Explainable AI and Random Forest Based Reliable Intrusion Detection system

Wali, Syed; Khan, Irfan Ali

doi:10.36227/techrxiv.17169080

Cited by 7 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, many studies have utilized machine learning (ML) and deep learning (DL) algorithms to develop efficient NIDSs. Furthermore, researchers also introduce XAI techniques to comprehend the output of the black box AI systems [32][33][34][35][36][37].…”

Section: Xai In Cybersecuritymentioning

confidence: 99%

Interpretability and Transparency of Machine Learning in File Fragment Analysis with Explainable Artificial Intelligence

Jinad,

Islam,

Shashidhar

2024

Electronics

View full text Add to dashboard Cite

Machine learning models are increasingly being used across diverse fields, including file fragment classification. As these models become more prevalent, it is crucial to understand and interpret their decision-making processes to ensure accountability, transparency, and trust. This research investigates the interpretability of four machine learning models used for file fragment classification through the lens of Explainable Artificial Intelligence (XAI) techniques. Specifically, we employ two prominent XAI methods, Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to shed light on the black-box nature of four machine learning models used for file fragment classification. By conducting a detailed analysis of the SHAP and LIME explanations, we demonstrate the effectiveness of these techniques in improving the interpretability of the models’ decision-making processes. Our analysis reveals that these XAI techniques effectively identify key features influencing each model’s predictions. The results also showed features that were critical to predicting specific classes. The ability to interpret and validate the decisions made by machine learning models in file fragment classification can enhance trust in these models and inform improvements for better accuracy and reliability. Our research highlights the importance of XAI techniques in promoting transparency and accountability in the application of machine learning models across diverse domains.

show abstract

Section: Xai In Cybersecuritymentioning

confidence: 99%

Interpretability and Transparency of Machine Learning in File Fragment Analysis with Explainable Artificial Intelligence

Jinad,

Islam,

Shashidhar

2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Faithfulness of an explanation is a desirable property that describes the ability to capture the features used by a predictor [7], but can be quite complex to compute. However, other methods can also be used to compute metrics related to Faithfulness [16,31]. Robustness measures the stability and consistency of a given XAI method [15,28], while Complexity [15] ensures that explanation would be easily understandable by users.…”

Section: Related Workmentioning

confidence: 99%

Explainability-based Metrics to Help Cyber Operators Find and Correct Misclassified Cyberattacks

Duraz,

Espes,

Francq

et al. 2023

Proceedings of the 2023 on Explainable and Safety Bounded, Fidelitous, Machine Learning for Networking

View full text Add to dashboard Cite

Machine Learning (ML)-based Intrusion Detection Systems (IDS) have shown promising performance. However, in a human-centered context where they are used alongside human operators, there is often a need to understand the reasons of a particular decision. EXplainable AI (XAI) has partially solved this issue, but evaluation of such methods is still difficult and often lacking. This paper revisits two quantitative metrics, Completeness and Correctness, to measure the quality of explanations, i.e., if they properly reflect the actual behaviour of the IDS. Because human operators generally have to handle a huge amount of information in limited time, it is important to ensure that explanations do not miss important causes, and that the important features are indeed causes of an event. However, to be more usable, it is better if explanations are compact. For XAI methods based on feature importance, Completeness shows on some public datasets that explanations tend to point out all important causes only with a high number of features, whereas Correctness seem to be highly correlated with prediction results of the IDS. Finally, besides evaluating the quality of XAI methods, Completeness and Correctness seem to enable identification of IDS failures and can be used to point the operator towards suspicious activity missed or misclassified by the IDS, suggesting manual investigation for correction. CCS CONCEPTS• Security and privacy → Intrusion detection systems; • Humancentered computing → HCI design and evaluation methods;• Computing methodologies → Machine learning.

show abstract

“…of Computer Science and Engineering, Texas A&M University, USA. (Emails: zahidhus-sain909@tamu.edu, irfankhan@tamu.edu) role of cybersecurity comes into play, acting as a digital shield to protect these interconnected systems [2]. However, as we delve deeper into the realm of IIoT, it becomes evident that traditional cybersecurity methods are struggling to keep up.…”

Section: Introductionmentioning

confidence: 99%

Sequentially Integrated Convolutional-Gated Recurrent Unit Autoencoder for Enhanced Security in Industrial Control Systems

Hussain,

Khan

2024

Preprint

View full text Add to dashboard Cite

In the contemporary era of rapid technological advancement, the Industrial Internet of Things (IIoT) has become a pivotal element in revolutionizing industrial operations. This paper delves into the escalating cybersecurity challenges posed by the sprawling networks of IIoT, accentuating the inadequacy of traditional cybersecurity methods in the face of sophisticated cyber threats. We introduce machine learning (ML) as a transformative approach to fortify the cybersecurity landscape of IIoT systems. Our research primarily focuses on the application of machine learning algorithms to detect, analyze, and counteract diverse cyber threats in IIoT environments. These algorithms are trained to recognize and respond to a spectrum of cyber threats, thereby enhancing the resilience of IIoT networks. We present a novel Convolutional-GRU autoencoder model, which demonstrates superior performance over traditional machine learning models in terms of accuracy, precision, recall, and F1score. This model is adept at learning and adapting from complex data patterns, ensuring robust defense against cyber intrusions.We also address the challenges in applying ML to IIoT cybersecurity, considering the varied nature of IIoT devices and the dynamic landscape of cyber threats. This study is an important stride towards enhancing IIoT cybersecurity, highlighting the symbiotic relationship between ML and IIoT. It serves as a foundation for future research and a guide for current implementations, aiming to create more secure, reliable, and efficient IIoT environments. By exploring the potential of ML in cybersecurity, we pave the way for a new era in industrial digital protection, one that is adaptable, forward-thinking, and resilient against the ever-evolving digital threats.

show abstract

Explainable AI and Random Forest Based Reliable Intrusion Detection system

Cited by 7 publications

References 24 publications

Interpretability and Transparency of Machine Learning in File Fragment Analysis with Explainable Artificial Intelligence

Interpretability and Transparency of Machine Learning in File Fragment Analysis with Explainable Artificial Intelligence

Explainability-based Metrics to Help Cyber Operators Find and Correct Misclassified Cyberattacks

Sequentially Integrated Convolutional-Gated Recurrent Unit Autoencoder for Enhanced Security in Industrial Control Systems

Contact Info

Product

Resources

About