Intrusion detection based on concept drift detection and online incremental learning
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| Udgivet i: | International Journal of Pervasive Computing and Communications vol. 21, no. 1 (2025), p. 81-115 |
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Emerald Group Publishing Limited
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| 024 | 7 | |a 10.1108/IJPCC-12-2023-0358 |2 doi | |
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| 045 | 2 | |b d20250101 |b d20250331 | |
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| 100 | 1 | |a Jemili, Farah |u ISITCom, Mars Research Laboratory, LR17ES05, University of Sousse, Sousse, Tunisia | |
| 245 | 1 | |a Intrusion detection based on concept drift detection and online incremental learning | |
| 260 | |b Emerald Group Publishing Limited |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a PurposeThe primary purpose of this paper is to introduce the drift detection method-online random forest (DDM-ORF) model for intrusion detection, combining DDM for detecting concept drift and ORF for incremental learning. The paper addresses the challenges of dynamic and nonstationary data, offering a solution that continuously adapts to changes in the data distribution. The goal is to provide effective intrusion detection in real-world scenarios, demonstrated through comprehensive experiments and evaluations using Apache Spark.Design/methodology/approachThe paper uses an experimental approach to evaluate the DDM-ORF model. The design involves assessing classification performance metrics, including accuracy, precision, recall and F-measure. The methodology integrates Apache Spark for distributed computing, using metrics such as processed records per second and input rows per second. The evaluation extends to the analysis of IP addresses, ports and taxonomies in the MAWILab data set. This comprehensive design and methodology showcase the model’s effectiveness in detecting intrusions through concept drift detection and online incremental learning on large-scale, heterogeneous data.FindingsThe paper’s findings reveal that the DDM-ORF model achieves outstanding classification results with 99.96% accuracy, demonstrating its efficacy in intrusion detection. Comparative analysis against a convolutional neural network-based model indicates superior performance in anomalous and suspicious detection rates. The exploration of IP addresses, ports and taxonomies uncovers valuable insights into attack patterns. Apache Spark evaluation attests to the system’s high processing rates. The study emphasizes the scalability, availability and fault tolerance of DDM-ORF, making it suitable for real-world scenarios. Overall, the paper establishes the model’s proficiency in handling dynamic, nonstationary data for intrusion detection.Research limitations/implicationsThe research acknowledges certain limitations, including the potential challenge of DDM detecting only frequency changes in class labels and not complex concept drifts. The incremental random forest’s reliance on memory may pose constraints as the forest size increases, potentially leading to overfitting. Addressing these limitations could involve exploring alternative concept drift detection algorithms and implementing ensemble pruning techniques for memory efficiency. Further research avenues may investigate algorithms balancing accuracy and memory usage, such as compressed random forests, to enhance the model’s effectiveness in evolving data environments.Practical implicationsThe study’s practical implications are noteworthy. The proposed DDM-ORF model, designed for intrusion detection through concept drift detection and online incremental learning, offers a scalable, available and fault-tolerant solution. Leveraging Apache Spark and Microsoft Azure Cloud enhances processing capabilities for large data sets in dynamic, nonstationary scenarios. The model’s applicability to heterogeneous data sets and its achievement of high-accuracy multi-class classification make it suitable for real-world intrusion detection. Moreover, the auto-scaling features of Microsoft Azure Cloud contribute to adaptability, ensuring efficient resource utilization without downtime. These practical implications underscore the model’s relevance and effectiveness in diverse operational contexts.Social implicationsThe DDM-ORF model’s social implications are significant, contributing to enhanced cybersecurity measures. By providing an effective intrusion detection system, it helps safeguard digital ecosystems, preserving user privacy and securing sensitive information. The model’s accuracy in identifying and classifying various intrusion attempts aids in mitigating potential cyber threats, thereby fostering a safer online environment for individuals and organizations. As cybersecurity is paramount in the digital age, the social impact lies in fortifying the resilience of networks, systems and data against malicious activities, ultimately promoting trust and reliability in online interactions.Originality/valueThe DDM-ORF model introduces a novel approach to intrusion detection by combining drift detection and online incremental learning. This originality lies in its utilization of the DDM-ORF algorithm, offering a dynamic and adaptive system for evolving data. The model’s contribution extends to its scalability, fault-tolerance and suitability for heterogeneous data sets, addressing challenges in dynamic, nonstationary environments. Its application on a large-scale data set and multi-class classification, along with integration with Apache Spark and Microsoft Azure Cloud, enhances the field’s understanding and application of intrusion detection, providing valuable insights for securing digital infrastructures. | |
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a System reliability | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Classification | ||
| 653 | |a Threats | ||
| 653 | |a Adaptability | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Fault tolerance | ||
| 653 | |a Taxonomy | ||
| 653 | |a Availability | ||
| 653 | |a Batch processing | ||
| 653 | |a Machine learning | ||
| 653 | |a Distributed processing | ||
| 653 | |a Efficiency | ||
| 653 | |a Intrusion detection systems | ||
| 653 | |a Nonstationary environments | ||
| 653 | |a Adaptive algorithms | ||
| 653 | |a Design analysis | ||
| 653 | |a Datasets | ||
| 653 | |a Adaptive systems | ||
| 653 | |a Performance measurement | ||
| 653 | |a Methodology | ||
| 653 | |a Network reliability | ||
| 653 | |a IP (Internet Protocol) | ||
| 653 | |a Neural networks | ||
| 653 | |a Empowerment | ||
| 653 | |a Effectiveness | ||
| 653 | |a Industrial Internet of Things | ||
| 653 | |a Methods | ||
| 653 | |a Algorithms | ||
| 653 | |a Resource utilization | ||
| 653 | |a Drift | ||
| 653 | |a Cybersecurity | ||
| 653 | |a Cloud computing | ||
| 700 | 1 | |a Jouini, Khaled |u ISITCom, Mars Research Laboratory, LR17ES05, University of Sousse, Sousse, Tunisia | |
| 700 | 1 | |a Korbaa, Ouajdi |u ISITCom, Mars Research Laboratory, LR17ES05, University of Sousse, Sousse, Tunisia | |
| 773 | 0 | |t International Journal of Pervasive Computing and Communications |g vol. 21, no. 1 (2025), p. 81-115 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3150563109/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
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| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3150563109/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |