Explainable AI-Based DDOS Attack Identification Method for IoT Networks

Kalutharage, Chathuranga Sampath; Liu, Xiaodong; Chrysoulas, Christos; Pitropakis, Nikolaos; Papadopoulos, Pavlos

doi:10.3390/computers12020032

Cited by 27 publications

(8 citation statements)

References 31 publications

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“…AI techniques have the potential to significantly enhance the security of IoT systems [13], [15]. Leveraging AI algorithms and models can enable intelligent threat detection, robust authentication mechanisms, and proactive security analytics.…”

Section: Ai Techniques For Iot Securitymentioning

confidence: 99%

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Securing the Internet of Things in Artificial Intelligence Era: A Comprehensive Survey

Humayun,

Tariq,

Alfayad

et al. 2024

IEEE Access

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The Internet of Things (IoT) has revolutionized various domains, enabling interconnected devices to communicate and exchange data. The integration of Artificial Intelligence (AI) in IoT systems further enhances their capabilities and potential benefits. Unfortunately, in the era of AI, ensuring the privacy and security of the IoT faces novel and specific challenges. IoT security is imperative, necessitating comprehensive strategies, including comprehension of IoT security challenges, implementation of AI methodologies, adoption of resilient security frameworks, and handling of privacy and ethical concerns to construct dependable and secure IoT systems. It is vital to note that the term 'security' encompasses a more comprehensive view than cyberattacks alone. Therefore, with an emphasis on securing against cyberattacks, this comprehensive survey also includes physical security threats on the IoT. It investigates the complexities and solutions for IoT systems, placing particular emphasis on AI-based security techniques. The paper undertakes a categorization of the challenges associated with ensuring IoT security, investigates the utilization of AI in IoT security, presents security frameworks and strategies, underscores privacy and ethical considerations, and provides insights derived from practical case studies. Furthermore, the survey sheds light on emerging trends concerning IoT security in the AI era. This survey provides significant contributions to the understanding of establishing dependable and secure IoT systems through an exhaustive examination of the present condition of IoT security and the ramifications of AI on it.

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Section: Ai Techniques For Iot Securitymentioning

confidence: 99%

“…AI techniques play a pivotal role in enhancing IoT security. Therefore, it is inevitable to explore the application of AI in addressing IoT security [13]- [15] challenges. This paper categorizes AI techniques into cyberattacks and threat detection and prevention, secure communication and authentication, and predictive security analytics.…”

mentioning

confidence: 99%

Securing the Internet of Things in Artificial Intelligence Era: A Comprehensive Survey

Humayun,

Tariq,

Alfayad

et al. 2024

IEEE Access

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“…The SHAP method employs the core idea of Shapley values from cooperative game theory to determine how much each feature contributes to the model's production. 50 In the context of machine learning, SHAP values calculate each feature's contribution to the model's prediction, making it helpful in identifying critical elements for specific tasks, such as detecting DDoS attacks based on network behavior. The SHAP feature selection process involves computing SHAP values for every feature in the dataset and ranking them in order of importance.…”

Section: Feature Selectionmentioning

confidence: 99%

“…SHapley Additive exPlanations (SHAP) is a technique used to select features in machine learning models to understand how each component affects the output. The SHAP method employs the core idea of Shapley values from cooperative game theory to determine how much each feature contributes to the model's production 50 . In the context of machine learning, SHAP values calculate each feature's contribution to the model's prediction, making it helpful in identifying critical elements for specific tasks, such as detecting DDoS attacks based on network behavior.…”

Section: Introductionmentioning

confidence: 99%

An efficient approach to detect distributed denial of service attacks for software defined internet of things combining autoencoder and extreme gradient boosting with feature selection and hyperparameter tuning optimization

Setitra

Fan

Bensalem

2023

Trans Emerging Tel Tech

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The growing popularity of Software Defined Networks (SDN) and the Internet of Things (IoT) has led to the emergence of Software Defined Internet of Things (SDIoT) based on centralized network management by the Control Plane, which can handle the dynamic nature of IoT devices and the high volume of network traffic. However, due to their specific design, SDIoTs are the ideal target for Distributed Denial of Service (DDoS) attacks, becoming one of the most destructive threats. Machine learning (ML) techniques are best suited to solve this problem due to the recent growth and sophistication of DDoS attacks. In this study, we propose an enhanced deep learning approach based on combining AutoEncoder (AE) and Extreme Gradient Boosting (XGBoost). First, we applied the SHapley Additive exPlanations (SHAP) feature selection method to select the appropriate features subset according to their correlation results. Next, the AE is trained on the previous subset to learn a compact representation of the input features. The latent representation generated by the AE is then used as input for the XGBoost model, which is trained to predict the target variable and classify the traffic as usual or attack. In parallel, Grid Search Cross Validation (GSCV) is used to find the optimal hyperparameters for the AE‐XGBoost. The experimental results using two publicly available realistic SDN‐Iot datasets demonstrate that the proposed approach enables precise identification of DDoS attacks in SDIoT networks, achieving a 99.9920% accuracy, an F1 score of 0.999917, and a low false positive rate. Furthermore, the proposed model's performance exceeds that of the models used for comparison.

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“…Kalutharage et al [27] proposed XAI-based techniques for detecting DDoS attack anomalies on the USBIDS (University of Sannio, Benevento Instrution Detection System) dataset. The research focuses on instance-by-instance, local and global explanations, and feature correlations, explaining anomalies by providing Autoencoder and Kernel SHAP techniques.…”

Section: B Xai For Modelsmentioning

confidence: 99%

AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification

Wei¹,

et al. 2021

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Distributed Denial-of-Service (DDoS) attacks are increasing as the demand for Internet connectivity massively grows in recent years. Conventional shallow machine learning-based techniques for DDoS attack classification tend to be ineffective when the volume and features of network traffic, potentially carry malicious DDoS payloads, increase exponentially as they cannot extract high importance features automatically. To address this concern, we propose a hybrid approach named AE-MLP that combines two deep learning-based models for effective DDoS attack detection and classification. The Autoencoder (AE) part of our proposed model provides an effective feature extraction that finds the most relevant feature sets automatically without human intervention (e.g., knowledge of cybersecurity professionals). The Multilayer Perceptron Network (MLP) part of our proposed model uses the compressed and reduced feature sets produced by the AE as inputs and classifies the attacks into different DDoS attack types to overcome the performance overhead and bias associated with processing large feature sets with noise (i.e., unnecessary feature values). Our experimental results, obtained through comprehensive and extensive experiments on different aspects of performance on the CICDDoS2019 dataset, demonstrate both a very high and robust accuracy rate and F1-score that exceed 98% which also outperformed the performance of many similar methods. This shows that our proposed model can be used as an effective DDoS defense tool against the growing number of DDoS attacks.

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Explainable AI-Based DDOS Attack Identification Method for IoT Networks

Cited by 27 publications

References 31 publications

Securing the Internet of Things in Artificial Intelligence Era: A Comprehensive Survey

Securing the Internet of Things in Artificial Intelligence Era: A Comprehensive Survey

An efficient approach to detect distributed denial of service attacks for software defined internet of things combining autoencoder and extreme gradient boosting with feature selection and hyperparameter tuning optimization

AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification

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