Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction

Talukder, Md. Alamin; Islam, Md. Manowarul; Uddin, Md Ashraf; Hasan, Khondokar Fida; Sharmin, Selina; Alyami, Salem A.; Moni, Mohammad Ali

doi:10.1186/s40537-024-00886-w

Cited by 33 publications

(3 citation statements)

References 67 publications

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“…Fig. 5 shows the ROC curve, which integrates response sensitivity and ongoing specificity variables, potentially revealing the relationship between these two metrics [36,42]. It displays classifier performance in Binary Classification Tasks derived from computing the True Positive Rate (TPR) and False Positive Rate (FPR) across various thresholds.…”

Section: Resultsmentioning

confidence: 99%

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Predicting Tennis Match Outcomes Mid-GameUsing Machine Learning on Psychological and Physical Data

Li,

Deng,

et al. 2024

Preprint

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Predicting game outcomes has significantly garnered the interest of researchersin recent years. The role of player performance is integral in-game analytics, significantlyimpacting the interpretation and results of the analysis. Quantifyingfactors impacting tennis games would advance the analysis of player’s performance.The proposed work intends to use real-time data from each game pointto determine essential feature values, formulate and assess the impact of psychologicalmomentum, and employ machine learning methodology on mid-matchdata for predicting the game’s victor. The data source is from Wimbledon and US Open games from 2017 to 2022, a total of 1592 games, and utilize 363 gamesof 2023 to evaluate their forecasting ability. We first obtained weights throughinformation entropy and defined psychological momentum, and then 3 best classifiers,random forest, CatBoost, and Logistic Regression, were detected to assessthe features. Additionally, we implemented a soft voting ensemble method integratingthe Random Forest and CatBoost classifiers. All four models achieve over90% accuracy and F1-score, with the soft voting classifier performing the best(accuracy: 97.5%, F1 score: 97.4%). These models achieve predictive accuraciesabove 70% using the first 25% data of a game.

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Section: Resultsmentioning

confidence: 99%

“…This ensemble approach increases accuracy and provides a reliable way to handle large datasets efficiently. RF has a fast training speed, requires minimal parameter tuning, and effectively addresses the challenges of overfitting by exploiting variability among trees to produce more stable and accurate predictions [36].…”

Section: Random Forestmentioning

confidence: 99%

Predicting Tennis Match Outcomes Mid-GameUsing Machine Learning on Psychological and Physical Data

Li,

Deng,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Another crucial function is associated with preserving the integrity of each of the data chunks. The data would not be reconstructed even if a single data chunk was tampered with by any of the storage nodes [13,14]. Moreover, the issues related to the processing time of service requests are also crucial.…”

Section: Introductionmentioning

confidence: 99%

Robust-DSN: A Hybrid Distributed Replication and Encoding Network Grouped with a Distributed Swarm Workflow Scheduler

Hameed,

Barzegar,

Ioini

et al. 2024

Electronics

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In many distributed applications such as the Internet of Things (IoT), large amounts of data are being generated that require robust storage solutions. Traditional cloud solutions, although efficient, often lack trust and transparency because of centralized management. To address these issues, we present Robust-DSN, a distributed storage network leveraging the hybrid distributed replication and encoding network (HYDREN) and the distributed swarm workflow scheduler (DSWS) as its main components. Our system uses an interplanetary file system (IPFS) as an underlay storage network and segments it into multiple regions to distribute the failure domain and improve the data’s proximity to users. HYDREN incorporates Reed–Solomon encoding and distributed replication to improve file availability, while DSWS optimizes resource allocation across the network. The uploaded file is encoded into chunks and distributed across distinct optimal nodes leveraging lightweight multithreading. Additionally, Robust-DSN verifies the integrity of all chunks by preserving the hashes when uploading and validating each chunk while downloading. The proposed system provides a comprehensive solution for resilient distributed data storage, focusing on the key challenges of data availability, integrity, and performance. The results reveal that compared with a state-of-the-art system, the proposed system improves file recovery by 15%, even with a 50% peer failure rate. Furthermore, with replication factor 4 and the same failure resilience as IPFS, it saves 50% storage and enhances file recovery by 8%. Robust-DSN acts as a distributed storage platform for emerging technologies, expanding storage system capabilities in a wide range of distributed applications.

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MLSTL-WSN: machine learning-based intrusion detection using SMOTETomek in WSNs

Talukder,

Sharmin,

Uddin

et al. 2024

Int. J. Inf. Secur.

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In the domain of cyber-physical systems, wireless sensor networks (WSNs) play a pivotal role as infrastructures, encompassing both stationary and mobile sensors. These sensors self-organize and establish multi-hop connections for communication, collectively sensing, gathering, processing, and transmitting data about their surroundings. Despite their significance, WSNs face rapid and detrimental attacks that can disrupt functionality. Existing intrusion detection methods for WSNs encounter challenges such as low detection rates, computational overhead, and false alarms. These issues stem from sensor node resource constraints, data redundancy, and high correlation within the network. To address these challenges, we propose an innovative intrusion detection approach that integrates machine learning (ML) techniques with the Synthetic Minority Oversampling Technique Tomek Link (SMOTE-TomekLink) algorithm. This blend synthesizes minority instances and eliminates Tomek links, resulting in a balanced dataset that significantly enhances detection accuracy in WSNs. Additionally, we incorporate feature scaling through standardization to render input features consistent and scalable, facilitating more precise training and detection. To counteract imbalanced WSN datasets, we employ the SMOTE-Tomek resampling technique, mitigating overfitting and underfitting issues. Our comprehensive evaluation, using the wireless sensor network dataset (WSN-DS) containing 374,661 records, identifies the optimal model for intrusion detection in WSNs. The standout outcome of our research is the remarkable performance of our model. In binary classification scenarios, it achieves an accuracy rate of 99.78%, and in multiclass classification scenarios, it attains an exceptional accuracy rate of 99.92%. These findings underscore the efficiency and superiority of our proposal in the context of WSN intrusion detection, showcasing its effectiveness in detecting and mitigating intrusions in WSNs.

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Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction

Cited by 33 publications

References 67 publications

Predicting Tennis Match Outcomes Mid-GameUsing Machine Learning on Psychological and Physical Data

Predicting Tennis Match Outcomes Mid-GameUsing Machine Learning on Psychological and Physical Data

Robust-DSN: A Hybrid Distributed Replication and Encoding Network Grouped with a Distributed Swarm Workflow Scheduler

MLSTL-WSN: machine learning-based intrusion detection using SMOTETomek in WSNs

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