Improving the Feature Set in IoT Intrusion Detection Problem Based on FP-Growth Algorithm

Van, Le Thi Hong; Hưởng, Phạm Văn; Thuan, Le Duc; Minh, Nguyen Hieu

doi:10.1109/atc50776.2020.9255431

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…The CLS-Miner [54] was designed with supplementary coverage and LBP. Despite numerous efforts [46][47][48][49][50], no significant work has examined the probability of coexisting items as HUIs for top-N HUI predictions [55]. Van et al [56] used FP growth to examine the association between available features to append new features with a certain threshold to perform HUI estimation.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Framework with Convolutional Sequential Semantic Embedding for Mining High-Utility Itemsets and Top-N Recommendations

Chaudhari

2024

JICCE

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High-utility itemset mining (HUIM) is a dominant technology that enables enterprises to make real-time decisions, including supply chain management, customer segmentation, and business analytics. However, classical support value-driven Apriori solutions are confined and unable to meet real-time enterprise demands, especially for large amounts of input data. This study introduces a groundbreaking model for top-N high utility itemset mining in real-time enterprise applications. Unlike traditional Apriori-based solutions, the proposed convolutional sequential embedding metrics-driven cosine-similarity-based multilayer perception learning model leverages global and contextual features, including semantic attributes, for enhanced top-N recommendations over sequential transactions. The MATLAB-based simulations of the model on diverse datasets, demonstrated an impressive precision (0.5632), mean absolute error (MAE) (0.7610), hit rate (HR)@K (0.5720), and normalized discounted cumulative gain (NDCG)@K (0.4268). The average MAE across different datasets and latent dimensions was 0.608. Additionally, the model achieved remarkable cumulative accuracy and precision of 97.94% and 97.04% in performance, respectively, surpassing existing state-of-the-art models. This affirms the robustness and effectiveness of the proposed model in real-time enterprise scenarios.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Framework with Convolutional Sequential Semantic Embedding for Mining High-Utility Itemsets and Top-N Recommendations

Chaudhari

2024

JICCE

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“…More attacks and a high number of instances in real-time IoT scenarios needed to be extended from this work. The authors of [13] improved the feature sets and association rule mining techniques, such as the FP growth algorithm, for the improvement of feature sets through the FP growth algorithm. The CNN model was implemented for Botnet attack detection with higher accuracy than existing features.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Deep Learning for Anomaly-Based Intrusion Detection in IoT Smart Home Networks

et al. 2022

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The Internet of Things (IoT) is a tremendous network based on connected smart devices. These networks sense and transmit data by using advanced communication standards and technologies. The smart home is one of the areas of IoT networks, where home appliances are connected to the internet and smart grids. However, these networks are at high risk in terms of security violations. Different kinds of attacks have been conducted on these networks where the user lost their data. Intrusion detection systems (IDSs) are used to detect and prevent cyberattacks. These systems are based on machine and deep learning techniques and still suffer from fitting or overfitting issues. This paper proposes a novel solution for anomaly-based intrusion detection for smart home networks. The proposed model addresses overfitting/underfitting issues and ensures high performance in terms of hybridization. The proposed solution uses feature selection and hyperparameter tuning and was tested with an existing dataset. The experimental results indicated a significant increase in performance while minimizing misclassification and other limitations as compared to state-of-the-art solutions.

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“…Feng W [3] et al proposed an FP-Growth algorithm based on Pearson coefficient, which used data from historical projects and combined calculations to obtain the review expert group with the highest degree of fit, so as to achieve efficient recommendation. Hong Van [4] et al proposed to study the original feature set in the database, analyze the characteristics of the original data, improve the original feature set, and then use FP-Growth for data mining, which finally proved to improve the Changsheng tong [5] proposed an FP-Growth mining analysis method based on historical operation and maintenance data and fault data to improve the effectiveness of association rules. Jing Yang [6] et al Proposed an FP-Growth algorithm with threshold setting to reduce errors, which improved the stability and accuracy of the detection system.…”

Section: Introductionmentioning

confidence: 99%

Research on improved algorithm of FP-growth based on data strong association

Ye,

Ning

2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

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Improving the Feature Set in IoT Intrusion Detection Problem Based on FP-Growth Algorithm

Cited by 4 publications

References 16 publications

Deep Learning Framework with Convolutional Sequential Semantic Embedding for Mining High-Utility Itemsets and Top-N Recommendations

Deep Learning Framework with Convolutional Sequential Semantic Embedding for Mining High-Utility Itemsets and Top-N Recommendations

Intelligent Deep Learning for Anomaly-Based Intrusion Detection in IoT Smart Home Networks

Research on improved algorithm of FP-growth based on data strong association

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