Intrusion Detection in IoT Systems Based on Deep Learning Using Convolutional Neural Network

Hưởng, Phạm Văn; Thuan, Le Duc; Van, Le Thi Hong; Hưng, Đặng Việt

doi:10.1109/nics48868.2019.9023871

Cited by 16 publications

(5 citation statements)

References 17 publications

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“…Deep learning has provided promising results for large datasets, which has attracted many authors to employ deep learning techniques for designing intrusion detection systems. The author has used a convolutional neural network (CNN) to propose an intrusion detection system [56,57]. In another study, the Recurrent Neural Network (RNN) was used to propose an intrusion detection system [58].…”

Section: Related Workmentioning

confidence: 99%

Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

Sana,

Nazir,

Yang

et al. 2024

IEEE Access

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Background and Motivation:The ever-expanding Internet of Things (IoT) landscape presents a double-edged sword. While it fosters interconnectedness, the vast amount of data generated by IoT devices creates a larger attack surface for cybercriminals. Intrusions in these environments can have severe consequences. To combat this growing threat, robust intrusion detection systems (IDS) are crucial. The data comprised by this attack is multivariate, highly complex, non-stationary, and nonlinear. To extract the complex patterns from this complex data, we require the most robust, optimized tools. Methods: Machine learning (ML) and deep learning (DL) have emerged as powerful tools for IDSs, offering high accuracy in detecting and preventing security breaches. This research delves into anomaly detection, a technique that identifies deviations from normal system behavior, potentially indicating attacks. Given the complexity of anomaly data, we explore methods to improve detection performance. Proposed Approach: This research investigates the design and evaluation of a novel IDS. We leverage and optimize supervised ML methods like tree-based Support Vector Machines (SVM), ensemble methods, and neural networks (NN) alongside the cutting-edge DL approach of long short-term memory (LSTM) and vision transformers (ViT). We optimized the hyperparameters of these algorithms using a robust Bayesian optimization approach. Results: The implemented ML models achieved impressive training accuracy, with Random Forest and Ensemble Bagged Tree surpassing 99.90% of accuracy, an AUC of 1.00, an F1-score, and a balanced Matthews Correlation Coefficient (MCC) of 99.78%. While the initial deep learning LSTM model yielded an accuracy of 99.97%, the proposed ViT architecture significantly boosted performance with 100% of all metrics, along with a validation accuracy of 78.70% and perfect training accuracy. Conclusion: Our findings demonstrate the potential of the proposed methods for enhanced attack and intrusion detection. This improved detection capability can be instrumental in safeguarding system integrity, identifying fraudulent activity, and optimizing system performance in IoT networks.

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Section: Related Workmentioning

confidence: 99%

Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

Sana,

Nazir,

Yang

et al. 2024

IEEE Access

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“…The approach has a 98.9% average accuracy. The study [48] presents a deep learning and transfer learning-based intrusion detection system. The suggested technique presents network data in the form of a grayscale image using stream data visualization, and then a deep learning method is developed to detect network intrusion based on texture features in the grayscale image.…”

Section: Related Workmentioning

confidence: 99%

Transfer and CNN-Based De-Authentication (Disassociation) DoS Attack Detection in IoT Wi-Fi Networks

Gebresilassie,

Rafferty,

Chen

et al. 2023

Electronics

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The Internet of Things (IoT) is a network of billions of interconnected devices embedded with sensors, software, and communication technologies. Wi-Fi is one of the main wireless communication technologies essential for establishing connections and facilitating communication in IoT environments. However, IoT networks are facing major security challenges due to various vulnerabilities, including de-authentication and disassociation DoS attacks that exploit IoT Wi-Fi network vulnerabilities. Traditional intrusion detection systems (IDSs) improved their cyberattack detection capabilities by adapting machine learning approaches, especially deep learning (DL). However, DL-based IDSs still need improvements in their accuracy, efficiency, and scalability to properly address the security challenges including de-authentication and disassociation DoS attacks tailored to suit IoT environments. The main purpose of this work was to overcome these limitations by designing a transfer learning (TL) and convolutional neural network (CNN)-based IDS for de-authentication and disassociation DoS attack detection with better overall accuracy compared to various current solutions. The distinctive contributions include a novel data pre-processing, and de-authentication/disassociation attack detection model accompanied by effective real-time data collection and parsing, analysis, and visualization to generate our own dataset, namely, the Wi-Fi Association_Disassociation Dataset. To that end, a complete experimental setup and extensive research were carried out with performance evaluation through multiple metrics and the results reveal that the suggested model is more efficient and exhibits improved performance with an overall accuracy of 99.360% and a low false negative rate of 0.002. The findings from the intensive training and evaluation of the proposed model, and comparative analysis with existing models, show that this work allows improved early detection and prevention of de-authentication and disassociation attacks, resulting in an overall improved network security posture for all Wi-Fi-enabled real-world IoT infrastructures.

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“…In [19], a fresh approach for intrusion detection in IoT systems was recommended resulting from DL using CNN. IoT log data like address, duration, location, functions, service and so on are extracted for actual feature set.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Deep-GAN Model for Intrusion Detection in IoT Through Enhanced Whale Optimization

Balaji¹,

Narayanan²

2022

IJC

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IoT networks emerging as a significant growth in modern communication technological applications. The network formed with sensor nodes with resource restrictions in complexity, open wireless transmission features lead them prone to security threats. An efficient Intrusion Detection System aids in detecting attacks and performs crucial counter act to promise secure and reliable function. However, for the reason of the widespread nature of IoT, the intrusion detection system is supposed to carry out in discrete form with fewer fascination on common manager. In order to conquer these issues, Distributed – Generative Adversarial Network (D-GAN) with Enhanced Whale Optimization – Distributed deep learning based on Artificial Neural Network (EWO-HDL+ANN) is proposed. Here the GAN can detect internal attacks and the D-GAN is capable of detecting both internal and external attacks effectively. Transfer By Subspace Similarity is engaged to carry out. After that the preprocessed data is fed into feature extraction stage. Modified Principal Component Analysis (MPCA) is applied to feature extraction, which is used to extract new features that are enlightened. Then, feature selection is executed by Enhanced Whale Optimization Algorithm, which is used to choose significant and superfluous features from the dataset. It gets better the classification accuracy through the greatest fitness value. Then the intrusion detection is evaluated by applying HDL+ANN algorithm used to detect the attacks powerfully. The experimental conclusion proves that the introduced EWO-DDL+ANN method provides enhanced intrusion detection system in the view of greater accuracy, precision, recall, f-measure and low False Positive Rate.

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Intrusion Detection in IoT Systems Based on Deep Learning Using Convolutional Neural Network

Cited by 16 publications

References 17 publications

Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

Transfer and CNN-Based De-Authentication (Disassociation) DoS Attack Detection in IoT Wi-Fi Networks

Hybrid Deep-GAN Model for Intrusion Detection in IoT Through Enhanced Whale Optimization

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