DeepLG SecNet: utilizing deep LSTM and GRU with secure network for enhanced intrusion detection in IoT environments

Nanjappan, Manikandan; Pradeep, K.; Natesan, Gobalakrishnan; Samydurai, A.; Premalatha, G.

doi:10.1007/s10586-023-04223-3

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…With an accuracy score of 99.68% and a loss of 0.0102, the results show that the DL model performed well at predicting attacks using the CICIDS 2017 dataset. The paper [22] proposed a method to combat security challenges and ensure the safety of IoT networks. They combine different DL and optimization techniques to support IoT devices against possible threats and unauthorized access.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning-Based Framework with Enhanced Feature Selection and Resampling for Improved Intrusion Detection

Malik,

Waqas Khan,

Rizwan

et al. 2024

Mathematics

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Intrusion Detection Systems (IDSs) play a crucial role in safeguarding network infrastructures from cyber threats and ensuring the integrity of highly sensitive data. Conventional IDS technologies, although successful in achieving high levels of accuracy, frequently encounter substantial model bias. This bias is primarily caused by imbalances in the data and the lack of relevance of certain features. This study aims to tackle these challenges by proposing an advanced machine learning (ML) based IDS that minimizes misclassification errors and corrects model bias. As a result, the predictive accuracy and generalizability of the IDS are significantly improved. The proposed system employs advanced feature selection techniques, such as Recursive Feature Elimination (RFE), sequential feature selection (SFS), and statistical feature selection, to refine the input feature set and minimize the impact of non-predictive attributes. In addition, this work incorporates data resampling methods such as Synthetic Minority Oversampling Technique and Edited Nearest Neighbor (SMOTE_ENN), Adaptive Synthetic Sampling (ADASYN), and Synthetic Minority Oversampling Technique–Tomek Links (SMOTE_Tomek) to address class imbalance and improve the accuracy of the model. The experimental results indicate that our proposed model, especially when utilizing the random forest (RF) algorithm, surpasses existing models regarding accuracy, precision, recall, and F Score across different data resampling methods. Using the ADASYN resampling method, the RF model achieves an accuracy of 99.9985% for botnet attacks and 99.9777% for Man-in-the-Middle (MITM) attacks, demonstrating the effectiveness of our approach in dealing with imbalanced data distributions. This research not only improves the abilities of IDS to identify botnet and MITM attacks but also provides a scalable and efficient solution that can be used in other areas where data imbalance is a recurring problem. This work has implications beyond IDS, offering valuable insights into using ML techniques in complex real-world scenarios.

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Section: Related Workmentioning

confidence: 99%

A Machine Learning-Based Framework with Enhanced Feature Selection and Resampling for Improved Intrusion Detection

Malik,

Waqas Khan,

Rizwan

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…Undoubtedly, the IoT is considered a significant prospect that contributes significantly to advanced digital technology. However, currently, the risk of unauthorized access due to the connection of a large number of devices over the network is one of the critical challenges [13,14], along with IoT analytics and the IoT process hierarchy.…”

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confidence: 99%