. By evaluating the proposed approach on standard IoT datasets, the study seeks to address the limitations of current IoT security frameworks and provide a more effective defense mechanism against cyberattacks on IoT systems. |
| first_indexed | 2024-03-10T23:32:26Z |
| format | Article |
| id | doaj.art-3c43d6be131345cea8f2b89dcbe639ee |
| institution | Directory Open Access Journal |
| issn | 2079-8954 |
| language | English |
| last_indexed | 2024-03-10T23:32:26Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj.art-3c43d6be131345cea8f2b89dcbe639ee2023-11-19T03:13:09ZengMDPI AGSystems2079-89542023-08-0111843610.3390/systems11080436VBQ-Net: A Novel Vectorization-Based Boost Quantized Network Model for Maximizing the Security Level of IoT System to Prevent IntrusionsGaneshkumar Perumal0Gopalakrishnan Subburayalu1Qaisar Abbas2Syed Muhammad Naqi3Imran Qureshi4College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi ArabiaDepartment of Information Technology, Hindustan Institute of Technology and Science, Kelambakkam, Chennai 603103, IndiaCollege of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi ArabiaDepartment of Computer Science, Quaid-i-Azam University, Islamabad 44000, PakistanCollege of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi ArabiaData sharing with additional devices across wireless networks is made simple and advantageous by the Internet of Things (IoT), an emerging technology. However, IoT systems are more susceptible to cyberattacks because of their continued growth and technological advances, which could lead to powerful assaults. An intrusion detection system is one of the key defense mechanisms for information and communications technology. The primary shortcomings that plague current IoT security frameworks are their inability to detect intrusions properly, their substantial latency, and their prolonged processing time and delay. Therefore, this work develops a clever and innovative security architecture called Vectorization-Based Boost Quantized Network (VBQ-Net) for protecting IoT networks. Here, a Vector Space Bag of Words (VSBW) methodology is used to reduce the dimensionality of features and identify a key characteristic from the featured data. In addition, a brand-new classification technique, called Boosted Variance Quantization Neural Networks (BVQNNs), is used to classify the different types of intrusions using a weighted feature matrix. A Multi-Hunting Reptile Search Optimization (MH-RSO) algorithm is employed during categorization to calculate the probability value for selecting the right choices while anticipating intrusions. In this study, the most well-known and current datasets, such as IoTID-20, IoT-23, and CIDDS-001, are used to validate and evaluate the effectiveness of the proposed methodology. By evaluating the proposed approach on standard IoT datasets, the study seeks to address the limitations of current IoT security frameworks and provide a more effective defense mechanism against cyberattacks on IoT systems.https://www.mdpi.com/2079-8954/11/8/436internet of things (IoT)intrusion detection system (IDS)vectorization-based boost quantized network (VBQ-Net)vector space bag of words (VSBW)boosted variance quantization neural network (BVQNN)multi-hunting reptile search optimization (MH-RSO) |
| spellingShingle | Ganeshkumar Perumal Gopalakrishnan Subburayalu Qaisar Abbas Syed Muhammad Naqi Imran Qureshi VBQ-Net: A Novel Vectorization-Based Boost Quantized Network Model for Maximizing the Security Level of IoT System to Prevent Intrusions Systems internet of things (IoT) intrusion detection system (IDS) vectorization-based boost quantized network (VBQ-Net) vector space bag of words (VSBW) boosted variance quantization neural network (BVQNN) multi-hunting reptile search optimization (MH-RSO) |
| title | VBQ-Net: A Novel Vectorization-Based Boost Quantized Network Model for Maximizing the Security Level of IoT System to Prevent Intrusions |
| title_full | VBQ-Net: A Novel Vectorization-Based Boost Quantized Network Model for Maximizing the Security Level of IoT System to Prevent Intrusions |
| title_fullStr | VBQ-Net: A Novel Vectorization-Based Boost Quantized Network Model for Maximizing the Security Level of IoT System to Prevent Intrusions |
| title_full_unstemmed | VBQ-Net: A Novel Vectorization-Based Boost Quantized Network Model for Maximizing the Security Level of IoT System to Prevent Intrusions |
| title_short | VBQ-Net: A Novel Vectorization-Based Boost Quantized Network Model for Maximizing the Security Level of IoT System to Prevent Intrusions |
| title_sort | vbq net a novel vectorization based boost quantized network model for maximizing the security level of iot system to prevent intrusions |
| topic | internet of things (IoT) intrusion detection system (IDS) vectorization-based boost quantized network (VBQ-Net) vector space bag of words (VSBW) boosted variance quantization neural network (BVQNN) multi-hunting reptile search optimization (MH-RSO) |
| url | https://www.mdpi.com/2079-8954/11/8/436 |
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